A Unified Hybrid Control Architecture for Multi-DOF Robotic Manipulators

TL;DR

This paper introduces a unified hybrid control architecture combining MPC and feedback regulation for multi-DOF robotic manipulators, improving control performance and computational efficiency through ML-based hardware implementation validated by experiments.

Contribution

It presents a novel integrated control scheme for complex manipulators, including stability analysis and a machine learning-based hardware implementation for real-time control.

Findings

Enhanced control accuracy under disturbances

Reduced computational load with ML hardware implementation

Demonstrated superior performance in simulations and experiments

Abstract

Multi-degree-of-freedom (DOF) robotic manipulators exhibit strongly nonlinear, high-dimensional, and coupled dynamics, posing significant challenges for controller design. To address these issues, this work proposes a unified hybrid control architecture that integrates model predictive control (MPC) with feedback regulation, together with a stability analysis of the proposed scheme. The proposed approach mitigates the optimization difficulty associated with high-dimensional nonlinear systems and enhances overall control performance. Furthermore, a hardware implementation scheme based on machine learning (ML) is proposed to achieve high computational efficiency while maintaining control accuracy. Finally, simulation and hardware experiments under external disturbances validate the proposed architecture, demonstrating its superior performance, hardware feasibility, and generalization capability for multi-DOF manipulation tasks.