Full-Dynamics Real-Time Nonlinear Model Predictive Control of Heavy-Duty Hydraulic Manipulator for Trajectory Tracking Tasks

2025-10-27

AI Summary

Heavy hydraulic manipulators (HHMs) pose significant challenges for high-precision, real-time trajectory tracking due to strong nonlinearities, high power requirements, and stringent physical and safety constraints. This paper proposes a full-dynamic, real-time nonlinear model predictive control (NMPC) framework—the first to enforce constraint satisfaction on the complete nonlinear dynamical model at 1 kHz. The method innovatively integrates multiple shooting, real-time multi-source sensor feedback, and virtual decomposition control to simultaneously ensure Cartesian-space end-effector accuracy and hard joint-level constraints on force, velocity, and position. Experimental validation on a full-scale HHM platform demonstrates strict adherence to actuator limits in both joint and task spaces, achieving substantially improved trajectory tracking accuracy and operational safety. This work establishes a new benchmark for real-time closed-loop control of large-scale hydraulic systems.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Heavy-duty hydraulic manipulators (HHMs) operate under strict physical and safety-critical constraints due to their large size, high power, and complex nonlinear dynamics. Ensuring that both joint-level and end-effector trajectories remain compliant with actuator capabilities, such as force, velocity, and position limits, is essential for safe and reliable operation, yet remains largely underexplored in real-time control frameworks. This paper presents a nonlinear model predictive control (NMPC) framework designed to guarantee constraint satisfaction throughout the full nonlinear dynamics of HHMs, while running at a real-time control frequency of 1 kHz. The proposed method combines a multiple-shooting strategy with real-time sensor feedback, and is supported by a robust low-level controller based on virtual decomposition control (VDC) for precise joint tracking. Experimental validation on a full-scale hydraulic manipulator shows that the NMPC framework not only enforces actuator constraints at the joint level, but also ensures constraint-compliant motion in Cartesian space for the end-effector. These results demonstrate the method's capability to deliver high-accuracy trajectory tracking while strictly respecting safety-critical limits, setting a new benchmark for real-time control in large-scale hydraulic systems.

Problem

• Ensuring trajectory compliance with actuator constraints in hydraulic manipulators
• Real-time nonlinear model predictive control for full-dynamics constraint satisfaction
• Achieving high-accuracy tracking while respecting safety-critical operational limits

Innovation

• Nonlinear model predictive control for full-dynamics constraint satisfaction
• Multiple-shooting strategy combined with real-time sensor feedback
• Virtual decomposition control for robust low-level joint tracking