| Abstract: | ABSTRACT This paper investigates the function approximation problem by using Walsh functions to establish a Walsh‐basis‐function neural network (WBFNN). The proposed novel system avoids the possible heavy computation problem of a controller usually encountered in adaptive neural controller design. With the developed adaptation scheme combined with the sliding mode control strategy for a class of nonlinear systems, the proposed WBFNN‐based controller can guarantee global stability of the closed‐loop system in the Lyapunov sense. The output tracking error then converges to zero asymptotically, and boundedness of all the signals in the whole system is ensured. Simulation validation for a nonlinear unstable system was performed to verify the effectiveness of the proposed controller design. |
