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.
Highlights
T HE production process of aluminum electrolytic industry is a strongly coupled and dynamic nonlinear process
2) This paper extends the work of [4] and [14] to develop an optimal controller for aluminum electrolysis production process (AEPP) system with input constraints
In this paper, a novel optimal control method based on Adaptive dynamic programming (ADP) was developed for AEPP system with control constraints
Summary
T HE production process of aluminum electrolytic industry is a strongly coupled and dynamic nonlinear process. Adaptive dynamic programming (ADP) known as a typical data-driven control method is proposed to approximately solve nonlinear optimal control problems [5,6,7]. In [14], a novel ADP method was proposed based on adaptive reinforcement learning for unknown nonlinear systems with input constraints. In [16], a data-driven robust approximate optimal tracking control scheme was proposed, where an unknown nonlinear system model was reconstructed and an approximate optimal tracking controller using the ADP method was designed. The reaction process is affected by the following factors: the temperature of the electrolytic cell, the voltage of the direct current, the concentration of alumina, the distance between the anode and the surface of. The liquid aluminum, the molecular ratio between sodium fluoride and aluminum fluoride, etc
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