| Title |
Adaptive Neural Control for Strict-feedback Nonlinear Systems without Backstepping |
| Authors |
박장현(Park, Jang-Hyun) ; 김성환(Kim, Seong-Hwan) ; 박영환(Park, Young-Hwan) |
| Keywords |
Adaptive neural control ; Strict-feedback nonlinear system |
| Abstract |
A new adaptive neuro-control algorithm for a SISO strict-feedback nonlinear system is proposed. All the previous adaptive neural control algorithms for strict-feedback nonlinear systems are based on the backstepping scheme, which makes the control law and stability analysis very complicated. The main contribution of the proposed method is that it demonstrates that the state-feedback control of the strict-feedback system can be viewed as the output-feedback control problem of the system in the normal form. As a result, the proposed control algorithm is considerably simpler than the previous ones based on backstepping. Depending heavily on the universal approximation property of the neural network (NN), only one NN is employed to approximate the lumped uncertain system nonlinearity. The Lyapunov stability of the NN weights and filtered tracking error is guaranteed in the semi-global sense. |