Neuroadaptive tracking control for uncertain pure-feedback systems under dynamic constraints

This paper presents a neuroadaptive tracking control method for a class of pure-feedback nonlinear systems in the presence of dynamic constraints and unmodeled dynamics simultaneously. By introducing a nonlinear mapping (NM), the tracking control problem for constrained pure-feedback system is recast into a regulation problem of the converted system without constraints. Such transformation allows the states to be confined within given regions directly, this is in contrast to the commonly used Barrier Lyapunov Function method that relies on the upper bound of the virtual control errors. To handle the unmodeled dynamics in the system, a dynamic compensation signal is introduced. It is shown that in the proposed scheme the neural networks (NN) not only act as a universal approximator to deal with unknown nonlinearity, but also function as a decoupler to cope with the coupling effects between state and the new variable arising from the introduction of the NM and the backstepping design. Simulation results also confirm the effectiveness of the proposed method.