Sub-optimal tracking in switched systems with fixed final time and fixed mode sequence using reinforcement learning

Recently, increasing attention has been paid to nuclear power control with the appeals of clean energy and demands of power regulation to integrate into the power grid. However, a nuclear power system is a discrete-time (DT) nonlinear and complicated system, where the parameters entangle with intrinsic states. Furthermore, the need for huge computational ability due to the high-level order property in the nuclear reactor model causes many difficulties in the power control of nuclear industries. In this study, a new scheme of optimal tracking control for DT nonlinear nuclear power systems is provided to accomplish the power control of a 2500-MW pressurized water reactor (PWR) nuclear power plant. The proposed approach based on the value iteration method is a novel algorithm in the human intelligence community, which has a basic actor-critic structure with neural networks (NNs). The new approach has some modifications, where the cost function is redefined by leveraging the higher-order polynomial to substitute neural networks in the entire actor critic architecture. Simulation results of the 2500-MW PWR nuclear power plant are given to demonstrate the effectiveness of the developed method.

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“…. ., π i (ε k ) and V i (ε k ) are obtained by ( 22)- (25). Let α, β, c, and η be constants that satisfy 26) and ( 27) hold uniformly.…”

Section: T F0mentioning

confidence: 99%

“…ere are also ADP techniques [19][20][21] to obtain solutions of optimal tracking problems with various system dynamics, such as partially unknown system models or completely unknown system models. Related optimal tracking control techniques have been applied in many industrial plants in recent years [22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%