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

TL;DR

This work tackles safe and precise trajectory tracking for heavy-duty hydraulic manipulators by formulating a full nonlinear NMPC operating at real-time speed. It employs a multiple-shooting NLP with forward dynamics and Cartesian feedback, integrated with a robust VDC low-level controller to ensure joint and end-effector constraint satisfaction. Real-time execution is achieved through solver warm-starting, a fixed iteration budget, high-frequency sensing, and deterministic data handling, enabling 1 ms update cycles at 1 kHz sampling. Experimental validation on a full-scale HHM demonstrates high-accuracy Cartesian and joint tracking while strictly respecting actuator and workspace limits, establishing a practical benchmark for real-time control of large hydraulic 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.

Figures (8)

• Figure 1: Experimental setup. Angular position $\hbox{\boldmath $\theta$}_{{\scriptsize \hbox{msr}}}$ and velocity $\,\dot{\!\hbox{\boldmath $\theta$}}_{{\scriptsize \hbox{msr}}}$ measurements are streamed at 1 kHz. Our NMPC computes an optimal control $\dot{\!\hbox{\boldmath $\theta$}}^{*}$ every 1 ms to the low-level controller VDC which commands voltage $u$ to the robot's hydraulic valves through the Beckoff system.
• Figure 2: Circumference reference trajectory for hydraulic robot's TCP
• Figure 3: Low-level control system tracking performance with circumference reference.
• Figure 4: Cartesian space radial velocity for Circumference trajectory.
• Figure 5: Spiral reference trajectory with increasing tangential speed of TCP.