| Title |
Direct Adaptive Neural Control of Perturbed Strict-feedback Nonlinear Systems |
| Authors |
박장현(Park, Jang-Hyun) ; 김성환(Kim, Seong-Hwan) ; 유영재(Yoo, Young-Jae) |
| Keywords |
Adaptive neural control ; Perturbed strict-feedback system ; Nonlinear control |
| Abstract |
An adaptive neural controller for perturbed strict-feedback nonlinear system is proposed. All the previous adaptive neural (or fuzzy) controllers are based on the backstepping scheme where the universal approximators are employed in every design steps. These schemes involve virtual controls and their time derivatives that make the stability analysis and implementation of the controller very complex. This fact is called 'explosion of complexty ' since the complexity grows exponentially as the system dynamic order increases. The proposed adaptive neural control scheme adopt the backstepping design procedure only for determining ideal control law and employ only one neural network to approximate the finally selected ideal controller, which makes the controller design procedure and stability analysis considerably simple compared to the previously proposed controllers. It is shown that all the time-varing signals containing tracking error are stable in the Lyapunov viewpoint. |