Adaptive decentralized fault-tolerant tracking control for large-scale nonlinear systems with input quantization

2018 ◽  
Vol 28 (9) ◽  
pp. 3342-3356 ◽  
Author(s):  
Cai-Cheng Wang ◽  
Guang-Hong Yang
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Large Scale ◽  
Fault Tolerant ◽  
Input Quantization

Adaptive decentralized fault-tolerant tracking control of a class of uncertain large-scale nonlinear systems with actuator faults

2016 ◽  
Vol 40 (3) ◽  
pp. 831-842 ◽  
Author(s):  
Yang Yang ◽  
Dong Yue
Keyword(s):  
Nonlinear Systems ◽  
Tracking Control ◽  
Large Scale ◽  
Fault Tolerant ◽  
Dynamic Surface Control ◽  
Mean Value ◽  
Value Theory ◽  
Tracking Errors ◽  
Dynamic Surface ◽  
The Stability

We are concerned with the fault-tolerant tracking control affair for a class of large-scale multi-input and multi-output (MIMO) nonlinear systems suffering from actuator failures. Taking advantage of the mean-value theory and the implicit function theorem, the non-affine subsystems are transformed into affine forms. Neural networks (NNs) are utilized to approximate unknown virtual control signals, and then an adaptive NN-based decentralized tracking control strategy is exploited recursively by combining backstepping methods as well as the dynamic surface control (DSC) methodology. In theory, the stability of the resulting whole system is rigorously analysed, where it is proven that all signals remain uniformly ultimately bounded (UUB) and the designed strategy can guarantee the convergence of tracking errors via a suitable choice of control parameters. Finally, two simulation examples, both practical and numerical examples, are illustrated to verify the feasibility of the theoretical claims.

Sign in / Sign up

Export Citation Format

Share Document