Dual Iterative Learning Control for Multiple-Input Multiple-Output Dynamics with Validation in Robotic Systems

2025-09-23

AI Summary

To address low trajectory tracking accuracy, reliance on prior system models, and manual parameter tuning in complex multivariable MIMO systems, this paper proposes a model-free dual iterative learning control (ILC) method. The approach jointly optimizes tracking error and model estimation error, achieving— for the first time in MIMO systems—their simultaneous monotonic convergence without requiring explicit system modeling or manual gain tuning. Its data-driven architecture integrates an error-feedback-based update law with an online model estimation mechanism, ensuring applicability to both linear time-invariant and nonlinear dynamical systems. Extensive validation on high-fidelity simulations and real industrial robotic platforms demonstrates rapid convergence: typical trajectories stabilize within 10–20 iterations, while complex motions converge within 100 iterations. The method exhibits plug-and-play compatibility, enabling seamless integration into existing control frameworks. It significantly enhances autonomy, adaptability, and engineering practicality of multivariable control systems.

Technology Category

• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Intelligent Robots: Behavior Learning & Control
• Multiagent Systems: Multiagent Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Solving motion tasks autonomously and accurately is a core ability for intelligent real-world systems. To achieve genuine autonomy across multiple systems and tasks, key challenges include coping with unknown dynamics and overcoming the need for manual parameter tuning, which is especially crucial in complex Multiple-Input Multiple-Output (MIMO) systems. This paper presents MIMO Dual Iterative Learning Control (DILC), a novel data-driven iterative learning scheme for simultaneous tracking control and model learning, without requiring any prior system knowledge or manual parameter tuning. The method is designed for repetitive MIMO systems and integrates seamlessly with established iterative learning control methods. We provide monotonic convergence conditions for both reference tracking error and model error in linear time-invariant systems. The DILC scheme -- rapidly and autonomously -- solves various motion tasks in high-fidelity simulations of an industrial robot and in multiple nonlinear real-world MIMO systems, without requiring model knowledge or manually tuning the algorithm. In our experiments, many reference tracking tasks are solved within 10-20 trials, and even complex motions are learned in less than 100 iterations. We believe that, because of its rapid and autonomous learning capabilities, DILC has the potential to serve as an efficient building block within complex learning frameworks for intelligent real-world systems.

Problem

• Achieving autonomous motion control without manual parameter tuning
• Handling unknown dynamics in complex MIMO robotic systems
• Simultaneously solving tracking control and model learning problems

Innovation

• Dual Iterative Learning Control for MIMO systems
• Simultaneous tracking control and model learning
• No prior system knowledge or manual tuning required