Robust Tube-Based Predictive Control for Visual Servoing of Constrained Differential-Drive Mobile Robots

Abstract

This paper proposes a control strategy for the vision-based control problem of a nonholonomic constrained differential-drive mobile robots with bounded disturbance by using robust tube-based model predictive control (MPC) method. The proposed control strategy mainly consists of an ancillary state feedback controller and an MPC control for a nominal robotic system. First, the state-error kinematics of the nominal system is converted into a chained form system, and then its MPC optimization is computed to deal with a quadratic programming optimization problem by integrating a linear variable inequality-based primal-dual neural network (LVI-PDNN). Next, the gain scheduling of the ancillary state feedback is obtained via solving robust pole assignment using LVI-PDNN. An optimal state trajectory is thus generated for the nominal robotic system by the MPC without various uncertainties, the ancillary state feedback control forces the state variables to be constrained within an invariant designed tube. Finally, extensive experimental results on stabilization control of the nonholonomic mobile robot are provided to verify the effectiveness of the proposed robust tube-based MPC.