A Robust Tube-Based Smooth-MPC for Robot Manipulator Planning

• DOI: 10.48550/arxiv.2103.09693
• Publication Date: 2021-01-01
• Authors: Yu Luo; Mingxuan Jing; Tianying Ji; Fuchun Sun; Huaping Liu
• Keywords: Model Predictive Control; Linearization; Smoothness

Model Predictive Control (MPC) has shown the great performance of target optimization and constraint satisfaction. However, heavy computation Optimal Problem (OCP) at each triggering instant brings serious delay from state sampling to control signals, which limits applications MPC in resource-limited robot manipulator systems over complicated tasks. In this paper, we propose a novel robust tube-based smooth-MPC strategy for nonlinear planning with disturbances constraints. Based on piecewise linearization prediction, our improves smoothness optimizes process. By deducing deviation real system states nominal states, can predict next set current instant. And by using as initial condition, solve OCP ahead store optimal controls based eliminates delay. Furthermore, linearize given upper bound error, reducing complexity improving response speed. theoretical framework tube MPC, prove that is recursively feasible closed-loop stable constraints disturbances. Numerical simulations have verified efficacy designed approach compared conventional MPC.