scholarly journals Adaptive Neural Control with Prespecified Tracking Accuracy for a Class of Switched Systems Subject to Input Delay

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Xu Zhang ◽  
Jian Wu ◽  
Wu Ai ◽  
Jing Li
Keyword(s):  
Switched Systems ◽  
Neural Control ◽  
A Priori ◽  
Tracking Error ◽  
Input Delay ◽  
Tracking Accuracy ◽  
Adaptive Tracking Control ◽  
Control Scheme ◽  
Switching Functions ◽  
Adaptive Neural Controller

This paper is concerned with the adaptive tracking control design for a class of uncertain switched systems subject to input delay. Unlike the existing results on uncertain switched systems, the new proposed control scheme ensures that the tracking error converges to the accuracy given a priori according to the requirement. To achieve this aim, some nonnegative switching functions are introduced to replace the conventional Lyapunov function. In addition, neural networks are used to approximate the unknown simultaneous domination functions. By combining the backstepping technique and some common nonnegative switching functions, a stable adaptive neural controller is established. It can be shown that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB) and the tracking error satisfies the predefined accuracy. The effectiveness of the proposed control scheme is verified by a simulation example.

scholarly journals Adaptive Control Design with Assigned Tracking Accuracy for a Class of Nonlinearly Parameterized Input-Delayed Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Dajie Yao ◽  
Jing Li ◽  
Jian Wu
Keyword(s):  
Adaptive Control ◽  
Closed Loop ◽  
A Priori ◽  
Tracking Error ◽  
Input Delay ◽  
Tracking Accuracy ◽  
Unknown Parameters ◽  
Controlled System ◽  
Delayed Systems ◽  
Nonlinearly Parameterized

This paper addresses the adaptive control problem of a class of nonlinear systems with unknown parameters and input delay, and the tracking accuracy of the controlled system is assigned a priori. The Pade approximation method is introduced to deal with the problem from the input delay. By creating a group of nonnegative functions, an appropriate controller is designed with the backstepping technology. It is shown that under the obtained controller, the boundedness of all the closed-loop signals is guaranteed, and the tracking error especially can converge to the accuracy assigned a priori. Finally, a simulation example is given to verify the effectiveness of the proposed scheme.

Adaptive multi-dimensional Taylor network tracking control for a class of switched nonlinear systems with input nonlinearity

2020 ◽  
Vol 42 (13) ◽  
pp. 2482-2491
Author(s):  
Shan-Liang Zhu ◽  
De-Yu Duan ◽  
Lei Chu ◽  
Ming-Xin Wang ◽  
Yu-Qun Han ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Low Cost ◽  
Tracking Error ◽  
Switched Nonlinear Systems ◽  
Nonlinear Functions ◽  
Input Nonlinearity ◽  
Control Approach ◽  
Adaptive Tracking Control ◽  
Control Scheme

In this paper, a multi-dimensional Taylor network (MTN)-based adaptive tracking control approach is proposed for a class of switched nonlinear systems with input nonlinearity. Firstly, the input nonlinearity is assumed to be bounded by a sector interval. Secondly, with the help of MTNs approximating the unknown nonlinear functions, a novel adaptive MTN control scheme has the advantages of low cost, simple structure and real time feature is developed via backstepping technique. It is shown that the tracking error finally converges to a small domain around the origin and all signals in the closed-loop system are bounded. Finally, two examples are given to demonstrate the effectiveness of the proposed control scheme.

scholarly journals Adaptive Predefined Performance Neural Control for Robotic Manipulators with Unknown Dead Zone

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Shifen Shao ◽  
Kaisheng Zhang ◽  
Jun Li ◽  
Jirong Wang
Keyword(s):  
Neural Control ◽  
Dead Zone ◽  
Tracking Error ◽  
Transient Behavior ◽  
Robotic Manipulators ◽  
System Stability ◽  
Control Effect ◽  
Tracking Errors ◽  
Control Scheme ◽  
Funnel Control

This paper proposes an adaptive predefined performance neural control scheme for robotic manipulators in the presence of nonlinear dead zone. A neural network (NN) is utilized to estimate the model uncertainties and unknown dynamics. An improved funnel function is designed to guarantee the transient behavior of the tracking error. The proposed funnel function can release the assumption on the conventional funnel control. Then, an adaptive predefined performance neural controller is proposed for robotic manipulators, while the tracking errors fall within a prescribed funnel boundary. The closed-loop system stability is proved via Lyapunov function. Finally, the numerical simulation results based on a 2-DOF robotic manipulator illustrate the control effect of the presented approach.

scholarly journals Tracking error constrained robust adaptive neural prescribed performance control for flexible hypersonic flight vehicle

2017 ◽  
Vol 14 (1) ◽  
pp. 172988141668270 ◽  
Author(s):  
Zhonghua Wu ◽  
Jingchao Lu ◽  
Jingping Shi ◽  
Qing Zhou ◽  
Xiaobo Qu
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Flight Vehicle ◽  
Model Uncertainties ◽  
Adaptive Neural Control ◽  
Prescribed Performance ◽  
Hypersonic Flight Vehicle ◽  
Hypersonic Flight ◽  
Control Scheme ◽  
Robust Adaptive

A robust adaptive neural control scheme based on a back-stepping technique is developed for the longitudinal dynamics of a flexible hypersonic flight vehicle, which is able to ensure the state tracking error being confined in the prescribed bounds, in spite of the existing model uncertainties and actuator constraints. Minimal learning parameter technique–based neural networks are used to estimate the model uncertainties; thus, the amount of online updated parameters is largely lessened, and the prior information of the aerodynamic parameters is dispensable. With the utilization of an assistant compensation system, the problem of actuator constraint is overcome. By combining the prescribed performance function and sliding mode differentiator into the neural back-stepping control design procedure, a composite state tracking error constrained adaptive neural control approach is presented, and a new type of adaptive law is constructed. As compared with other adaptive neural control designs for hypersonic flight vehicle, the proposed composite control scheme exhibits not only low-computation property but also strong robustness. Finally, two comparative simulations are performed to demonstrate the robustness of this neural prescribed performance controller.

