On-line set-point optimization for intelligent supervisory control and improvement of Q-learning convergence
| Autor: | Il Yong Kang, Song Ho Kim, Chung Il Hyon, Kwang Rim Song |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
0209 industrial biotechnology
Computer science Applied Mathematics 020208 electrical & electronic engineering Stability (learning theory) Process (computing) Q-learning 02 engineering and technology Computer Science Applications 020901 industrial engineering & automation Rate of convergence Supervisory control Control and Systems Engineering Control theory Convergence (routing) 0202 electrical engineering electronic engineering information engineering Process control Electrical and Electronic Engineering Layer (object-oriented design) |
| Zdroj: | Control Engineering Practice. 114:104859 |
| ISSN: | 0967-0661 |
| Popis: | This paper proposes a design method of the Q-learning based intelligent supervisory control system (ISCS) for optimal operation of the three step kiln process and a new practical method for improvement of Q-learning convergence. First, the Q-learning based intelligent supervisory control system with two layer-structures is designed to find the on-line optimal set-points of control loops for the kiln process. Next, C4.5 is used to extract automatically the operational experience rules of the human operator from the historical data in the lower layer (i.e., process control layer) and the Q-function value is initialized by using the extracted rules in order to determine the optimal initial point of Q-learning in the higher layer (i.e., supervisory control layer). Hence, the convergence rate of Q-learning is extremely accelerated, so that the hierarchical ISCS can replace the human operator in the kiln process in which trial-and-error operation is not allowed. Through simulations and experiments, Q-learning convergence and the stability of the process operation have been evaluated sufficiently under the variable conditions of the states. |
| Databáze: | OpenAIRE |
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