Adaptive Dynamic Surface Control for Uncertain Nonlinear Systems With Unknown Control Directions

This paper focuses on an uncertain control directions problem in strict-feedback systems with unmeasured dynamics. In this control law, a dynamic signal is chosen to handle the problem of unmeasured dynamics, and in order to eliminate the uncertain functions in the system, radial basis function neural networks (RBFNN) are utilized. Nussbaum gain functions are applied for the uncertain signs of control gains. We combine dynamic surface control (DSC) technique with above control methods to overcome the complexity explosion caused by backstepping, and the process of calculation is simplified. By using the proposed controllers all signals in the system are semi-globally uniformly ultimately bounded (SGUUB). Moreover, the tracking deviations of outputs are guaranteed within a small domain of origin. Finally, a simulation example exemplifies the availability of the controller. It is concluded that the control law is effective, which is illustrated by both theoretical analysis and simulation results.