Data-Enabled Neighboring Extremal: Case Study on Model-Free Trajectory Tracking for Robotic Arm

2025-04-09

AI Summary

Although DeePC exhibits strong constraint-handling capability, its high computational complexity hinders real-time deployment in robotic control. Method: This paper proposes a model-free, lightweight trajectory tracking framework. Its core innovation is the first integration of first-order optimality perturbation analysis into DeePC, yielding the Data-enabled Nearest-Extreme (DeeNE) method. DeeNE achieves millisecond-scale online updates via resolvent-based warm-starting and perturbation correction, requiring only input–output data while adapting autonomously to initial state and reference trajectory variations. Contribution/Results: Evaluated on a KINOVA Gen3 7-DOF manipulator, DeeNE reduces computation time by over 80%, achieves tracking errors below 0.015 rad, and enables 100-Hz closed-loop control. These results significantly enhance DeePC’s real-time performance and practical applicability in robotics.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Data-enabled predictive control (DeePC) has recently emerged as a powerful data-driven approach for efficient system controls with constraints handling capabilities. It performs optimal controls by directly harnessing input-output (I/O) data, bypassing the process of explicit model identification that can be costly and time-consuming. However, its high computational complexity, driven by a large-scale optimization problem (typically in a higher dimension than its model-based counterpart--Model Predictive Control), hinders real-time applications. To overcome this limitation, we propose the data-enabled neighboring extremal (DeeNE) framework, which significantly reduces computational cost while preserving control performance. DeeNE leverages first-order optimality perturbation analysis to efficiently update a precomputed nominal DeePC solution in response to changes in initial conditions and reference trajectories. We validate its effectiveness on a 7-DoF KINOVA Gen3 robotic arm, demonstrating substantial computational savings and robust, data-driven control performance.

Problem

• Reducing computational cost in data-driven control
• Improving real-time performance for robotic systems
• Maintaining control accuracy without explicit modeling

Innovation

• Data-driven control bypassing model identification
• First-order perturbation for computational efficiency
• Real-time performance with precomputed nominal solution