A5019906827- Robot Manipulation and Learning
- Adaptive Control of Nonlinear Systems
- Teleoperation and Haptic Systems
- Muscle activation and electromyography studies
- Soft Robotics and Applications
- Adaptive Dynamic Programming Control
- Iterative Learning Control Systems
- Robotic Path Planning Algorithms
- Prosthetics and Rehabilitation Robotics
- Advanced Control Systems Optimization
- Robotic Mechanisms and Dynamics
- Distributed Control Multi-Agent Systems
- Control and Dynamics of Mobile Robots
- EEG and Brain-Computer Interfaces
- Hand Gesture Recognition Systems
- Reinforcement Learning in Robotics
- Robotics and Sensor-Based Localization
- Gaze Tracking and Assistive Technology
- Neural Networks and Applications
- Robotic Locomotion and Control
- Robotics and Automated Systems
- Neural Networks Stability and Synchronization
- Neuroscience and Neural Engineering
- Advanced Vision and Imaging
- Motor Control and Adaptation
University of Liverpool
2024-2025
Bristol Robotics Laboratory
2019-2025
South China University of Technology
2016-2025
Hefei University of Technology
2025
Wuhan Textile University
2025
Chinese Academy of Medical Sciences & Peking Union Medical College
2021-2025
University of the West of England
2019-2024
Hebei Normal University
2024
Chinese University of Hong Kong
2022-2024
Zhongyuan University of Technology
2023-2024
This paper develops a novel integral sliding mode controller (ISMC) for general type of underwater robots based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). The difficulties associated with the unmeasured velocities, unknown disturbances, uncertain hydrodynamics robot have been successfully solved in control design. An adaptive MIMO-ESO is designed not only to estimate unmeasurable linear angular but also external disturbances. ISMC then using Lyapunov synthesis,...
In this paper, we investigate the trajectory tracking problem for a fully actuated autonomous underwater vehicle (AUV) that moves in horizontal plane. External disturbances, control input nonlinearities and model uncertainties are considered our design. Based on dynamics derived discrete-time domain, two neural networks (NNs), including critic an action NN, integrated into adaptive The NN is introduced to evaluate long-time performance of designed current time step, used compensate unknown...
For parameter identifications of robot systems, most existing works have focused on the estimation veracity, but few literature are concerned with convergence speed. In this paper, we developed a control/identification scheme to identify unknown kinematic and dynamic parameters enhanced rate. Superior traditional methods, information error was properly integrated into proposed identification algorithm, such that performance achieved. Besides, Newton-Euler (NE) method used build model, where...
Robots with coordinated dual arms are able to perform more complicated tasks that a single manipulator could hardly achieve. However, rigorous motion precision is required guarantee effective cooperation between the arms, especially when they grasp common object. In this case, internal forces applied on object must also be considered in addition external forces. Therefore, prescribed tracking performance at both transient and steady states first specified, then, controller synthesized...
This paper presents a novel human-like learning controller to interact with unknown environments. Strictly derived from the minimization of instability, motion error, and effort, compensates for disturbance in environment interaction tasks by adapting feedforward force impedance. In contrast conventional controllers, new can deal unstable situations that are typical tool use gradually acquire desired stability margin. Simulations show this is good model human motor adaptation. Robotic...
This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface technique is incorporated into radial-basis-function neural networks (NNs)-based framework to eliminate problem explosion complexity. To avoid analytic computation, command filter employed produce signals and their derivatives. Different from directly toward asymptotic tracking, accuracy identified models taken consideration. The prediction error...
This paper studies both indirect and direct global neural control of strict-feedback systems in the presence unknown dynamics, using dynamic surface (DSC) technique a novel manner. A new switching mechanism is designed to combine an adaptive controller approximation domain, together with robust that pulls transient states back into domain from outside. In comparison conventional techniques, which could only achieve semiglobally uniformly ultimately bounded stability, proposed scheme...
In this paper, a novel control scheme is developed for teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate effects caused by communication delays dynamics uncertainties. The system set up with TouchX joystick as master device simulated Baxter robot arm slave robot. haptic feedback provided human operator sense interaction force between environment when manipulating stylus of joystick. To utilize...
This paper proposes an enhanced robot skill learning system considering both motion generation and trajectory tracking. During demonstrations, dynamic movement primitives (DMPs) are used to model robotic motion. Each DMP consists of a set systems that enhances the stability generated toward goal. A Gaussian mixture regression integrated improve performance DMP, such more features can be extracted from multiple demonstrations. The learned scaled in space time. Besides, neural-network-based...
In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on Baxter robot. Guaranteed performance of the achieved at both kinematic dynamic levels. At level, automatic collision avoidance by design level exploiting joint space redundancy, thus human operator would be able to only concentrate motion robot's end-effector without concern possible collision. A posture restoration scheme also integrated based simulated parallel enable manipulator restore back...
