Adaptive optimal tracking control for a class of nonlinear systems with fully unknown parameters
| Autor: | Hamid Shiri, Hossein Mohammadi |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Lyapunov function
0209 industrial biotechnology Artificial neural network Computer science Hamilton–Jacobi–Bellman equation 02 engineering and technology Optimal control System dynamics symbols.namesake Nonlinear system 020901 industrial engineering & automation Control theory Adaptive system 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing |
| Zdroj: | 2017 5th International Conference on Control, Instrumentation, and Automation (ICCIA). |
| DOI: | 10.1109/icciautom.2017.8258703 |
| Popis: | In this paper, a new adaptive optimal tracking approximate solution for the infinite-horizon function is presented to design a new controller for a class of fully unknown continuous-times nonlinear systems. A dynamic neural network identifier (DNN) derived from a Lyapunov function, is achieved to approximate the unknown system dynamics. We utilize an adaptive steady-state controller based on the identified plant to keep tracking performance and an adaptive optimal controller is used to stabilize the systems. A critic neural network is utilized for estimating optimal value function of the Hamilton-Jacobi-Bellman (HJB). The simulation examples are presented to confirm the effectiveness of the proposed controller method. |
| Databáze: | OpenAIRE |
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