Optimal Control for Aluminum Electrolysis Process Using Adaptive Dynamic Programming

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. INDEX TERMS Adaptive dynamic programming (ADP), optimal control, input constraints, aluminum electrolysis.

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“…Recently, ADP-based methods are utilized to design optimal controllers for continuoustime [16,17] and discrete-time [18,19] nonlinear systems with input/output constraints [20,21], external disturbances [22,23], and mismatched interconnections [24,25]. Since the optimal control problems of nonlinear systems are solved gradually, the ADP-based optimal control approaches [26] are applied to various fields [27,28]. Nevertheless, all the aforementioned control methods were developed based on the time-triggered mechanism, which neglected the huge amount of unnecessary computation, communication, and energy cost in a long working time.…”