ORCID Profile
0000-0003-2789-9530
Current Organisations
IFREMER
,
Murdoch University
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Publications
Applying landscape metrics to species distribution model predictions to characterize internal range structure and associated changes.
Publisher: Wiley
Date: 17-11-2022
DOI: 10.1111/GCB.16496
Abstract: Distributional shifts in species ranges provide critical evidence of ecological responses to climate change. Assessments of climate-driven changes typically focus on broad-scale range shifts (e.g. poleward or upward), with ecological consequences at regional and local scales commonly overlooked. While these changes are informative for species presenting continuous geographic ranges, many species have discontinuous distributions-both natural (e.g. mountain or coastal species) or human-induced (e.g. species inhabiting fragmented landscapes)-where within-range changes can be significant. Here, we use an ecosystem engineer species (Sabellaria alveolata) with a naturally fragmented distribution as a case study to assess climate-driven changes in within-range occupancy across its entire global distribution. To this end, we applied landscape ecology metrics to outputs from species distribution modelling (SDM) in a novel unified framework. SDM predicted a 27.5% overall increase in the area of potentially suitable habitat under RCP 4.5 by 2050, which taken in isolation would have led to the classification of the species as a climate change winner. SDM further revealed that the latitudinal range is predicted to shrink because of decreased habitat suitability in the equatorward part of the range, not compensated by a poleward expansion. The use of landscape ecology metrics provided additional insights by identifying regions that are predicted to become increasingly fragmented in the future, potentially increasing extirpation risk by jeopardising metapopulation dynamics. This increased range fragmentation could have dramatic consequences for ecosystem structure and functioning. Importantly, the proposed framework-which brings together SDM and landscape metrics-can be widely used to study currently overlooked climate-driven changes in species internal range structure, without requiring detailed empirical knowledge of the modelled species. This approach represents an important advancement beyond predictive envelope approaches and could reveal itself as paramount for managers whose spatial scale of action usually ranges from local to regional.
Perception-aware online trajectory generation for a prescribed manoeuvre of unmanned surface vehicle in cluttered unstructured environment
Publisher: Elsevier BV
Date: 11-2023
Terminal sliding mode control for steer-by-wire system in electric vehicles
Publisher: IEEE
Date: 07-2012
Correction to: Robust control of reaction wheel bicycle robot via adaptive integral terminal sliding mode
Publisher: Springer Science and Business Media LLC
Date: 05-05-2021
Intermittent Fault Diagnosis and Prognosis for Steer-by-Wire System Using Composite Degradation Model
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Design of Fractional Order Odd-Harmonics Repetitive Controller for Discrete-Time Linear Systems with Experimental Validations
Publisher: MDPI AG
Date: 16-11-2022
DOI: 10.3390/S22228873
Abstract: This paper presents a simple and straightforward design of a discrete-time fractional-order odd-harmonics repetitive controller (RC). Unlike general RC designs, the proposed method utilizes an internal model with a half-period delay and a stabilizing controller with a fractional phase lead compensator. First, the odd-harmonics internal model representing odd-harmonics frequencies is constructed by using the information of the reference’s basis period and the preferred tracking bandwidth. Secondly, an optimization problem synthesized from the stability condition of the RC closed-loop system is solved to obtain the fractional phase lead compensator. Finally, the fractional term of the stabilizing controller is realized by using a causal and stable infinite impulse response (IIR) filter, where the filter coefficients are computed by applying the Thiran formula. Simulation and experimental validation on a servomotor system are conducted to verify the effectiveness of the proposed design.
Position control of spherical inverted pendulum via improved discrete-time neural network approach
Publisher: Springer Science and Business Media LLC
Date: 08-01-2020
Exploring pre-analytical factors for the optimisation of serum diagnostics: Progressing the clinical utility of ATR-FTIR spectroscopy
Publisher: Elsevier BV
Date: 07-2020
A novel hierarchical sliding mode control strategy for a two-wheeled self-balancing vehicle
Publisher: IEEE
Date: 07-2017
Robust chattering-free sliding mode control of electronic throttle systems in drive-by-wire vehicles
Publisher: IEEE
Date: 07-2017
An efficient adjoint computational method based on lifted irk integrator and exact penalty function for optimal control problems involving continuous inequality constraints
Publisher: American Institute of Mathematical Sciences (AIMS)
Date: 2018
Analog Control Circuit Designs for a Class of Continuous-Time Adaptive Fault-Tolerant Control Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
A Thermal Estimation Method for IGBT Module Adaptable to Operating Conditions
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Fault Diagnosis for Electromechanical System via Extended Analytical Redundancy Relations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2018
Robust Sliding Mode-Based Learning Control for Steer-by-Wire Systems in Modern Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
Parameter Estimation for Robotic Manipulator Systems
Publisher: MDPI AG
Date: 19-05-2022
Abstract: In this paper, a novel methodology for estimating the parameters of robotic manipulator systems is proposed. It can be seen that, for the purpose of parameter estimation, the input torque to each joint motor is designed as a linear combination of sinusoids. After the transient responses of joint angles exponentially converge to zero, the steady states of joint angle outputs can be extracted. Since the steady states of joint angles are the equivalent finite Fourier series, the coefficients of the steady state components of joint angles can be further extracted in a fundamental period. With the amazing finding that the steady states contain all dynamic information of manipulator systems, all unknown parameters of the system model can be accurately estimated with the extracted coefficients in finite frequency bands. The simulation results for a two-link manipulator are carried out to illustrate the effectiveness and robustness against measurement noise of the proposed method.
