Stochastic optimal controller design for medium access constrained networked control systems with unknown dynamics
| Autor: | Luigi Glielmo, B. Subathra, Sreram Balasubramaniyan, Seshadhri Srinivasan, Valentina Emilia Balas, Hamed Kebraei |
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| Přispěvatelé: | Balasubramaniyan, Sreram, Srinivasan, Seshadhri, Kebraei, Hamed, Subathra, B., Balas, Valentina Emilia, Glielmo, Luigi |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science Q-learning 02 engineering and technology Networked control systems (NCSs) 020901 industrial engineering & automation Software Control theory Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Markov Decision Process (MDP) Controller design business.industry Stochastic optimal controller Control engineering Networked control system Human-Computer Interaction Constraint Dynamics (music) Control system 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Medium acce business |
| Zdroj: | Intelligent Decision Technologies. 11:253-264 |
| ISSN: | 1875-8843 1872-4981 |
| DOI: | 10.3233/idt-170293 |
| Popis: | This paper proposes a stochastic optimal controller for networked control systems (NCS) with unknown dynamics and medium access constraints. The medium access constraint of NCS is modelled as a Markov Decision Process (MDP) that switches modes depending the channel access to the actuators. We then show that using the MDP assumption, the NCS with medium access constraint can be modelled as a Markovian jump linear system. Then a stochastic optimal controller is proposed that minimizes the quadratic cost function using Q-learning algorithm. The resulting control algorithm simultaneously optimizes the quadratic cost function and also allocates the network bandwidth judiciously by designing a scheduler. Two compensation strategies transmit zero and zero-order hold for control inputs that fail to get an access to channel are studied. The proposed controller and scheduler are illustrated using experiments on networks and simulations on an industrial four-tank system. The advantage of the proposed approach is that the optimal controller and scheduler can be designed forward-in-time for NCS with unknown dynamics. This is a departure from traditional dynamic programming based approaches that assume complete knowledge of the NCS dynamics and network constraints beforehand to solve the optimal controller problem backward-in-time. |
| Databáze: | OpenAIRE |
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