A Position and Torque Switching Control of a Single-Link Flexible Joint Manipulator using Model Predictive Control

Abstract

The advances in Robotics in recent decades allow a growing range of robotic manipulator applications in various industry sectors. This directly impacts Human-Robot Interaction (HRI), increasing tasks that require a shared work environment, safety performance, and the contact detection ability of the robotic manipulator. Consequently, control methods capable of predicting contact, and controlling force or trajectory to avoid damage during collisions become increasingly necessary either for safety or performance reasons. This work proposes a Model Predictive Control (MPC) approach for a robotic nonlinear switching system. MPC is an advanced control method that consists of solving an optimization problem to find the optimal control actions, minimizing a cost function. Two advantages of the method are the capability to improve controller performance by incorporating future reference information into the control problem and the addition of constraints. In this paper, the switching system has two modes: position control mode and torque control mode (contact mode). The MPC Control is defined separately for each control mode so that a switching algorithm is implemented. The results show that the implemented method can effectively control both modes of the system, presenting low prediction error, approximately 2% in position control mode and 0.5% in torque control mode, even considering cyclical changes in the modes.