Reinforcement Learning based Design of Linear Fixed Structure Controllers
| Autor: | Johan U. Backstrom, Michael G. Forbes, Philip D. Loewen, R. Bhushan Gopaluni, Gregory E. Stewart, Nathan P. Lawrence |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning 0209 industrial biotechnology Artificial neural network Computer science 020208 electrical & electronic engineering Stability (learning theory) PID controller Systems and Control (eess.SY) 02 engineering and technology Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) Random search Step response 020901 industrial engineering & automation Function approximation Optimization and Control (math.OC) Control and Systems Engineering Control theory FOS: Mathematics FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Reinforcement learning Mathematics - Optimization and Control |
| Zdroj: | IFAC-PapersOnLine. 53:230-235 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2020.12.127 |
| Popis: | 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. IFAC World Congress 2020 |
| Databáze: | OpenAIRE |
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