Unified Model Predictive Interaction Control Integrating Impedance Matching and Constraint Optimization

Abstract

This paper proposes a model predictive interaction control (MPIC) framework based on impedance matching, embedding impedance regulation into the predictive optimization process. The proposed approach ensures seamless transitions between impedance control in unconstrained situations and optimal control adaptation under task-specific and physical constraints, enhancing interaction safety, robustness, and adaptability. A unified robot-environment interaction model is formulated by incorporating series-parallel interaction properties to simultaneously consider the impact of external perturbations and robot reference position variations on force prediction and optimization. Simulation and experimental results validate the effectiveness of MPIC over conventional impedance control (IC) in terms of constraint handling, disturbance rejection, and balancing compliance and precision, providing a scalable and adaptable solution for complex robot-environment interaction.