Barrier Lyapunov function-based robot control with an augmented neural network approximator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zuguo Zhang ◽  
Qingcong Wu ◽  
Xiong Li ◽  
Conghui Liang
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Control Algorithm ◽  
Tracking Performance ◽  
Content Type ◽  
Barrier Lyapunov Function ◽  
Output Error ◽  
Control Scheme ◽  
Stringent Constraint ◽  
Continuous Dynamic

Purpose Considering the complexity of dynamic and friction modeling, this paper aims to develop an adaptive trajectory tracking control scheme for robot manipulators in a universal unmodeled method, avoiding complicated modeling processes. Design/methodology/approach An augmented neural network (NN) constituted of radial basis function neural networks (RBFNNs) and additional sigmoid-jump activation function (SJF) neurons is introduced to approximate complicated dynamics of the system: the RBFNNs estimate the continuous dynamic term and SJF neurons handle the discontinuous friction torques. Moreover, the control algorithm is designed based on Barrier Lyapunov Function (BLF) to constrain output error. Findings Lyapunov stability analysis demonstrates the exponential stability of the closed-loop system and guarantees the tracking errors within predefined boundaries. The introduction of SJFs alleviates the limitation of RBFNNs on discontinuous function approximation. Owing to the fast learning speed of RBFNNs and jump response of SJFs, this modified NN approximator can reconstruct the system model accurately at a low compute cost, and thereby better tracking performance can be obtained. Experiments conducted on a manipulator verify the improvement and superiority of the proposed scheme in tracking performance and uncertainty compensation compared to a standard NN control scheme. Originality/value An enhanced NN approximator constituted of RBFNN and additional SJF neurons is presented which can compensate the continuous dynamic and discontinuous friction simultaneously. This control algorithm has potential usages in high-performance robots with unknown dynamic and variable friction. Furthermore, it is the first time to combine the augmented NN approximator with BLF. After more exact model compensation, a smaller tracking error is realized and a more stringent constraint of output error can be implemented. The proposed control scheme is applicable to some constraint occasion like an exoskeleton and surgical robot.

scholarly journals Robust Tracking Control of the Euler–Lagrange System Based on Barrier Lyapunov Function and Self-Structuring Neural Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yi Wang ◽  
He Ma ◽  
Weidong Wu
Keyword(s):  
Neural Network ◽  
Robust Control ◽  
Lyapunov Function ◽  
Tracking Control ◽  
Tracking Error ◽  
Robust Tracking ◽  
Robust Tracking Control ◽  
Barrier Lyapunov Function ◽  
Control Scheme ◽  
Unknown Disturbances

This article studies the robust tracking control problems of Euler–Lagrange (EL) systems with uncertainties. To enhance the robustness of the control systems, an asymmetric tan-type barrier Lyapunov function (ATBLF) is used to dynamic constraint position tracking errors. To deal with the problems of the system uncertainties, the self-structuring neural network (SSNN) is developed to estimate the unknown dynamics model and avoid the calculation burden. The robust compensator is designed to estimate and compensate neural network (NN) approximation errors and unknown disturbances. In addition, a relative threshold event-triggered strategy is introduced, which greatly saves communication resources. Under the proposed robust control scheme, tracking behavior can be implemented with disturbance and unknown dynamics of the EL systems. All signals in the closed-loop system are proved to be bounded by stability analysis, and the tracking error can converge to the neighborhood near the origin. The numerical simulation results show the effectiveness and the validity of the proposed robust control scheme.

Aerodynamic coefficients modeling using Levenberg–Marquardt algorithm and network

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhigang Wang ◽  
Aijun Li ◽  
Lihao Wang ◽  
Xiangchen Zhou ◽  
Boning Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Aerodynamic Parameter ◽  
Content Type ◽  
Flight Data ◽  
Output Error ◽  
Aerodynamic Derivatives ◽  
Levenberg Marquardt ◽  
Error Method

Purpose The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm. Design/methodology/approach Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives. Findings Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data. Research limitations/implications The proposed method requires iterative calculation and can only identify parameters offline. Practical implications The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems. Originality/value In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.

Finite-Time-Disturbance-Observer-Based Tracking Control of DC Motors With Velocity Constraint

2015 ◽  
Author(s):  
Z. L. Dong ◽  
J. Y. Yao ◽  
Z. B. Xu ◽  
D. W. Ma
Keyword(s):  
Lyapunov Function ◽  
Finite Time ◽  
Disturbance Observer ◽  
Design Procedure ◽  
Tracking Performance ◽  
Backstepping Design ◽  
Barrier Lyapunov Function ◽  
Dc Motors ◽  
Asymptotic Tracking ◽  
Velocity Constraint

The integration design of high tracking performance and velocity constraint for dc motors is present in this paper. When designing advanced controllers for dc motors, it would be best to take the constraint of output velocity into consideration due to performance and/or physical limitations. A barrier Lyapunov function, which grows to infinity when its arguments approach some pre-set limits, is utilized to prevent constraint violation. By ensuring the stabilization of the barrier Lyapunov function, and imposing a hard-bound on associated error signals through the steps of the backstepping design procedure, the system output velocity constraint is guaranteed to be not transgressed. However, potential disturbances, including unmodelled dynamic effects, parametric uncertainties and external disturbances may destroy above theoretical results and degrade the tracking performance. In this paper, a finite time disturbance observer is employed and integrated with the barrier Lyapunov function based backstepping design to achieve the asymptotic tracking without the violation of the velocity constraint meanwhile overcoming the disturbances. Extensive simulation results are provided to illustrate the performance of the proposed control strategy.