Disturbance-Observer-Based Second-Order Sliding Mode Controller for Speed Control of PMSM Drives
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Sliding Mode Control for Steer-by-Wire Systems With AC Motors in Road Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Straw/Spring Teeth Interaction Analysis of Baler Picker in Smart Agriculture via an ADAMS-DEM Coupled Simulation Method
Publisher: MDPI AG
Date: 19-11-2021
Abstract: In this paper, a new coupling simulation method is proposed for baler picker using automatic dynamic analysis of mechanical systems (ADAMS) and discrete element method (DEM). Field tests are carried out to verify the accuracy of the simulation model. By using the coupling method, not only was it obtained that the forward velocity (FV) and the ground clearance of spring teeth (GCST) are positively correlated with the pick-up loss rate (PLR), but also that the blockage of the picker mainly occurs in the straw pushing area, and an optimization plan is proposed. Through the analysis of the acting force (AF) between the roller and the track groove, we speculate that the structure of the track groove in a certain area is defective. The coupling method and optimization scheme proposed in this paper can provide a reference for the optimal design of the picker.
A Simultaneous Diagnosis Method for Power Switch and Current Sensor Faults in Grid-Connected Three-Level NPC Inverters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
The application of granular computing in electric vehicle fault diagnosis
Publisher: Informa UK Limited
Date: 2014
Fast nonsingular terminal sliding mode control for permanent-magnet linear motor via ELM
Publisher: Springer Science and Business Media LLC
Date: 24-09-2019
Subtidal Microphytobenthos: A Secret Garden Stimulated by the Engineer Species Crepidula fornicata
Publisher: Frontiers Media SA
Date: 18-12-2018
Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Reinforcement learning‐based adaptive optimal tracking algorithm for Markov jump systems with partial unknown dynamics
Publisher: Wiley
Date: 24-05-2022
DOI: 10.1002/OCA.2903
Abstract: In this article, a novel method is proposed to solve the adaptive optimal tracking algorithm for a class of Markov jump systems. First, the augmented system with the tracking signal is built under the decoupling Markov jump systems and it is proved that the selected performance index satisfies the algebraic Riccati equation which can be solved by policy iteration schemes. Then, a reinforcement learning (RL) algorithm is used to solve the coupled algebraic Riccati equations by using partial knowledge of system dynamics. The convergence of the partial model‐free integral RL iteration algorithm is also proved. Finally, a simulation ex le is given to show the better tracking effectiveness and accuracy of the online iteration algorithm comparing with the offline one.
Observer-based Sensor Fault Tolerant Control with Prescribed Tracking Performance for a Class of Nonlinear Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Computational Intelligence-Based Prognosis for Hybrid Mechatronic System Using Improved Wiener Process
Publisher: MDPI AG
Date: 30-08-2021
DOI: 10.3390/ACT10090213
Abstract: In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations.
Obstacle Detection by Autonomous Vehicles: An Adaptive Neighborhood Search Radius Clustering Approach
Publisher: MDPI AG
Date: 02-01-2023
Abstract: For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation of point clouds, resulting in false detection or missed detection of obstacles. In order to solve these problems, a new obstacle detection method was required. Firstly, we applied a distance-based filter and a ground segmentation algorithm, to pre-process the original 3D point cloud. Secondly, we proposed an adaptive neighborhood search radius clustering algorithm, based on the analysis of the relationship between the clustering radius and point cloud spatial distribution, adopting the point cloud pitch angle and the horizontal angle resolution of the lidar, to determine the clustering threshold. Finally, an autonomous vehicle platform and the offline autonomous driving KITTI dataset were used to conduct multi-scene comparative experiments between the proposed method and a Euclidean clustering method. The multi-scene real vehicle experimental results showed that our method improved clustering accuracy by 6.94%, and the KITTI dataset experimental results showed that the F1 score increased by 0.0629.
