scholarly journals Event-Triggered Adaptive Control for Prescribed Performance Tracking of Constrained Uncertain Nonlinear Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 23527-23536
Author(s):  
Xiao-Ning Shi ◽  
Ruifeng Li ◽  
Zhi-Gang Zhou ◽  
Di Zhou
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Uncertain Nonlinear Systems ◽  
Prescribed Performance ◽  
Performance Tracking ◽  
Event Triggered

Event-Triggered Adaptive Control for a Class of Uncertain Nonlinear Systems

2017 ◽  
Vol 62 (4) ◽  
pp. 2071-2076 ◽  
Author(s):  
Lantao Xing ◽  
Changyun Wen ◽  
Zhitao Liu ◽  
Hongye Su ◽  
Jianping Cai
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Uncertain Nonlinear Systems ◽  
Event Triggered

scholarly journals Prescribed Performance Tracking Control for High-Order Uncertain Nonlinear Systems

2022 ◽  
Author(s):  
Jiling Ding ◽  
Weihai Zhang
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
State Feedback ◽  
Low Complexity ◽  
High Order ◽  
State Feedback Control ◽  
Uncertain Nonlinear Systems ◽  
Prescribed Performance ◽  
Approximation Techniques ◽  
Performance Tracking

Abstract This paper considers the prescribed performance tracking control for high-order uncertain nonlinear systems. For any initial system condition, a state feedback control is designed, which guarantees the prescribed tracking performance and the boundedness of closed-loop signals. The proposed controller can be implemented without using any approximation techniques for estimating unknown nonlinearities. In this respect, a significant advantage of this article is that the explosion of complexity is avoided, which is raised by backstepping-like approaches that are typically employed to the control of uncertain nonlinear systems, and a low-complexity controller is achieved. Moreover, contrary to the existing results in existing literature, the restrictions on powers of high-order nonlinear systems are relaxed to make the considered problem having stronger theoretical and practical values. The effectiveness of the proposed scheme is verified by some simulation results.

Event-triggered Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Systems

2021 ◽  
pp. 2150275
Author(s):  
Hui Hu ◽  
Yang Li ◽  
Wei Yi ◽  
Yuebiao Wang ◽  
Fan Qu ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Control Method ◽  
Uncertain Nonlinear Systems ◽  
Network Resource ◽  
Lyapunov Approach ◽  
Stability Method ◽  
The Stability ◽  
Event Triggered

In the paper, an event triggering adaptive control method based on neural network (NN) is proposed for a class of uncertain nonlinear systems with external disturbances. In order to reduce the network resource utilization, a novel event-triggered condition by the Lyapunov approach is proposed. In addition, the NN controller and adaptive parameters determined by the Lyapunov stability method are updated only at triggered instants to reduce the amount of calculation. Only one NN is used as the controller in the entire system. The stability analysis results of the closed-loop system are obtained by the Lyapunov approach, which shows that all the signals in the systems with bounded disturbance are semi-globally bounded. Zeno behavior is avoided. Finally, the analytical design is confirmed by the simulation results on a two-link robotic manipulator.

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