Zero Phase Robust Control of XY Table Linear Servo System

2013 ◽  
Vol 419 ◽  
pp. 713-717
Author(s):  
Xi Mei Zhao ◽  
Ming Ming Jiang ◽  
Hong Yi Li ◽  
Hao Liu
Keyword(s):  
Disturbance Observer ◽  
Position Control ◽  
Control Method ◽  
Phase Error ◽  
Tracking Error ◽  
Servo System ◽  
Direct Drive ◽  
Tracking Accuracy ◽  
Control Scheme ◽  
Zero Phase

For direct drive XY table servo system, position control is designed. Considering the error which is caused by the disturbance of the system, friction factor and so on. The control method combing the zero phase error tracking controller (ZPETC) with the disturbance observer (DOB) is adopted. The system tracking error is reduced by adopting ZPETC, and through influences of disturbance to the system is diminished by the disturbance observer. Thus the tracking accuracy and robustness of the system are improved. Simulation results show that this control scheme is effective. It can obviously improve the accuracy of the system.

scholarly journals Partial-State-Constrained Adaptive Intelligent Tracking Control of Nonlinear Nonstrict-Feedback Systems with Unmodeled Dynamics and Its Application

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yuzhuo Zhao ◽  
Ben Niu ◽  
Xiaoli Jiang ◽  
Ping Zhao ◽  
Huanqing Wang ◽  
...  
Keyword(s):  
Tracking Control ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Unmodeled Dynamics ◽  
Feedback Systems ◽  
Feedback Form ◽  
Design Algorithm ◽  
Adaptive Tracking Control ◽  
Control Scheme ◽  
Nonlinear Uncertainties

In this paper, an adaptive intelligent control scheme is presented to investigate the problem of adaptive tracking control for a class of nonstrict-feedback nonlinear systems with constrained states and unmodeled dynamics. By approximating the unknown nonlinear uncertainties, utilizing Barrier Lyapunov functions (BLFs), and designing a dynamic signal to deal with the constrained states and the unmodeled dynamics, respectively, an adaptive neural network (NN) controller is developed in the frame of the backstepping design. In order to simplify the design process, the nonstrict-feedback form is treated by using the special properties of Gaussian functions. The proposed adaptive control scheme ensures that all variables involved in the closed-loop system are bounded, the corresponding state constraints are not violated. Meanwhile, the tracking error converges to a small neighborhood of the origin. In the end, the proposed intelligent design algorithm is applied to one-link manipulator to demonstrate the effectiveness of the obtained method.

scholarly journals Adaptive Asymptotic Tracking Control for a Class of Uncertain Switched Systems via Dynamic Surface Technique

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Xuemiao Chen ◽  
Qianjin Zhao ◽  
Chunsheng Zhang ◽  
Jian Wu
Keyword(s):  
Switched Systems ◽  
Tracking Control ◽  
Control Method ◽  
Reference Signal ◽  
Adaptive Controller ◽  
Dynamic Surface Control ◽  
Dynamic Surface ◽  
Adaptive Tracking Control ◽  
Nonlinear Switched Systems ◽  
Control Scheme

A novel adaptive tracking control scheme is proposed for a class of uncertain nonlinear switched systems with perturbations in this paper. The common Lyapunov function method is introduced to handle the switched system in the design process of the desired adaptive controller. In addition, a dynamic surface control method is proposed by employing a nonlinear filter such that the “explosion of complexity” problem existing in the conventional backstepping design can be overcome. Under the presented adaptive controller, all the closed-loop signals are semiglobally bounded, and especially the output signal of the controlled system can follow the given reference signal asymptotically. To show the availability of the presented control scheme, a simulation is given in this paper.

scholarly journals Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Min Wang ◽  
Huiping Ye ◽  
Zhiguang Chen
Keyword(s):  
Neural Control ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Dynamic Surface Control ◽  
Dynamic Surface ◽  
Flexible Joint ◽  
Persistent Excitation ◽  
Neural Learning ◽  
Control Scheme ◽  
Flexible Joint Manipulator

This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

scholarly journals Adaptive Neural Control of Nonaffine Nonlinear Systems without Differential Condition for Nonaffine Function

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Chaojiao Sun ◽  
Bo Jing ◽  
Zongcheng Liu
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Tracking Error ◽  
Nonlinear Function ◽  
Mean Value ◽  
Mean Value Theorem ◽  
Adaptive Neural Control ◽  
Control Gain ◽  
Control Scheme ◽  
Nonaffine Nonlinear Systems

An adaptive neural control scheme is proposed for nonaffine nonlinear system without using the implicit function theorem or mean value theorem. The differential conditions on nonaffine nonlinear functions are removed. The control-gain function is modeled with the nonaffine function probably being indifferentiable. Furthermore, only a semibounded condition for nonaffine nonlinear function is required in the proposed method, and the basic idea of invariant set theory is then constructively introduced to cope with the difficulty in the control design for nonaffine nonlinear systems. It is rigorously proved that all the closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Finally, simulation examples are provided to demonstrate the effectiveness of the designed method.

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