Robust Control of a Vehicle Steer-by-Wire System Using Adaptive Sliding Mode
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Extreme-learning-machine-based FNTSM control strategy for electronic throttle
Publisher: Springer Science and Business Media LLC
Date: 29-08-2019
Robust hierarchical sliding mode control for steer-by-wire equipped vehicle yaw stability control
Publisher: IEEE
Date: 12-2018
Discrete Component Prognosis for Hybrid Systems Under Intermittent Faults
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Krill Herd Optimization based Fault Diagnosis for Hybrid Mechatronic System
Publisher: IEEE
Date: 07-2019
Switched Energy and Neural Network-Based Approach for Swing-Up and Tracking Control of Double Inverted Pendulum
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Actuators and Sensors for Application in Agricultural Robots: A Review
Publisher: MDPI AG
Date: 09-10-2022
Abstract: In recent years, with the rapid development of science and technology, agricultural robots have gradually begun to replace humans, to complete various agricultural operations, changing traditional agricultural production methods. Not only is the labor input reduced, but also the production efficiency can be improved, which invariably contributes to the development of smart agriculture. This paper reviews the core technologies used for agricultural robots in non-structural environments. In addition, we review the technological progress of drive systems, control strategies, end-effectors, robotic arms, environmental perception, and other related systems. This research shows that in a non-structured agricultural environment, using cameras and light detection and ranging (LiDAR), as well as ultrasonic and satellite navigation equipment, and by integrating sensing, transmission, control, and operation, different types of actuators can be innovatively designed and developed to drive the advance of agricultural robots, to meet the delicate and complex requirements of agricultural products as operational objects, such that better productivity and standardization of agriculture can be achieved. In summary, agricultural production is developing toward a data-driven, standardized, and unmanned approach, with smart agriculture supported by actuator-driven-based agricultural robots. This paper concludes with a summary of the main existing technologies and challenges in the development of actuators for applications in agricultural robots, and the outlook regarding the primary development directions of agricultural robots in the near future.
Energy management strategy for electric vehicles based on deep Q-learning using Bayesian optimization
Publisher: Springer Science and Business Media LLC
Date: 24-10-2019
Robust sliding mode control for Steer-by-Wire systems with AC motors in road vehicles
Publisher: IEEE
Date: 06-2013
Driver Fatigue Detection via Differential Evolution Extreme Learning Machine Technique
Publisher: MDPI AG
Date: 05-11-2020
DOI: 10.3390/ELECTRONICS9111850
Abstract: Fatigue driving (FD) is one of the main causes of traffic accidents. Traditionally, machine learning technologies such as back propagation neural network (BPNN) and support vector machine (SVM) are popularly used for fatigue driving detection. However, the BPNN exhibits slow convergence speed and many adjustable parameters, while it is difficult to train large-scale s les in the SVM. In this paper, we develop extreme learning machine (ELM)-based FD detection method to avoid the above disadvantages. Further, since the randomness of the weight and biases between the input layer and the hidden layer of the ELM will influence its generalization performance, we further apply a differential evolution ELM (DE-ELM) method to the analysis of the driver’s respiration and heartbeat signals, which can effectively judge the driver fatigue state. Moreover, not only will the Doppler radar and smart bracelet be used to obtain the driver respiration and heartbeat signals, but also the s le database required for the experiment will be established through extensive signal collections. Experimental results show that the DE-ELM has a better performance on driver’s fatigue level detection than the traditional ELM and SVM.
Uninterrupted path planning system for Multi-USV sampling mission in a cluttered ocean environment
Publisher: Elsevier BV
Date: 06-2022
Robust adaptive position control of automotive electronic throttle valve using PID-type sliding mode technique
Publisher: IEEE
Date: 07-2016
A Novel Torque Distribution Strategy Based on Deep Recurrent Neural Network for Parallel Hybrid Electric Vehicle
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Asynchronous Fault Detection Observer for 2-D Markov Jump Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Engine clutch engagement control for a parallel hybrid electric vehicle using sliding mode control scheme
Publisher: Informa UK Limited
Date: 10-2016
Self-Learning Takagi-Sugeno Fuzzy Control with Application to Semi-Car Active Suspension Model
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Extreme-learning-machine-based robust AITSM control for steer-by-wire systems
Publisher: IEEE
Date: 07-2019
Internal Model Control of PMSM Position Servo System: Theory and Experimental Results
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Global position tracking control of PMSM servo system via internal model approach and experimental validations
Publisher: Wiley
Date: 05-08-2022
DOI: 10.1002/RNC.6317
Abstract: In practical engineering, load torque disturbance and parameter uncertainties are two important factors, which may deteriorate the tracking accuracy of permanent magnet synchronous motor (PMSM) servo system. Therefore, its global position tracking control problem is a challenging task when the load torque disturbance is time‐varying and the motor parameters are unknown. In this article, an internal model controller based on global robust output regulation (GROR) theory is proposed to achieve this control objective. In particular, we first formulate the global position tracking control problem as a GROR problem. Then, the GROR problem is converted into a global robust stabilization problem of an augmented system by constructing an appropriate internal model. Finally, we can stabilize the augmented system by a global stabilization controller instead of local stabilization controller used in the recent work, which guarantees global position tracking and disturbance rejection of PMSM servo system. The excellent position tracking performance of our design is demonstrated by both simulation and experimental results.
