This paper applies a synchronously approximate dynamic programming (ADP) scheme to solve the Nash controls of the dual-driven load system (DDLS) with different motor properties based on game theory. First, a neural network (NN) is applied to approximate the dual-driven servo unknown system model. Because the properties of two motors are different, they have different performance indexes. Another NN is used to approximate performance index function of each motor. In order to minimize the performance index, the Hamilton function is constructed to solve the approximate optimal controls of the load system. Based on parameter error information, an adaptive law is designed to estimate NN weights. Finally, the practical DDLS is simulated to demonstrate that the optimal control inputs can be studied by ADP algorithm.
Servo system Approximate dynamic programming Nash equilibrium Multi-input system Neural networks
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The work was supported by National Natural Science Foundation of China (No. 61433003 and No. 61273150).
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