Optimal Derivative Feedback Control for an Active Magnetic Levitation System: An Experimental Study on Data-Driven Approaches

2026-02-06

AI Summary

This study addresses the challenge of achieving high-performance control in active magnetic levitation systems when an accurate mathematical model is unavailable. The authors propose and compare two data-driven optimal differential feedback control strategies: a direct model-free reinforcement learning approach and an indirect method based on system identification. An innovative “epoch-loop” policy iteration mechanism is introduced to enhance exploration diversity through multiple rounds of data collection, effectively mitigating bias issues inherent in model-free learning. Experimental results demonstrate that both approaches outperform a nominal model-based controller. Notably, the direct model-free method—enabled by multi-epoch data collection—exhibits significantly superior stability and control performance compared to the indirect approach, which relies on a single batch of data combined with Dynamic Mode Decomposition with control (DMDc) and Prediction Error Method (PEM) for system identification.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Active Learning
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents the design and implementation of data-driven optimal derivative feedback controllers for an active magnetic levitation system. A direct, model-free control design method based on the reinforcement learning framework is compared with an indirect optimal control design derived from a numerically identified mathematical model of the system. For the direct model-free approach, a policy iteration procedure is proposed, which adds an iteration layer called the epoch loop to gather multiple sets of process data, providing a more diverse dataset and helping reduce learning biases. This direct control design method is evaluated against a comparable optimal control solution designed from a plant model obtained through the combined Dynamic Mode Decomposition with Control (DMDc) and Prediction Error Minimization (PEM) system identification. Results show that while both controllers can stabilize and improve the performance of the magnetic levitation system when compared to controllers designed from a nominal model, the direct model-free approach consistently outperforms the indirect solution when multiple epochs are allowed. The iterative refinement of the optimal control law over the epoch loop provides the direct approach a clear advantage over the indirect method, which relies on a single set of system data to determine the identified model and control.

Problem

• magnetic levitation
• optimal control
• data-driven control
• model-free control
• derivative feedback

Innovation

• model-free control
• reinforcement learning
• policy iteration
• magnetic levitation
• data-driven control