Adaptive NN control of partially known nonlinear strict-feedback systems

This paper presents a direct adaptive NN control scheme for partially known strict-feedback systems. In this scheme, a priori information of the system under study can be incorporated into controller design. The benefits of using the a priori information include: (i) much simplified algorithm, and (ii) much less neurons employed for approximation, which makes the algorithm computationally feasible. With the help of NN approximation, the overparametrization problem in adaptive backstepping design is avoided without using tuning functions. Semi-global uniform ultimate boundedness of all the signals in the closed-loop is guaranteed and the output of the system is proven to converge to a small neighborhood of the desired trajectory. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters. Simulation studies are conducted to show the effectiveness of the scheme