Extreme-learning-machine-based robust integral terminal sliding mode control of bicycle robot
Publisher: Elsevier BV
Date: 04-2022
Prognosis of Electric Scooter With Intermittent Faults: Dual Degradation Processes Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Leakage-type adaptive state and disturbance observers for uncertain nonlinear systems
Publisher: Springer Science and Business Media LLC
Date: 24-07-2021
Robust tracking control for vehicle electronic throttle using adaptive dynamic sliding mode and extended state observer
Publisher: Elsevier BV
Date: 2020
Adaptive Control of Uncertain Nonlinear Systems via Event-Triggered Communication and NN Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
There's no harm in having too much: A comprehensive toolbox of methods in trophic ecology
Publisher: Elsevier BV
Date: 12-2018
Adaptive fault‐tolerant control of mobile robots with actuator faults and unknown parameters
Publisher: Institution of Engineering and Technology (IET)
Date: 13-05-2019
A Novel Data-Assisted Model and Discrete-Time Sliding Mode Steering Controller of Robotic Fish
Publisher: IEEE
Date: 05-2018
Robust adaptive position control of automotive electronic throttle valve using PID-type sliding mode technique
Publisher: Springer Science and Business Media LLC
Date: 06-04-2016
Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers
Publisher: MDPI AG
Date: 11-10-2022
DOI: 10.20944/PREPRINTS202110.0155.V1
Abstract: This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential-flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/ approximate the fault and uncertainties associated functions. The fault detection mechanism is developed based on output residual generation and monitoring so that any unfavourable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making of faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.
Co-Design of Adaptive Event Generator and Asynchronous Fault Detection Filter for Markov Jump Systems via Genetic Algorithm
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Robotic Fish Path Planning in Complex Environment
Publisher: IEEE
Date: 07-2019
Adaptive fast non-singular terminal sliding mode control for a vehicle steer-by-wire system
Publisher: Institution of Engineering and Technology (IET)
Date: 05-2017
Compound Fault Diagnosis and Sequential Prognosis for Electric Scooter with Uncertainties
Publisher: MDPI AG
Date: 03-12-2020
DOI: 10.3390/ACT9040128
Abstract: This paper addresses diagnosis and prognosis problems for an electric scooter subjected to parameter uncertainties and compound faults (i.e., permanent fault and intermittent fault with non-monotonic degradation). First, the diagnostic bond graph in linear fractional transformation form is used to model the uncertain electric scooter and derive the analytical redundancy relations incorporating the nominal part and uncertain part, based on which the adaptive thresholds for robust fault detection and the fault signature matrix for fault isolation can be obtained. Second, an adaptive enhanced unscented Kalman filter is proposed to identify the fault magnitudes and distinguish the fault types where an auxiliary detector is introduced to capture the appearing and disappearing moments of intermittent fault. Third, a dynamic model with usage dependent degradation coefficient is developed to describe the degradation process of intermittent fault under various usage conditions. Due to the variation of degradation coefficient and the presence of non-monotonic degradation characteristic under some usage conditions, a sequential prognosis method is proposed where the reactivation of the prognoser is governed by the reactivation events. Finally, the proposed methods are validated by experiment results.
Off-policy reinforcement learning for tracking control of discrete-time Markov jump linear systems with completely unknown dynamics
Publisher: Elsevier BV
Date: 02-2023
Practical Model-Free Robust Control Design for an Underwater IPMC Actuator
Publisher: IEEE
Date: 07-2019
Continuous Adaptive Fast Terminal Sliding Mode-based Speed Regulation Control of PMSM Drive via Improved Super-Twisting Observer
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Adaptive Cuckoo Search-Extreme Learning Machine Based Prognosis for Electric Scooter System Under Intermittent Fault
Publisher: MDPI AG
Date: 22-10-2021
DOI: 10.3390/ACT10110283
Abstract: In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.
Dissipativity-based finite-time asynchronous output feedback control for wind turbine system via a hidden Markov model
Publisher: Informa UK Limited
Date: 24-05-2022
Robust terminal sliding mode control for automotive electronic throttle with lumped uncertainty estimation
Publisher: Inderscience Publishers
Date: 2017
Robust sliding mode learning control for uncertain discrete-time multi-input multi-output systems
Publisher: Institution of Engineering and Technology (IET)
Date: 08-2014
A composite control scheme for automotive Steer-By-Wire system
Publisher: IEEE
Date: 12-2015
Robust hierarchical sliding mode control of a two-wheeled self-balancing vehicle using perturbation estimation
Publisher: Elsevier BV
Date: 05-2020
Adaptive Sliding Mode-Based Lateral Stability Control of Steer-by-Wire Vehicles With Experimental Validations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Robust Motion Control of a Linear Motor Positioner Using Fast Nonsingular Terminal Sliding Mode
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Discrete‐time modified repetitive sliding mode control for uncertain linear systems
Publisher: Wiley
Date: 14-08-2021
DOI: 10.1002/ACS.3316
Abstract: This article presents a design strategy and stability analysis of modified repetitive sliding mode controller for uncertain linear systems. A modified repetitive controller is adopted to simultaneously track and reject periodic signals. A discrete‐time sliding mode controller is combined to compensate the slow response of repetitive control and to provide robustness against plant parameters uncertainties. Stability analysis is provided to prove boundedness of the proposed control law and the convergence of sliding function and the tracking error. Comparative simulation results demonstrate that the proposed method is able to accurately track reference signal and to reject disturbance with fast transient response. The results also indicate that the closed‐loop system remains stable in the presence of plant parameter changes.
