| Title | Adaptive Neural Network Asymptotic Tracking for Nonstrict-Feedback Switched Nonlinear Systems |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Yongchao, Zhu, Qidan |
| Conference Name | 2021 International Conference on Security, Pattern Analysis, and Cybernetics（SPAC) |
| Keywords | adaptive backstepping, Adaptive systems, Artificial neural networks, asymptotic tracking control, backstepping, composability, control systems, Metrics, Networked Control Systems Security, Neural Network, pubcrawl, resilience, Resiliency, security, simulation, switched nonlinear systems, Switches |
| Abstract | This paper develops an adaptive neural network (NN) asymptotic tracking control scheme for nonstrict-feedback switched nonlinear systems with unknown nonlinearities. The NNs are used to dispose the unknown nonlinearities. Different from the published results, the asymptotic convergence character is achieved based on the bound estimation method. By combining some smooth functions with the adaptive backstepping scheme, the asymptotic tracking control strategy is presented. It is proved that the fabricated scheme can guarantee that the system output can asymptotically follow the desired signal, and also that all signals of the entire system are bounded. The validity of the devised scheme is evaluated by a simulation example. |
| DOI | 10.1109/SPAC53836.2021.9539952 |
| Citation Key | liu_adaptive_2021 |
Adaptive Neural Network Asymptotic Tracking for Nonstrict-Feedback Switched Nonlinear Systems