Adaptive full-state constrained tracking control for mobile robotic system with unknown dead-zone input

Abstract

This paper studies the adaptive neural networks (NNs) tracking control problem for a class of mobile robot systems with full-state constraints. First, to compensate for the adverse effects of the unknown dead-zone input, which is ubiquitous in mobile robot motors, a new robust control algorithm is put forward by the use of adaptive control technique. Then, a new unified barrier function (UBF) is constructed to deal with the problem of state constraints. Different from traditional barrier Lyapunov function (BLF) methods, which can only constrain the error of the system state and virtual controller, our proposed control method can constrain the system state directly by introducing a novel nonlinear transformation function and a new coordinate transformation. It’s worth noting that the UBF can be utilized to deal with both constrained and unconstrained cases by resizing parameters without changing the control structure. Furthermore, adaptive NNs are introduced to approximate the uncertainty of the system. Finally, based on the Lyapunov stability theory, it is proved that all signals in the closed-loop system are ultimately bounded and the full-state constraints are never violated. The effectiveness of the control method is verified on simulation examples and a mobile robot experimental platform.