Robust control for vehicle lane-keeping with sliding mode
Publisher: IEEE
Date: 12-2018
Adaptive extreme learning machine‐based event‐triggered control for perturbed Euler–Lagrange systems
Publisher: Wiley
Date: 26-09-2022
DOI: 10.1002/RNC.6377
Abstract: The problem of event‐triggered finite‐time trajectory tracking control of perturbed Euler–Lagrange systems with nonlinear dynamics and disturbances is addressed in this article. Extreme learning machine (ELM) framework is employed to formulate unknown nonlinearities, and adaptive technique is adopted to adjust output weights of the ELM networks and remedy the negative impacts of disturbances, nonlinearities, and residual errors. Then to ensure the system follows the desired position trajectory within a finite‐time, an adaptive ELM‐based sliding mode control strategy is developed. Moreover, event‐triggered control technique is proposed to regulate control outputs on the basis of the developed control strategy for reducing actuator actions and saving communication resources. Lyapunov stability theorem is utilized to confirm bounded trajectory tracking results and finite‐time convergence of the Euler–Lagrange system. Finally, the effectiveness of the developed adaptive ELM‐based event‐triggered sliding‐mode control strategies is substantiated by simulations in a robotic manipulator system.
A Novel Integral Terminal Sliding Mode Control of Yaw Stability for Steer-by-Wire Vehicles
Publisher: IEEE
Date: 07-2018
Asynchronous Fault Detection for Interval Type-2 Fuzzy Nonhomogeneous Higher Level Markov Jump Systems With Uncertain Transition Probabilities
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Learning-Based Distributed Resilient Fault-Tolerant Control Method for Heterogeneous MASs Under Unknown Leader Dynamic
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Fuzzy-Based Adaptive Optimization of Unknown Discrete-Time Nonlinear Markov Jump Systems With Off-Policy Reinforcement Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Non‐contact and anti‐interference diagnosis of SiC MOSFET module bond wire faults for EV wireless charging device
Publisher: Institution of Engineering and Technology (IET)
Date: 09-08-2023
DOI: 10.1049/PEL2.12569
Abstract: SiC MOSFET modules are widely used for bidirectional wireless charging of electric vehicles. However, prolonged use can result in bond wire faults, leading to reliability issues. To address this problem, a non‐contact bond wire monitoring method that can be carried out using the charging coil of the wireless charging device is proposed. Here, two monitoring loops are constructed based on the circuit topology, and their impedance magnitudes are measured on the charging coil to avoid the influence of battery voltage on monitoring results. To detect the bond wire condition, the impedance magnitude difference of the two monitoring loops is chosen as an indicator, which is very sensitive to the bond wire lift‐off. However, the monitoring results may be influenced by the aging of resonant capacitors and the offset of the charging coil. In particular, the influence of the coil offset is significant. To mitigate this effect, a prognostic parameter calibration method is proposed. Experimental results confirm the effectiveness of the proposed method, which provides a promising solution for the reliable and safe operation of SiC MOSFET modules in wireless charging devices.
Neural-network-based robust control for steer-by-wire systems with uncertain dynamics
Publisher: Springer Science and Business Media LLC
Date: 23-01-2015
A Partition Decoupling Algorithm for Compact Thermal Model in Multichip IGBT Modules
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Intelligent fault diagnosis of induction motor with stator winding fault
Publisher: IEEE
Date: 08-2016
A Novel Yawing Suppression Model and Control of Robotic Fish via Stable Point
Publisher: IEEE
Date: 07-2018
Control of an IPMC Soft Actuator Using Adaptive Full-Order Recursive Terminal Sliding Mode
Publisher: MDPI AG
Date: 16-02-2021
DOI: 10.3390/ACT10020033
Abstract: The ionic polymer metal composite (IPMC) actuator is a kind of soft actuator that can work for underwater applications. However, IPMC actuator control suffers from high nonlinearity due to the existence of inherent creep and hysteresis phenomena. Furthermore, for underwater applications, they are highly exposed to parametric uncertainties and external disturbances due to the inherent characteristics and working environment. Those factors significantly affect the positioning accuracy and reliability of IPMC actuators. Hence, feedback control techniques are vital in the control of IPMC actuators for suppressing the system uncertainty and external disturbance. In this paper, for the first time an adaptive full-order recursive terminal sliding-mode (AFORTSM) controller is proposed for the IPMC actuator to enhance the positioning accuracy and robustness against parametric uncertainties and external disturbances. The proposed controller incorporates an adaptive algorithm with terminal sliding mode method to release the need for any prerequisite bound of the disturbance. In addition, stability analysis proves that it can guarantee the tracking error to converge to zero in finite time in the presence of uncertainty and disturbance. Experiments are carried out on the IPMC actuator to verify the practical effectiveness of the AFORTSM controller in comparison with a conventional nonsingular terminal sliding mode (NTSM) controller in terms of smaller tracking error and faster disturbance rejection.
