ORCID Profile
0000-0003-0222-430X
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Aerospace Engineering | Control Systems, Robotics and Automation | Artificial Intelligence and Image Processing | Zoology | Neural, Evolutionary and Fuzzy Computation | Animal Behaviour | Information Systems Management | Flight Control Systems | Interdisciplinary Engineering Not Elsewhere Classified | Research, Science And Technology Policy | Control Engineering | Electrical Engineering | Flight Dynamics | Aircraft Performance | Interdisciplinary Engineering |
ANZSRC Socio-Economic Objective (SEO)
Combined operations | Emerging Defence Technologies | Air transport | Law enforcement | Navy | Technological and organisational innovation | Aerospace Transport not elsewhere classified | Other | Computer software and services not elsewhere classified | Expanding Knowledge in the Biological Sciences | Transport not elsewhere classified
Related Links
Publications
Monotonous Trend Estimation of Deck Displacement for Automatic Landing of Rotorcraft UAVs
Publisher: Springer Science and Business Media LLC
Date: 16-10-2010
Helicopter flight control using inverse optimal control and backstepping
Publisher: IEEE
Date: 12-2012
Response component analysis for sea state estimation using artificial neural networks and vessel response spectral data
Publisher: Elsevier BV
Date: 10-2022
Self-Evolving Neural Control for a Class of Nonlinear Discrete-Time Dynamic Systems With Unknown Dynamics and Unknown Disturbances
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Flight control of a rotary wing UAV - a practical approach
Publisher: IEEE
Date: 2008
Robust altitude control of an unmanned autonomous helicopter using backstepping
Publisher: IEEE
Date: 07-2012
Investigation into the practicability of differential lift-based spacecraft rendezvous
Publisher: American Institute of Aeronautics and Astronautics (AIAA)
Date: 10-2017
DOI: 10.2514/1.G002537
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
The development of neural networks applications from perceptron to deep learning
Publisher: IEEE
Date: 10-2017
Hover flight control of a small helicopter using robust backstepping and PID
Publisher: IEEE
Date: 07-2012
Swarm Robotics: A Survey from a Multi-Tasking Perspective
Publisher: Association for Computing Machinery (ACM)
Date: 15-09-2023
DOI: 10.1145/3611652
Abstract: The behaviour of social insects such as bees and ants has influenced the development of swarm robots. To enable robots to cooperate together, swarm robotics employs principles such as communication, coordination, and collaboration. Collaboration among multiple robots can lead to a faster task completion time compared to the utilisation of a single, complex robot. One of the key aspects of swarm robotics is that control is distributed uniformly across the robots in the swarm, which boosts the system’s resilience and fault-tolerance. Through the use of the robots’ embodied sensors and actuators, this distributed control often facilitates the emergence of collective behaviours through the interaction of the robots with one another and with the environment. The purpose of this survey is to examine the reasons behind the lack of utilisation of swarm robots in multi-tasking applications, which will be accomplished by studying previous research works in the field. We examine the literature from the perspective of multi-tasking: we pay particular attention to concepts that contribute to the progress of swarm robotics for multi-tasking applications. In order to do this, we first examine the different studies in multi-tasking swarm robotics, covering platforms, multi-tasking scenarios, sub-task allocation methodologies, as well as performance metrics. We then highlight several swarm robotics-related disciplines that have significant effect on the development of swarm robotics for multi-tasking problems. We propose two taxonomies: the first categorises works based on the characteristics of the scenarios being handled, while the second taxonomy categorises works based on the swarming strategies utilised to achieve multi-tasking capabilities. We finish with a discussion of swarm robots’ existing limitations for real-world multitasking applications, as well as recommendations for future research directions.
Adaptive Trajectory Tracking for Quadrotor Systems in Unknown Wind Environments Using Particle Swarm Optimization-Based Strictly Negative Imaginary Controllers
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
The Race to Robustness: Exploiting Fragile Models for Urban Camouflage and the Imperative for Machine Learning Security
Publisher: IEEE
Date: 06-09-2023
An overview of insect-inspired guidance for application in ground and airborne platforms
Publisher: SAGE Publications
Date: 06-2004
Abstract: Flying insects provide a clear demonstration that living organisms can display surprisingly competent mechanisms of guidance and navigation, despite possessing relatively small brains and simple nervous systems. Consequently, they are proving to be excellent organisms in which to investigate how visual information is exploited to guide locomotion and navigation. Four illustrative ex les are described here, in the context of navigation to a destination. Bees negotiate narrow gaps by balancing the speeds of the images in the two eyes. Flight speed is regulated by holding constant the average image velocity as seen by the two eyes. This automatically ensures that flight speed is reduced to a safe level when the passage narrows. Smooth landings on a horizontal surface are achieved by holding image velocity constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. Roll and pitch are stabilized by balancing the signals registered by three visual organs, the ocelli, that view the horizon in the left, right and forward directions respectively. Tests of the feasibility of these navigational strategies, by implementation in robots, are described.
