Adaptive neural network control for uncertain strict-feedback nonlinear systems with unknown control coefficients: A fully actuated system approach

In this paper, an adaptive control scheme is designed for a class of strict-feedback nonlinear systems with unknown control coefficients. Unlike the existing iterative design approaches, we apply a fully actuated system approach to reduce the algorithmic complexity. First, the considered strict-feedback nonlinear systems are transformed into fully actuated systems by state transformations. After that, we compensate for the uncertainties arising from the unknown control coefficients and unknown nonlinear functions by the neural network approximation technique. Then, an adaptive neural network controller based on a fully actuated system approach is designed. According to Lyapunov stability analysis, it is shown that all the signals of the closed-loop system are bounded and the system states converge to a small neighborhood of the origin under the designed controller. Moreover, by further enhancing the adaptive neural network mechanism, this paper extends the relevant results to more general uncertain strict-feedback nonlinear systems with unknown coefficients. Finally, the effectiveness of the proposed control scheme is demonstrated by a practical application example.