Musical Chairs on Temperate Reefs: Species Turnover and Replacement Within Functional Groups Explain Regional Diversity Variation in Assemblages Associated With Honeycomb Worms
Publisher: Frontiers Media SA
Date: 11-05-2021
DOI: 10.3389/FMARS.2021.654141
Abstract: Reef-building species are recognized as having an important ecological role and as generally enhancing the ersity of benthic organisms in marine habitats. However, although these ecosystem engineers have a facilitating role for some species, they may exclude or compete with others. The honeycomb worm Sabellaria alveolata ( Linnaeus, 1767 ) is an important foundation species, commonly found from northwest Ireland to northern Mauritania, whose reef structures increase the physical complexity of the marine benthos, supporting high levels of bio ersity. Local patterns and regional differences in taxonomic and functional ersity were examined in honeycomb worm reefs from 10 sites along the northeastern Atlantic to explore variation in ersity across biogeographic regions and the potential effects of environmental drivers. While taxonomic composition varied across the study sites, levels of ersity remained relatively constant along the European coast. Assemblages showed high levels of species turnover compared to differences in richness, which varied primarily in response to sea surface temperatures and sediment content, the latter suggesting that local characteristics of the reef had a greater effect on community composition than the density of the engineering species. In contrast, the functional composition of assemblages was similar regardless of taxonomic composition or biogeography, with five functional groups being observed in all sites and only small differences in abundance in these groups being detected. Functional groups represented primarily filter-feeders and deposit-feeders, with the notable absence of herbivores, indicating that the reefs may act as biological filters for some species from the local pool of organisms. Redundancy was observed within functional groups that may indicate that honeycomb worm reefs can offer similar niche properties to its associated assemblages across varying environmental conditions. These results highlight the advantages of comparing taxonomic and functional metrics, which allow identification of a number of ecological processes that structure marine communities.
Fourier‐transform infrared spectroscopy of biofluids: A practical approach
Publisher: Wiley
Date: 24-01-2021
Abstract: Biofluid spectroscopy is an emerging technology in the field of clinical investigation, providing a simple way to extract diagnostic and observational information from easy to acquire s les. Infrared spectroscopy is well suited to analyse a large range of biofluid s les, including blood and its derivatives, due to flexible s ling modes and high sensitivity to subtle biological changes. As the technology advances towards the clinic, factors influencing successful clinical translation are becoming apparent. Here, we provide a tutorial for effective biofluid spectroscopy study design, discussing s le and instrument parameters, as well as clinical considerations. The aim is to present the current understanding of clinical translation in the field of biofluid spectroscopy, and to facilitate other clinical applications to advance to the clinic. image
Scheduled health monitoring of hybrid systems with multiple distinct faults
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Robust Prescribed Trajectory Tracking Control of a Robot Manipulator Using Adaptive Finite-Time Sliding Mode and Extreme Learning Machine Method
Publisher: MDPI AG
Date: 15-10-2022
Abstract: This study aims to provide a robust trajectory tracking controller which guarantees the prescribed performance of a robot manipulator, both in transient and steady-state modes, experiencing parametric uncertainties. The main core of the controller is designed based on the adaptive finite-time sliding mode control (SMC) and extreme learning machine (ELM) methods to collectively estimate the parametric model uncertainties and enhance the quality of tracking performance. Accordingly, the global estimation with a fast convergence rate is achieved while the tracking error and the impact of chattering on the control input are mitigated significantly. Following the control design, the stability of the overall control system along with the finite-time convergence rate is proved, and the effectiveness of the proposed method is investigated via extensive simulation studies. The results of simulations confirm that the prescribed transient and steady-state performances are obtained with enough accuracy, fast convergence rate, robustness, and smooth control input which are all required for practical implementation and applications.
Tracking Control of a Linear Motor Positioner Based on Barrier Function Adaptive Sliding Mode
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Event-Based Sequential Prognosis for Uncertain Hybrid Systems With Intermittent Faults
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Real-Time Average Junction Temperature Estimation for Multichip IGBT Modules With Low Computational Cost
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Steering feel design for steer-by-wire system on electric vehicles
Publisher: IEEE
Date: 07-2019
A Generalized Fault Tolerant Control Based on Back EMF Feedforward Compensation: Derivation and Application on Induction Motors Drives
Publisher: MDPI AG
Date: 21-12-2022
DOI: 10.3390/EN16010051
Abstract: In this paper, a fault-tolerant three-phase induction drive based on field-oriented control is studied, and an analytical approach is proposed to elucidate the limitations of FOC in flux-torque regulation from the controller perspective. With an open-phase fault, the disturbance terms appear in the controller reference frame and degrade the controller performance when operating in a d-q plane with DC quantities. In addition, the hardware reconfiguration, which is essential to operate faulted three-phase drives, causes substantial change in the way the control parameters vd, vq are reflected onto the machine terminals. An accurate understanding of the feedforward term, by considering the open-phase fault and the hardware modifications, is provided to re-enable the FOC in presence of an open-phase fault. Furthermore, the concept of feedforward term derivation is generically extended to cover multiphase induction drives encountering an open-phase fault whereby no hardware reconfiguration is intended. The proposed method is explained based on a symmetrical six-phase induction and can be extended to drives with a higher number of phases. The effectiveness of the proposed derivation method, which is required to form a feedforward fault-tolerant controller, is verified and compared through the simulation and experiment, ensuring smooth operation in postfault mode.
