Abstract
According to the time delay in industrial control objects, the PID neural network control method and Smith predictor compensation principle are combined to form the PIDNN-Smith control algorithm. Namely, in Smith predictor compensation control system, the PIDNN as the controller, using the PIDNN neural network on-line self-learning function to tune weight value, make the implicit layers of proportion, integral and differential neurons to achieve the best combination, thus overcome disadvantages of the conventional PID algorithm that does not adapt to the control of large delay system and conventional Smith algorithm depended too much on model precision of the defect. Simulation results show strong robustness and good control quality of this algorithm.
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