Robust Model Predictive Tracking Control for Robot Manipulators With Disturbances

In this article, a robust model predictive control (MPC) algorithm based on tube approach is presented for time-varying trajectory tracking control of robot manipulator. The robot manipulator is affected by disturbances, and is subject to both joint state constraints and input torque limits. To ensure the satisfaction of constraints, by taking into account the effect of disturbances explicitly, the constraints are tightened for the nominal system, and the MPC strategy drives the actual system trajectory within a tube centered around the nominal system trajectory. This article shows how to construct three key ingredients, i.e., the terminal cost, controller, and region, of the robust model predictive tracking controller to guarantee the feasibility of MPC optimization problem for all time, and to ensure input-to-state stability of the closed-loop tracking error system. The performance of the proposed algorithm is validated through an experimental study using a Baxter robot.

Automated summary

This article presents a robust Model Predictive Control (MPC) algorithm based on a tube approach for time-varying trajectory tracking control of a robot manipulator. The algorithm tightens constraints, constructs key components, and ensures system stability and performance through experimental validation using a Baxter robot.