IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive Critic Learning Techniques for Engine Torque and Air–Fuel Ratio Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Neural Networks
A self-learning call admission control scheme for CDMA cellular networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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In this paper, a neural network (NN)-based adaptive dynamic programming (ADP) algorithm is employed to solve the optimal temperature control problem in the water-gas shift (WGS) process. Since the WGS process has characteristics of nonlinearity, multi-input, time-delay and strong dynamic coupling, it is very difficult to establish a precise model and achieve optimal temperature control using traditional control methods. We develop an NN model of the conversion furnace using data gathered from the WGS process, and then establish an NN controller based on dual heuristic dynamic programming (DHP) to optimize the temperature control in the WGS. Simulation results demonstrate the effectiveness of the neuro-controller.