Asymptotic tracking control for state-constrained nonlinear systems with disturbances: theory and experiment

In this paper, we investigate the asymptotic tracking control problem for a kind of nonlinear system under full-state constraints and external disturbances. First, the original constrained system is equivalently transformed into an unconstrained system by using the nonlinear state-dependent function (SDF), which eliminates the feasibility condition. Next, a new disturbance observer (DO) is proposed to estimate asymptotically and compensate for the unknown disturbances. Meanwhile, a neuro-adaptive tracking controller based on a novel first-order filter is constructed, and the dynamic surface control (DSC) scheme is used to avoid repeated differentiations. Compared with the existing backstepping-based methods and DOs, the developed control strategy not only enables the system output to asymptotically track the desired trajectory, but also realizes the filtering error asymptotically converges to zero. And all signals in the closed-loop systems are bounded. The simulation and the experiment results are given finally to show the effectiveness of the proposed control algorithm.