In this paper, automatic motion control is investigated for wheeled inverted pendulum (WIP) models, which have been widely applied modeling of a large range two modern vehicles. First, the underactuated WIP model decomposed into fully actuated second-order subsystem Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</sub> consisting planar movement vehicle forward and yaw angular motions, passive (nonactuated) first-order...
Due to strongly coupled nonlinearities of the grasped dual-arm robot and internal forces generated by objects, control with uncertain kinematics dynamics raises a challenging problem. In this paper, an adaptive fuzzy scheme is developed for robot, where approximate Jacobian matrix applied address kinematic control, while decentralized logic controller constructed compensate robotic arms manipulated object. Also, novel finite-time convergence parameter adaptation technique estimation...
Sliding mode control (SMC) is attractive for nonlinear systems due to its invariance both parametric and nonparametric uncertainties. However, the of SMC not guaranteed in a reaching phase. Integral (ISMC) eliminates phase such that achieved an entire system response. To reduce chattering ISMC, it was suggested switching element smoothed by using low-pass filter integral sliding variable modified. This study discusses several crucial problems regarding performance, modification, improvement...
In this paper, a physical human-robot interaction approach is presented for the developed robotic exoskeleton using admittance control to deal with human subject's intention as well unknown inertia masses and moments in dynamics. The represented by reference trajectory when complying external force. Online estimation of stiffness employed variable impedance property exoskeleton. Admittance first based on measured force order generate tasks. Then, adaptive proposed uncertain dynamics...
In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of unknown robotic dynamics and environment with which robot comes into contact. First, an FNN algorithm is developed to identify plant model. Second, introduced regulate input order improve environment-robot interaction, can track desired trajectory generated by learning. Third, light condition requiring move finite...
In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The design based on the transformed predictor form, and actor-critic NN control architecture includes two NNs, whereas critic used to approximate strategic utility function, action employed minimize both function tracking error. A deterministic learning technique has been guarantee that partial persistent excitation condition of internal states satisfied during...
In this article, an admittance-based controller for physical human-robot interaction (pHRI) is presented to perform the coordinated operation in constrained task space. An admittance model and a soft saturation function are employed generate differentiable reference trajectory ensure that end-effector motion of manipulator complies with human avoids collision surroundings. Then, adaptive neural network (NN) involving integral barrier Lyapunov (IBLF) designed deal tracking issues. Meanwhile,...
In this paper, we investigate optimized adaptive control and trajectory generation for a class of wheeled inverted pendulum (WIP) models vehicle systems. Aiming at shaping the controlled dynamics to be minimized motion tracking errors as well angular accelerations, employ linear quadratic regulation optimization technique obtain an optimal reference model. Adaptive has then been developed using variable structure method ensure model exactly matched in finite-time horizon, even presence...
This article discusses the control design and experiment validation of a flexible two-link manipulator (FTLM) system represented by ordinary differential equations (ODEs). A reinforcement learning (RL) strategy is developed that based on actor–critic structure to enable vibration suppression while retaining trajectory tracking. Subsequently, closed-loop with proposed RL algorithm proved be semi-global uniform ultimate bounded (SGUUB) Lyapunov's direct method. In simulations, approach...
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An improved human-robot collaborative control scheme is proposed in a teleoperated minimally invasive surgery scenario, based on hierarchical operational space formulation of seven-degree-of-freedom redundant robot. Redundancy exploited to guarantee remote center motion (RCM) constraint and provide compliant behavior for the medical staff. Based implemented framework, an RCM safe are applied nullspace achieve surgical tasks with interaction. Due physical interactions, safety accuracy may be...
This article presents a control scheme for the robot manipulator's trajectory tracking task considering output error constraints and input saturation. We provide an alternative way to remove feasibility condition that most BLF-based controllers should meet design on premise constraint violation possibly happens due A bounded barrier Lyapunov function is proposed adopted handle constraints. Besides, suppress saturation effect, auxiliary system designed emerged into scheme. Moreover,...
It has been established that the transfer of human adaptive impedance is great significance for physical human-robot interaction (pHRI). By processing electromyography (EMG) signals collected from muscles, limb could be extracted and transferred to robots. The existing interfaces rely only on visual feedback and, thus, may insufficient skill in a sophisticated environment. In this paper, haptic mechanism introduced result muscle activity would generate EMG natural manner, order achieve...
In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USVs, we consider that mass and damping matrices are not diagonal input saturation problem. Neural networks (NNs) employed to approximate unknown external disturbances uncertain hydrodynamics USVs. Moreover, both full-state feedback output presented, unmeasurable velocities controller...