A Robust Adaptive Chattering-Free Sliding Mode Control Strategy for Automotive Electronic Throttle System via Genetic Algorithm
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Adaptive Tracking Control of an Electronic Throttle Valve Based on Recursive Terminal Sliding Mode
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Robust Control for Steer-by-Wire Systems With Partially Known Dynamics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Global robust output regulation of a class of MIMO nonlinear systems by nonlinear internal model control
Publisher: Wiley
Date: 24-02-2021
DOI: 10.1002/RNC.5456
Intelligent multiple-fault diagnosis of a mobile robot system in the presence of hide effect
Publisher: IEEE
Date: 08-2016
Discrete-time integral terminal sliding mode-based speed tracking control for a robotic fish
Publisher: Springer Science and Business Media LLC
Date: 12-06-2021
Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma
Publisher: Royal Society of Chemistry (RSC)
Date: 2019
DOI: 10.1039/C9AN01731C
Abstract: There are currently no methods in place for the early detection of brain cancer. A reliable serum triage test could avoid the need for surgery, and speed up time to definitive treatment. Could high-throughput infrared spectroscopy fill the void?
Continuous Fast Nonsingular Terminal Sliding Mode Control of Automotive Electronic Throttle Systems Using Finite-Time Exact Observer
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Gastric Malignancy Survival Outcomes in Zambia, Southern Africa
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 10-2012
An Online Reinforcement Learning Approach for Dynamic Pricing of Electric Vehicle Charging Stations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
A sequential computational approach to optimal control problems for differential-algebraic systems based on efficient implicit Runge–Kutta integration
Publisher: Elsevier BV
Date: 06-2018
Specific niche requirements underpin multidecadal range edge stability, but may introduce barriers for climate change adaptation
Publisher: Wiley
Date: 02-02-2021
DOI: 10.1111/DDI.13224
Abstract: To investigate some of the environmental variables underpinning the past and present distribution of an ecosystem engineer near its poleward range edge. locations spanning ,400 km around Ireland. We collated past and present distribution records on a known climate change indicator, the reef‐forming worm Sabellaria alveolata (Linnaeus, 1767) in a biogeographic boundary region over 182 years (1836–2018). This included repeat s ling of 60 locations in the cooler 1950s and again in the warmer 2000s and 2010s. Using species distribution modelling, we identified some of the environmental drivers that likely underpin S. alveolata distribution towards the leading edge of its biogeographical range in Ireland. Through plotting 981 records of presence and absence, we revealed a discontinuous distribution with discretely bounded sub‐populations, and edges that coincide with the locations of tidal fronts. Repeat surveys of 60 locations across three time periods showed evidence of population increases, declines, local extirpation and recolonization events within the range, but no evidence of extensions beyond the previously identified distribution limits, despite decades of warming. At a regional scale, populations were relatively stable through time, but local populations in the cold Irish Sea appear highly dynamic and vulnerable to local extirpation risk. Contemporary distribution data (2013–2018) computed with modelled environmental data identified specific niche requirements which can explain the many distribution gaps, namely wave height, tidal litude, stratification index, then substrate type. In the face of climate warming, such specific niche requirements can create environmental barriers that may prevent species from extending beyond their leading edges. These boundaries may limit a species’ capacity to redistribute in response to global environmental change.
