scholarly journals Adaptive Fixed-Time Control of Strict-Feedback High-Order Nonlinear Systems

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 963
Author(s):  
Yang Li ◽  
Jianhua Zhang ◽  
Xiaoyun Ye ◽  
Cheng Siong Chin
Keyword(s):  
Nonlinear Systems ◽  
Fixed Time ◽  
High Order ◽  
Convergence Time ◽  
Feedback Form ◽  
Time Control ◽  
Control Scheme ◽  
Lyapunov Stability Criterion ◽  
The Stability ◽  
Strict Feedback

This paper examines the adaptive control of high-order nonlinear systems with strict-feedback form. An adaptive fixed-time control scheme is designed for nonlinear systems with unknown uncertainties. In the design process of a backstepping controller, the Lyapunov function, an effective controller, and adaptive law are constructed. Combined with the fixed-time Lyapunov stability criterion, it is proved that the proposed control scheme can ensure the stability of the error system in finite time, and the convergence time is independent of the initial condition. Finally, simulation results verify the effectiveness of the proposed control strategy.

scholarly journals Adaptive Robust Control for Networked Strict-Feedback Nonlinear Systems with State and Input Quantization

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2783
Author(s):  
Yanbin Liu ◽  
Jue Wang ◽  
Luis Gomes ◽  
Weichao Sun
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Networked Control Systems ◽  
Tracking Error ◽  
Adaptive Robust Control ◽  
Feedback Form ◽  
Control Approach ◽  
Input Quantization ◽  
The Stability ◽  
Strict Feedback

Backstepping method is a successful approach to deal with the systems in strict-feedback form. However, for networked control systems, the discontinuous virtual law caused by state quantization introduces huge challenges for its applicability. In this article, a quantized adaptive robust control approach in backsetpping framework is developed in this article for networked strict-feedback nonlinear systems with both state and input quantization. In order to prove the efficiency of the designed control scheme, a novel form of Lyapunov candidate function was constructed in the process of analyzing the stability, which is applicable for the systems with nondifferentiable virtual control law. In particular, the state and input quantizers can be in any form as long as they meet the sector-bound condition. The theoretic result shows that the tracking error is determined by the pregiven constants and quantization errors, which are also verified by the simulation results.

Adaptive finite-time control for stochastic nonlinear systems using multi-dimensional Taylor network

2021 ◽  
pp. 014233122110396
Author(s):  
Shan-Liang Zhu ◽  
Ming-Xin Wang ◽  
Yu-Qun Han
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Lyapunov Stability Theory ◽  
Loop System ◽  
Stochastic Nonlinear Systems ◽  
Time Control ◽  
Control Scheme ◽  
The Stability

In this paper, the problem of adaptive finite-time multi-dimensional Taylor network (MTN) control for a class of stochastic nonlinear systems is investigated. By combining the MTN-based approximate method and adaptive backstepping technique, a novel adaptive finite-time MTN control scheme is proposed. In this scheme, the MTNs are used to approximate the unknown nonlinear functions of the systems. The finite-time Lyapunov stability theory is utilized to prove the stability of the close-loop system. The proposed scheme can ensure that all signals in the closed-loop system are bounded in probability and the tracking error converges to a small neighborhood of the origin in a finite time. Three simulation examples are presented to show the effectiveness of the control scheme. It should be pointed that the adaptive MTN controller proposed in this paper has the advantages of fast computational speed and good real-time performance thanks to the simple structure of the MTN.

scholarly journals Adaptive Fixed-time Control for Nonlinear Systems Against Time-varying Actuator Faults

2021 ◽  
Author(s):  
Yu Mei ◽  
Jing Wang ◽  
Ju H. Park ◽  
Kaibo Shi ◽  
Hao Shen
Keyword(s):  
Nonlinear Systems ◽  
Fault Tolerant ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Fixed Time ◽  
Time Interval ◽  
Time Varying ◽  
Actuator Faults ◽  
Time Control ◽  
Control Scheme

Abstract The adaptive fixed-time control problem for nonlinear systems with time-varying actuator faults is investigated in this paper. A novel adaptive fixed-time controller is designed via combining the Lyapunov stability theory with the backstepping method. It can be adapted to both system uncertainties and unknown actuator faults. Compared with the existing fault-tolerant control schemes subject to actuator faults, the adaptive fixed-time neural networks control scheme can make sure that the tracking error is convergent in a small neighborhood of the origin within a fixed-time interval, and it does not depend on the original states of the system and actuator faults. In light of the control scheme proposed in this paper, the fixed-time stability of the closed-loop system can be guaranteed by theoretical analysis, and a numerical example is provided to verify the effectiveness of obtained theoretical results.

scholarly journals Stabilization of a Class of Stochastic Nonlinear Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Valiollah Ghaffari ◽  
Hamid Reza Karimi ◽  
Navid Noroozi ◽  
S. Vahid Naghavi
Keyword(s):  
Nonlinear Systems ◽  
Stochastic Systems ◽  
Adaptive Controller ◽  
Stochastic Nonlinear Systems ◽  
Nonlinear Stochastic Systems ◽  
Closed Loop Systems ◽  
Time Control ◽  
Control Scheme ◽  
Control Schemes ◽  
The Stability

This paper addresses two control schemes for stochastic nonlinear systems. Firstly, an adaptive controller is designed for a class of motion equations. Then, a robust finite-time control scheme is proposed to stabilize a class of nonlinear stochastic systems. The stability of the closed-loop systems is established based on stochastic Lyapunov stability theorems. Links between these two methods are given. The efficiency of the control schemes is evaluated using numerical simulations.

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