Workspace-Based Model Predictive Control for Cable-Driven Robots
Chen Song, Darwin Lau
Abstract
The control of cable-driven robots is challenging due to the system’s nonlinearity, actuation redundancy, and the unilaterally bounded actuation constraints. To solve this problem, a workspace-based model predictive control (W-MPC) scheme is proposed, which combines the online model predictive control with offline workspace analysis. Using the workspace, a set of convex constraints can be generated for a given reference trajectory. This can then be used to formulate a convex optimization problem for the online W-MPC. Meanwhile, strict recursive feasibility and stability are obtained by taking advantage of the predictive feature of MPC. To demonstrate the effectiveness of the proposed W-MPC, simulation was performed on a 2-link planar cable-driven robot and a spatial cable-driven parallel robot for both nominal and non-nominal scenarios. Hardware experiment was also carried out using a 3 degree-of-freedom planar cable robot. The results show that the controller is efficient and effective to perform motion tracking with the cable force constraints satisfied despite the existence of various model uncertainties.