Finite-Time Control of a Linear Motor Positioner Using Adaptive Recursive Terminal Sliding Mode
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Sliding mode learning based congestion control for DiffServ networks
Publisher: Institution of Engineering and Technology (IET)
Date: 07-2016
Nonlinear speed tracking control of PMSM servo system: A global robust output regulation approach
Publisher: Elsevier BV
Date: 07-2021
Adaptive Integral Terminal Sliding Mode Control for Automobile Electronic Throttle via an Uncertainty Observer and Experimental Validation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Sliding mode learning control of non‐minimum phase nonlinear systems
Publisher: Wiley
Date: 18-08-2015
DOI: 10.1002/RNC.3406
Biofluid diagnostics by FTIR spectroscopy: A platform technology for cancer detection
Publisher: Elsevier BV
Date: 05-2020
RBF-Neural-Network-Based Sliding Mode Controller of Automotive Steer-by-Wire Systems
Publisher: IEEE
Date: 08-2015
Robust control of reaction wheel bicycle robot via adaptive integral terminal sliding mode
Publisher: Springer Science and Business Media LLC
Date: 02-04-2021
Sliding mode adaptive control for DC motors using function approximation form
Publisher: Inderscience Publishers
Date: 2016
Connectivity modelling informs metapopulation structure and conservation priorities for a reef‐building species
Publisher: Wiley
Date: 06-08-2022
DOI: 10.1111/DDI.13596
Abstract: In coastal marine systems, biogenic reef‐building species have great importance for conservation as they provide habitat for a wide range of species, promoting bio ersity, ecosystem functioning and services. Biogenic reef persistence and recovery from perturbations depend on recolonization by new recruits. Characterizing larval dispersal among distant reefs is key to understanding how connectivity shapes metapopulation structure and determines network coherence all of which are of critical importance for effective conservation. Northeast Atlantic coast and western English Channel, France. We used a biophysical transport model to simulate larval dispersal of the reef‐building polychaete Sabellaria alveolata . We combined dispersal modelling and network analysis into a framework aiming to identify key reef areas and critical dispersal pathways, whose presence in the network is vital to its overall coherence. We evaluated changes in dispersal pathways constrained by different connectivity thresholds, i.e., minimum dispersal rate for the presence of a connection. We tested scenarios of sequential loss of reefs: randomly, by habitat quality (a score for reef status and occupancy in an area) or by betweenness centrality metric ( BC quantifying the proportion of shortest paths connecting all areas that are passing through any given area). We found that the network of S. alveolata reefs forms two main regional clusters, the Atlantic coast and the English Channel, which are connected only through weak sporadic dispersal events. Within each cluster, the network is characterized by relatively high connectivity among neighbouring areas with reefs, maintained even under higher connectivity thresholds. Simulating scenarios of sequential loss of reefs further identified high centrality reefs, those with highest BC values, key to network coherence. Effective conservation of this important reef habitat requires a network of protected areas designed to sustain a combination of locally important source reefs, and those that act as stepping stones connecting distant reefs.
Sliding mode learning compensator-based robust control of automotive steer-by-wire systems
Publisher: Inderscience Publishers
Date: 2016
A recurrent neural network for modeling crack growth of aluminium alloy
Publisher: Springer Science and Business Media LLC
Date: 21-09-2014
Design and Implementation of Adaptive Terminal Sliding-Mode Control on a Steer-by-Wire Equipped Road Vehicle
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2016
Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers
Publisher: MDPI AG
Date: 28-10-2021
DOI: 10.3390/ELECTRONICS10212637
Abstract: This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.
A novel sliding mode control for lane keeping in road vehicles
Publisher: IEEE
Date: 11-2016
Robust fast nonsingular terminal sliding mode control strategy for electronic throttle based on extreme learning machine
Publisher: IEEE
Date: 07-2019
Discrete-time sliding mode learning based congestion control for connection-oriented communication networks
Publisher: IEEE
Date: 11-2016
Direct Thrust Force Control of Primary Permanent Magnet Linear Motor Based on Improved Extended State Observer and Model-Free Adaptive Predictive Control
Publisher: MDPI AG
Date: 22-09-2022
DOI: 10.3390/ACT11100270
Abstract: A model-free adaptive predictive control algorithm based on an improved extended state observer (IESO) is proposed to solve the problem that the primary permanent magnet linear motor is susceptible to time-varying parameters and unknown disturbances. Firstly, a model-free adaptive control algorithm based on compact format is designed to achieve high control precision of the system and reduce thrust fluctuation, only through the input/output data of the system. Because the traditional model-free adaptive control is too sensitive to the internal parameters of the controller, a combination of model-free adaptive control and predictive control is further developed. By predicting the data for a future time in advance, the sensitivity to the internal parameters of the controller is reduced and the control performance is further improved. Since the load change and other nonlinear disturbances in practical applications have a great impact on the control effect of the system, an improved extended state observer is further used to compensate for the impact of nonlinear disturbances on the control system. In addition, the stability of the closed-loop system is analyzed. Comparable simulation results clearly demonstrate the good tracking performance and strong robustness of the proposed control.
Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine
Publisher: Springer Science and Business Media LLC
Date: 15-08-2021
A Novel Adaptive SMO-Based Simultaneous Diagnosis Method for IGBT Open-Circuit Faults and Current Sensor Incipient Faults of Inverters in PMSM Drives for Electric Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Sliding mode based learning control for interconnected systems
Publisher: IEEE
Date: 06-2013
Fault diagnosis and RUL prediction of nonlinear mechatronic system via adaptive genetic algorithm-particle filter
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Precise Discrete-Time Steering Control for Robotic Fish Based on Data-Assisted Technique and Super-Twisting-Like Algorithm
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
Active Front Steering-Based Electronic Stability Control for Steer-by-Wire Vehicles via Terminal Sliding Mode and Extreme Learning Machine
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2020
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