Adaptive Neural Network Control of Uncertain Systems with Full State Constraints and Unknown Gain Sign

2019 ◽  
pp. 515-525
Author(s):  
Heqing Liu ◽  
Tianping Zhang ◽  
Meizhen Xia ◽  
Ziwen Wu
Keyword(s):  
Neural Network ◽  
Uncertain Systems ◽  
State Constraints ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Full State ◽  
Full State Constraints ◽  
Control Of Uncertain Systems

Barrier Lyapunov function–based adaptive neural network control for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints via command filtering

2020 ◽  
pp. 107754632096263
Author(s):  
Senkui Lu ◽  
Xingcheng Wang
Keyword(s):  
Neural Network ◽  
Fractional Order ◽  
State Constraints ◽  
Network Control ◽  
Neural Network Control ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Adaptive Neural Network Control ◽  
Full State ◽  
Full State Constraints

This article considers the problem of adaptive neural network control via command filtering for incommensurate fractional-order chaotic permanent magnet synchronous motors with full-state constraints and parameter uncertainties. First, a neural network state observer based on a K-filter is established to reconstruct unmeasured feedback information. Then, the command filtered technology is used to overcome the inherent “explosion of complexity” problem under fractional-order framework. Furthermore, to eliminate the errors generated by filters, an error compensation system is used. Meanwhile, the nonlinear unknown functions are approximated by using neural networks. In addition, the barrier Lyapunov functions are designed to avoid the violation of the state constraints. Finally, the availability of the proposed control algorithm is revealed by numerical simulations.

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