1. Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming
- Author
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Jun Yi, Jianyang Shi, Wen He, Gang Yin, and Wei Zhou
- Subjects
0209 industrial biotechnology ,Adaptive dynamic programming (ADP) ,General Computer Science ,input constraints ,Computer science ,020208 electrical & electronic engineering ,General Engineering ,Process (computing) ,Hamilton–Jacobi–Bellman equation ,aluminum electrolysis ,02 engineering and technology ,Function (mathematics) ,Optimal control ,Dynamic programming ,optimal control ,020901 industrial engineering & automation ,Recurrent neural network ,Control theory ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Robust control ,Actuator ,lcsh:TK1-9971 - Abstract
Optimal control of aluminum electrolysis production process (AEPP) has long been a challenging industrial issue due to its inherent difficulty in establishing an accurate dynamic model. In this paper, a novel robust optimal control algorithm based on adaptive dynamic programming (ADP) is proposed for the AEPP, where the system subjects to input constraints. First, to establish an accurate dynamic model for the AEPP system, recursive neural network (RNN) is employed to reconstruct the system dynamic using the input-output production data. To ensure input constraints are not to exceed the bound of the actuator, the optimal control problem of the AEPP is formulated under a new nonquadratic form performance index function. Then, considering the perturbation of the AEPP, the robust control problem is effectively converted to the constrained optimal control problem via system transformation. Furthermore, a single critic network framework is developed to obtain the approximate solution of the Hamilton-Jacobi-Bellman (HJB) equation. Finally, the proposed ADP controller is applied to the AEPP system to validate the effectiveness and performance.
- Published
- 2020
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