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

TL;DR

This paper introduces a real-time nonlinear model predictive control framework for heavy-duty hydraulic manipulators, ensuring constraint satisfaction and high-accuracy trajectory tracking at 1 kHz.

Contribution

It presents a novel NMPC approach that guarantees constraint compliance for full nonlinear dynamics of HHMs in real-time, supported by sensor feedback and a low-level VDC controller.

Findings

Enforces actuator constraints during trajectory tracking

Ensures Cartesian space constraint compliance for end-effector

Operates reliably at 1 kHz control frequency

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.