GDL

Neural Network Adaptive Control for X-Y Position Platform with Uncertainty

An improvement neural network adaptive control strategy is put forward for X-Y position platform with uncertainty by the paper. Firstly, dynamics model of X-Y position platform is established. Then, RBF neural network with good learning ability is used to approach non-linear system. The early period control accuracy of the problem is considered by the paper, because good precision in the early period is difficult to be obtained by neural network controller, so PID controller is designed to compensate control. An improvement dynamic optimization adjustment algorithm of network weights is designed to speed up the learning speed. Simulation results show that the control method is more effective to improve the control precision and real-time and has a good application value.

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