DSC-based RBF neural network control for nonlinear time-delay systems with time-varying full state constraints

2021 ◽  
Author(s):  
Youguo He ◽  
Yu Zhou ◽  
Yingfeng Cai ◽  
Chaochun Yuan ◽  
Jie Shen
Keyword(s):  
Neural Network ◽  
State Constraints ◽  
Rbf Neural Network ◽  
Network Control ◽  
Delay Systems ◽  
Time Varying ◽  
Neural Network Control ◽  
Time Delay Systems ◽  
Full State ◽  
Full State Constraints

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.

Command filtered adaptive neural tracking control of uncertain nonlinear time-delay systems with asymmetric time-varying full state constraints and actuator saturation

2020 ◽  
pp. 095965182097526
Author(s):  
Yuxiang Wu ◽  
Tian Xu ◽  
Haoran Fang
Keyword(s):  
Time Delay ◽  
Tracking Control ◽  
State Constraints ◽  
Actuator Saturation ◽  
Small Neighborhood ◽  
Delay Systems ◽  
Time Varying ◽  
Time Delay Systems ◽  
Full State ◽  
Full State Constraints

This article investigates the command filtered adaptive neural tracking control for uncertain nonlinear time-delay systems subject to asymmetric time-varying full state constraints and actuator saturation. To stabilize such a class of systems, the radial basis function neural networks and the backstepping technique are used to structure an adaptive controller. The command filter is utilized to overcome the complexity explosion problem in backstepping. By employing the Lyapunov–Krasovskii functionals, the effect of time-delay is eliminated. The asymmetric time-varying barrier Lyapunov functions are designed to ensure full state constraint satisfaction. Moreover, the hyperbolic tangent function and an instrumental variable are introduced to deal with actuator saturation. All signals in the closed-loop system are proved to be bounded and the tracking error converges to a small neighborhood of the origin. Finally, two examples are provided to illustrate the effectiveness of the proposed method.

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