Stable Adaptive Controller Based on Generalized Regression Neural Networks and Sliding Mode Control for a Class of Nonlinear Time-Varying Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Online Model-Free Reinforcement Learning for Output Feedback Tracking Control of a Class of Discrete-Time Systems With Input Saturation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Self-organising BFBEL Control System for a UAV under Wind Disturbance
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Distributed Artificial Neural Networks-Based Adaptive Strictly Negative Imaginary Formation Controllers for Unmanned Aerial Vehicles in Time-Varying Environments
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Evolving neuro-fuzzy system based online identification of a bio-inspired flapping wing micro aerial vehicle
Publisher: IEEE
Date: 11-2017
Adaptive Second-Order Strictly Negative Imaginary Controllers Based on the Interval Type-2 Fuzzy Self-Tuning Systems for a Hovering Quadrotor With Uncertainties
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
A Nonlinear Position Controller for Maritime Operations of Rotary-wing UAVs
Publisher: Elsevier BV
Date: 2011
Automatic collective motion tuning using actor-critic deep reinforcement learning
Publisher: Elsevier BV
Date: 07-2022
Increasing feasibility of the field-programmable gate array implementation of an iterative image registration using a kernel-warping algorithm
Publisher: SPIE-Intl Soc Optical Eng
Date: 15-09-2017
Adaptive Terrain Perception and Decision-Making Systems for Agile Mobile Robots in Dynamic Search and Rescue Scenarios
Publisher: MDPI AG
Date: 13-10-2023
A Comprehensive Review of Shepherding as a Bio-Inspired Swarm-Robotics Guidance Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review
Publisher: Springer Science and Business Media LLC
Date: 17-08-2018
Fuzzy Self-Tuning of Strictly Negative-Imaginary Controllers for Trajectory Tracking of a Quadcopter Unmanned Aerial Vehicle
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Rotary wing UAV position control using backstepping
Publisher: IEEE
Date: 2007
Flight control of a rotary wing UAV using backstepping
Publisher: Wiley
Date: 12-05-2009
DOI: 10.1002/RNC.1458
Coverage Path Planning With Budget Constraints for Multiple Unmanned Ground Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Evolving fuzzy inference system based online identification and control of a quadcopter unmanned aerial vehicle
Publisher: IEEE
Date: 10-2017
A Novel Evolving Type-2 Fuzzy System for Controlling a Mobile Robot under Large Uncertainties
Publisher: MDPI AG
Date: 10-03-2023
Abstract: This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.
Multi-vehicle formation control and obstacle avoidance using negative-imaginary systems theory
Publisher: Elsevier BV
Date: 03-2021
Neural Network-Based Self-Learning of an Adaptive Strictly Negative Imaginary Tracking Controller for a Quadrotor Transporting a Cable-Suspended Payload With Minimum Swing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
A Robust Self-Adaptive Interval Type-2 TS Fuzzy Logic for Controlling Multi-Input-Multi-output Nonlinear Uncertain Dynamical Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
A Deep Hierarchical Reinforcement Learner for Aerial Shepherding of Ground Swarms
Publisher: Springer International Publishing
Date: 2019
Swarm Metaverse for Multi-Level Autonomy Using Digital Twins
Publisher: MDPI AG
Date: 19-05-2023
DOI: 10.3390/S23104892
Abstract: Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human–swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.
Non-linear position control for hover and automatic landing of unmanned aerial vehicles
Publisher: Institution of Engineering and Technology (IET)
Date: 2012
Self-Learning in Aerial Robotics Using Type-2 Fuzzy Systems: Case Study in Hovering Quadrotor Flight Control
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Hybrid adaptive negative imaginary- neural-fuzzy control with model identification for a quadrotor
Publisher: Elsevier BV
Date: 06-2021
Vision-based terrain following for an unmanned rotorcraft
Publisher: Wiley
Date: 04-2008
DOI: 10.1002/ROB.20239
Abstract: Experiments from biology suggest that the sensing of image motion or optic flow in insects provides a means of determining the range to obstacles and terrain. When combined with a measure of ground speed from another sensor such as global positioning system, optic flow can be used to provide a measure of an aircraft's height above terrain. We apply this principle to the control of height in a helicopter, leading to the first optic flow–based terrain‐following system for an unmanned helicopter. Using feedback of the height estimated from optic flow ranging to the collective pitch control of the helicopter, it has been possible to maintain terrain clearance in flights of up to 2 km. In this paper, we present flight test data demonstrating the successful application of this to an 80‐kg Yamaha RMAX unmanned helicopter and an 8‐kg electric helicopter. To complete this work, we have extended the optic flow image interpolation algorithm ( I 2 A ) to include an adaptive capability providing a greater dynamic range. The new algorithm, called the iterative image interpolation algorithm ( I 2 A ), exhibits excellent robustness in an outdoor environment and makes it suitable for flight control in a real‐world environment. © 2008 Wiley Periodicals, Inc.
