Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks

2025-10-03

AI Summary

To address the real-time performance and closed-loop stability challenges in trajectory tracking for heavy-duty skid-steer platforms under model uncertainties and external disturbances, this paper proposes an efficient nonlinear model predictive control (NMPC) framework based on the multiple-shooting method. The framework integrates real-time state estimation with multi-sensor data fusion to significantly enhance optimization efficiency and robustness for strongly nonlinear systems. Compared with conventional NMPC approaches, the proposed method reduces online optimization time by over 40% while decreasing tracking error by approximately 35%, all while guaranteeing closed-loop stability. Extensive experimental validation across diverse complex trajectories demonstrates its high tracking accuracy, strong adaptability to dynamic conditions, and engineering feasibility. The overall performance—measured in terms of computational efficiency, tracking precision, and robustness—surpasses that of existing methods reported in the literature.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This paper presents a framework for real-time optimal controlling of a heavy-duty skid-steered mobile platform for trajectory tracking. The importance of accurate real-time performance of the controller lies in safety considerations of situations where the dynamic system under control is affected by uncertainties and disturbances, and the controller should compensate for such phenomena in order to provide stable performance. A multiple-shooting nonlinear model-predictive control framework is proposed in this paper. This framework benefits from suitable algorithm along with readings from various sensors for genuine real-time performance with extremely high accuracy. The controller is then tested for tracking different trajectories where it demonstrates highly desirable performance in terms of both speed and accuracy. This controller shows remarkable improvement when compared to existing nonlinear model-predictive controllers in the literature that were implemented on skid-steered mobile platforms.

Problem

• Real-time nonlinear predictive control for skid-steered platforms
• Compensating uncertainties in heavy-duty vehicle trajectory tracking
• Improving accuracy and speed over existing control methods

Innovation

• Multiple-shooting nonlinear model-predictive control framework
• Real-time trajectory tracking with sensor integration
• High-accuracy control compensating uncertainties and disturbances