Reinforcement Learning based Design of Linear Fixed Structure Controllers
This is an incremental improvement for control systems engineering, offering a simpler method to tune linear fixed-structure controllers.
The paper tackles the problem of tuning PID controllers by proposing a finite-difference reinforcement learning approach based on random search, which iteratively improves PID gains using the entire closed-loop step response to achieve a desired response without modeling.
Reinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications. The existing methods often utilize function approximation, such as neural networks, to update the controller parameters at each time-step of the underlying process. In this work, we present a simple finite-difference approach, based on random search, to tuning linear fixed-structure controllers. For clarity and simplicity, we focus on PID controllers. Our algorithm operates on the entire closed-loop step response of the system and iteratively improves the PID gains towards a desired closed-loop response. This allows for embedding stability requirements into the reward function without any modeling procedures.