Adaptive neural control for strict-feedback nonlinear systems without backstepping

Jang-Hyun Park, Seong-Hwan Kim, Chae-Joo Moon
IEEE Transactions on Neural Networks 2009, 20 (7): 1204-9
In this brief, a new adaptive neurocontrol algorithm for a single-input-single-output (SISO) strict-feedback nonlinear system is proposed. Most of the previous adaptive neural control algorithms for strict-feedback nonlinear systems were 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 semiglobal sense.

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