Machine Learning Applications in System Identification and Control

Abstract

The proliferation of advanced machine learning techniques has opened up new avenues for system identification and control. This research highlights five principal applications. First, system identification, a process traditionally reliant on mathematical models for predicting system behavior, has seen the incorporation of machine learning models trained on empirical data. Techniques such as neural networks, random forests, and support vector machines facilitate this. Second, Model Predictive Control (MPC) is being enhanced through machine learning by establishing more precise models and optimizing control actions. Reinforcement learning, a machine learning subtype involving environmental interaction, has been applied for deriving optimal control policies. Third, fault detection and diagnostics have benefitted from machine learning's ability to identify system anomalies. Training models on standard system behavior allows them to recognize deviations, indicating potential faults. Fourth, adaptive control, focused on real-time control parameter adjustment as system dynamics change, is being transformed by reinforcement learning which adapts the controller's actions according to the system's behavior. Fifth, machine learning tackles the challenges of non-linear control systems. Techniques like deep learning prove particularly useful, capable of modeling complex, high-dimensional, and non-linear relationships which traditional methodologies struggle with. Despite these advantages, machine learning's application comes with its own set of challenges. It often demands extensive data and computational resources, and the resulting models may lack the interpretability of traditional ones, making system behavior comprehension difficult. Consequently, meticulous and thoughtful application of these techniques is paramount, marking a significant area for future investigation.