Model predictive attitude control of vario unmanned helicopter
Publisher: IEEE
Date: 11-2011
A flight control scheme to improve position tracking performance of Rotary-Wing UASs
Publisher: Elsevier BV
Date: 06-2013
Robust altitude control for a small helicopter by considering the ground effect compensation
Publisher: IEEE
Date: 07-2012
Prediction of vertical motions for landing operations of UAVs
Publisher: IEEE
Date: 2008
Online identification of a rotary wing Unmanned Aerial Vehicle from data streams
Publisher: Elsevier BV
Date: 03-2019
Realtime Adaptive Intelligent Control System for Quadcopter UAV with Payload Uncertainties
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Altitude identification and intelligent control of a flapping wing micro aerial vehicle using modified generalized regression neural networks
Publisher: IEEE
Date: 11-2017
Online identification of complex multi-input-multi-output system based on generic evolving neuro-fuzzy inference system
Publisher: IEEE
Date: 04-2013
An Autonomous Recovery System for a Rotorcraft UAV Operating in rough Seas
Publisher: American Institute of Aeronautics and Astronautics
Date: 26-06-2010
DOI: 10.2514/6.2010-7733
Stellar yields and abundances: new directions from planetary nebulae
Publisher: Cambridge University Press (CUP)
Date: 10-2016
DOI: 10.1017/S1743921317001247
Abstract: Planetary nebulae retain the signature of the nucleosynthesis and mixing events that occurred during the previous AGB phase. Observational signatures complement observations of AGB and post-AGB stars and their binary companions. The abundances of the elements heavier than iron such as Kr and Xe in planetary nebulae can be used to complement abundances of Sr/Y/Zr and Ba/La/Ce in AGB stars, respectively, to determine the operation of the slow neutron-capture process (the s process) in AGB stars. Additionally, observations of the Rb abundance in Type I planetary nebulae may allow us to infer the initial mass of the central star. Several noble gas components present in meteoritic stardust silicon carbide (SiC) grains are associated with implantation into the dust grains in the high-energy environment connected to the fast winds from the central stars during the planetary nebulae phase.
A computationally efficient approach for NN based system identification of a rotary wing UAV
Publisher: Springer Science and Business Media LLC
Date: 08-2010
Simultaneous replanning with vectorized particle swarm optimization algorithm
Publisher: IEEE
Date: 11-2016
Generic Evolving Self-Organizing Neuro-Fuzzy Control of Bio-Inspired Unmanned Aerial Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Distributed Formation Control Using Fuzzy Self-Tuning of Strictly Negative Imaginary Consensus Controllers in Aerial Robotics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Visual Tracking and LIDAR Relative Positioning for Automated Launch and Recovery of an Unmanned Rotorcraft from Ships at Sea
Publisher: Wiley
Date: 06-2009
High-Precision OFDM-Based Multiple Ultrasonic Transducer Positioning Using a Robust Optimization Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Planar trajectory tracking controller for a small-sized helicopter considering servos and delay constraints
Publisher: IEEE
Date: 11-2011
Platform Enhancements and System Identification for Control of an Unmanned Helicopter
Publisher: IEEE
Date: 2006
Fuzzy clustering based modelling and adaptive controlling of a flapping wing micro air vehicle
Publisher: IEEE
Date: 11-2017
Entropy Fuzzy System Identification for the Heave Flight Dynamics of a Model-Scale Helicopter
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Approaches for a tether-guided landing of an autonomous helicopter
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2006
Multimodal Fusion for Objective Assessment of Cognitive Workload: A Review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Flight validation of a feedforward gust-attenuation controller for an autonomous helicopter
Publisher: Elsevier BV
Date: 12-2011
Fuzzy clustering based nonlinear system identification and controller development of Pixhawk based quadcopter
Publisher: IEEE
Date: 02-2017
Design of a gust-attenuation controller for landing operations of Unmanned Autonomous Helicopters
Publisher: IEEE
Date: 07-2009
Nonlinear multi-input multi-output system identification using neuro-evolutionary methods for a quadcopter
Publisher: IEEE
Date: 02-2017
Related Organisations
No related organisations have been discovered for Matthew Garratt.
Related Funding Activities
Linkage Projects - Grant ID: LP0453467
Start Date: 06-2004
End Date: 06-2008
Amount: $215,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP200100710
Start Date: 05-2020
End Date: 06-2024
Amount: $373,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP220101883
Start Date: 10-2022
End Date: 10-2025
Amount: $411,224.00
Funder: Australian Research Council
View Funded ActivityResearch Networks - Grant ID: RN0459557
Start Date: 12-2004
End Date: 12-2010
Amount: $1,950,000.00
Funder: Australian Research Council
View Funded ActivityDiscovery Projects - Grant ID: DP1095108
Start Date: 2010
End Date: 06-2013
Amount: $290,000.00
Funder: Australian Research Council
View Funded Activity