Neurodynamics-Based Visual Servo Predictive Control for Improving Smooth Movement of Logistics Omnidirectional Robots

Abstract

Smooth movement and constraint satisfaction are the key safety and effectiveness concerns of visual servoing systems of logistics transport robots. In this article, we propose a novel neurodynamics-based visual servo predictive control (NVSPC) approach of logistics omnidirectional mobile robots (OMRs) subject to various physical and visual constraints and nonlinearities. The general neurodynamics are introduced to the visual servoing error model based on feature point extraction. Then the neurodynamics-based nonlinear visual servoing error model is derived, which is further designed as the state-dependent linear parameter varying system with nonlinear control inputs. Moreover, the idea of quasi-min–max model predictive control (MPC) is used to design the visual servoing controller that is formulated as a semi-definite optimization problem being the form of linear matrix inequalities (LMIs). The controller is then determined by online solving the problem, with guaranteed recursive feasibility and stability. Two physical experiments verify the visual servoing performance of the proposed approach in terms of constraint satisfaction and smooth movement of the robot.