Controller Tuning

Controller tuning, the process of optimizing controller parameters for optimal system performance, is a crucial challenge across diverse engineering domains. Current research emphasizes data-driven approaches, employing Bayesian optimization, reinforcement learning, and neural networks to automate this process, often addressing high-dimensional parameter spaces and real-time constraints. These methods aim to overcome limitations of traditional model-based techniques, particularly in complex or time-varying systems, leading to improved efficiency, robustness, and safety in applications ranging from robotics and autonomous vehicles to industrial process control. The development of more efficient and reliable auto-tuning methods holds significant potential for enhancing the performance and reliability of numerous technological systems.