Neuro-Optimal Tracking Control for Continuous Stirred Tank Reactor With Input Constraints
| Autor: | Haibo He, Jun Yi, Wei Zhou, Taifu Li, Liu Huachao |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science 020208 electrical & electronic engineering Continuous stirred-tank reactor 02 engineering and technology Function (mathematics) Chemical reactor Optimal control Inductor Computer Science Applications Dynamic programming Control and Systems Engineering Control theory 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Actuator Information Systems |
| Zdroj: | IEEE Transactions on Industrial Informatics. 15:4516-4524 |
| ISSN: | 1941-0050 1551-3203 |
| DOI: | 10.1109/tii.2018.2884214 |
| Popis: | This paper proposes a novel data-based optimal control algorithm for continuous stirred tank reactor (CSTR) system based on adaptive dynamic programming (ADP). To overcome the challenge of establishing an accurate mathematical model for the CSTR system, neural networks are employed to reconstruct the dynamics of the CSTR system using the production data of the system. A new nonquadratic form performance index function is provided, where the control input is constrained in order not to exceed the bound of the actuator. Then, the operational optimal control problem of CSTR is formulated. Furthermore, an iterative ADP (IADP) algorithm is developed to obtain the optimal tracking controller for the CSTR system with control constraints. In particular, the convergence analysis of the IADP algorithm is developed. The proposed IADP algorithm is implemented via the dual heuristic dynamic programming structure. Finally, the proposed approach is applied to the real CSTR system to verify the effectiveness and performance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|