Study on TCP/AQM network congestion with adaptive neural network and barrier Lyapunov function

2019 ◽  
Vol 363 ◽  
pp. 27-34 ◽  
Author(s):  
Kun Wang ◽  
Yang Liu ◽  
Xiaoping Liu ◽  
Yuanwei Jing ◽  
Georgi M. Dimirovski
Keyword(s):  
Neural Network ◽  
Lyapunov Function ◽  
Network Congestion ◽  
Adaptive Neural Network ◽  
Barrier Lyapunov Function

scholarly journals Barrier Lyapunov Function-Based Adaptive Control of an Uncertain Hovercraft with Position and Velocity Constraints

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Mingyu Fu ◽  
Taiqi Wang ◽  
Chenglong Wang
Keyword(s):  
Lyapunov Function ◽  
Tracking Error ◽  
Path Following ◽  
External Disturbances ◽  
Barrier Lyapunov Function ◽  
Yaw Rate ◽  
Path Following Control ◽  
Control Scheme ◽  
Velocity Constraints ◽  
Theoretical Analyses

This paper considers the problem of constrained path following control for an underactuated hovercraft subject to parametric uncertainties and external disturbances. A four-degree-of-freedom hovercraft model with unknown curve-fitted coefficients is first rewritten into a parameterized form. By introducing a barrier Lyapunov function into the line-of-sight guidance, the specific transient tracking performance in terms of position error is guaranteed. A novel constrained yaw rate controller is proposed to ensure time-varying yaw rate constraint satisfaction, in which the yaw rate barrier is required to vary with the speed of the hovercraft. Moreover, a command filter is incorporated into the control design to generate the desired virtual controls and its time derivatives. Theoretical analyses show that, under the proposed controller, the position tracking error constraints and the yaw rate constraint can be strictly guaranteed. Finally, numerical simulations illustrate the effectiveness and advantages of the proposed control scheme.

scholarly journals Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Jinzhu Peng ◽  
Zeqi Yang ◽  
Tianlei Ma
Keyword(s):  
Neural Network ◽  
Impedance Control ◽  
Tracking Error ◽  
Tracking Performance ◽  
Robotic Systems ◽  
Position Tracking ◽  
External Disturbances ◽  
Outer Loop ◽  
Control Scheme ◽  
Force Tracking

In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

Fault-tolerant control of spacecraft attitude regulation: a concise adaptive dual-mode scheme

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yu Lu ◽  
Pengpeng Ye ◽  
Ming-Zhe Dai ◽  
Jin Wu ◽  
Chengxi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Attitude Control ◽  
Control Algorithm ◽  
Fault Tolerant ◽  
Spacecraft Attitude ◽  
Attitude Control System ◽  
Dual Mode ◽  
Content Type ◽  
Control Scheme ◽  
Adaptive Backstepping Design

Purpose This paper aims to address the spacecraft attitude regulation problem in the presence of extrinsic disturbances and actuator faults. Design/methodology/approach Based on adaptive backstepping design technique, a new concise adaptive dual-mode control scheme is proposed, which can either use the fault information detected by fault diagnosis mechanisms or switch to the fault-unknown mode when the fault diagnosis information is non-existent for control signal generation. These two modes share an adaptive mechanism that reduces the complexity of the algorithm. Findings The new fault-tolerant attitude control algorithm can accommodate both modes with and without fault diagnosis mechanisms. Originality/value The proposed algorithm in this paper can be applied to both cases when the attitude control system is equipped with or without fault diagnosis capability. This also enhances the robustness of attitude control algorithm. This study performs numerical simulations and verifies that the algorithm could effectively adapt to both modes.

PID control of an omnidirectional mobile platform based on an RBF neural network controller

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Niu Zijie ◽  
Zhang Peng ◽  
Yongjie Cui ◽  
Zhang Jun
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Model ◽  
Neural Network Model ◽  
Control Algorithm ◽  
Rbf Neural Network ◽  
Mobile Platform ◽  
Content Type ◽  
The Neural Network ◽  
Omnidirectional Mobile Platform

Purpose Omnidirectional mobile platforms are still plagued by the problem of heading deviation. In four-Mecanum-wheel systems, this problem arises from the phenomena of dynamic imbalance and slip of the Mecanum wheels while driving. The purpose of this paper is to analyze the mechanism of omnidirectional motion using Mecanum wheels, with the aim of enhancing the heading precision. A proportional-integral-derivative (PID) setting control algorithm based on a radial basis function (RBF) neural network model is introduced. Design/methodology/approach In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. Findings The network RBF NN1 calculates the deviations ?Kp, ?Ki and ?Kd to regulate the three coefficients Kp, Ki and Kd of the heading angle PID controller. This corrects the driving heading in real time, resolving the problems of low heading precision and unstable driving. The experimental data indicate that, for a externally imposed deviation in the heading angle of between 34º and ∼38°, the correction time for an omnidirectional mobile platform applying the algorithm during longitudinal driving is reduced by 1.4 s compared with the traditional PID control algorithm, while the overshoot angle is reduced by 7.4°; for lateral driving, the correction time is reduced by 1.4 s and the overshoot angle is reduced by 4.2°. Originality/value In this study, the mechanism of omnidirectional motion using Mecanum wheels is analyzed, with the aim of enhancing the heading precision. A PID setting control algorithm based on an RBF neural network model is introduced. The algorithm is based on a kinematics model for an omnidirectional mobile platform and corrects the driving heading in real time. In this algorithm, the neural network RBF NN2 is used for identifying the state of the system, calculating the Jacobian information of the system and transmitting information to the neural network RBF NN1. The method is innovative.

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