Adaptive Neural Control of Strict-feedback Systems with Predefined Constraints

In this paper, a novel nonlinear mapping is proposed to achieve the output constraints, which transforms constrained tracking errors into equivalent unconstrained ones. Further, adaptive neural network (NN) control of strict-feedback systems is investigated based on the above nonlinear mapping. The control method proposed in the paper ensures that all the signals are bounded, output constraints are never violated and asymptotic tracking performance is achieved. Finally, a numerical example is provided to verify the proposed scheme.