A Simplified Adaptive Neural Network Prescribed Performance Controller for Uncertain MIMO Feedback Linearizable Systems

2015 ◽  
Vol 26 (3) ◽  
pp. 589-600 ◽  
Author(s):  
Achilles Theodorakopoulos ◽  
George A. Rovithakis
Keyword(s):  
Neural Network ◽  
Adaptive Neural Network ◽  
Prescribed Performance ◽  
Feedback Linearizable Systems

Adaptive neural network control of second-order underactuated systems with prescribed performance constraints

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Can Ding ◽  
Jing Zhang ◽  
Yingjie Zhang ◽  
Zhe Zhang
Keyword(s):  
Neural Network ◽  
Second Order ◽  
Singular Problem ◽  
Underactuated System ◽  
Adaptive Neural Network ◽  
Prescribed Performance ◽  
Performance Constraints ◽  
Linearization Methods ◽  
Control Scheme ◽  
Adaptive Neural Network Control

Abstract This paper studies the trajectory tracking control problem of second-order underactuated system subject to system uncertainties and prescribed performance constraints. By combining radial basis function neural networks (RBFNNs) with input–output linearization methods, an adaptive neural network-based control approach is proposed and the adaptive laws are given through Lyapunov method and Taylor expansion linearization approach. The main contributions of this paper are that: (1) by introducing weight performance function and transformation function, the states never violate the prescribed performance constraints; (2) the control scheme takes the unknown control gain direction into consideration and the singular problem of control design can be avoided; (3) through rigorously stability analysis, all signal of closed-loop system are proved to be uniformly ultimately bounded. The effectiveness of the proposed control scheme was verified by comparative simulation.

scholarly journals Adaptive Neural Network Dynamic Inversion with Prescribed Performance for Aircraft Flight Control

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Wendong Gai ◽  
Honglun Wang ◽  
Jing Zhang ◽  
Yuxia Li
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Network Dynamic ◽  
Dynamic Inversion ◽  
Adaptive Neural Network ◽  
Prescribed Performance ◽  
Aircraft Flight ◽  
Attitude Angle ◽  
Neural Network Dynamic ◽  
Aircraft Flight Control

An adaptive neural network dynamic inversion with prescribed performance method is proposed for aircraft flight control. The aircraft nonlinear attitude angle model is analyzed. And we propose a new attitude angle controller design method based on prescribed performance which describes the convergence rate and overshoot of the tracking error. Then the model error is compensated by the adaptive neural network. Subsequently, the system stability is analyzed in detail. Finally, the proposed method is applied to the aircraft attitude tracking control system. The nonlinear simulation demonstrates that this method can guarantee the stability and tracking performance in the transient and steady behavior.

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