Abstract
The industrial systems has became more complex, because of the rapid development of industrial production. Large time-delay, time variation, and highly nonlinear have placed higher requirements on industrial control systems. Smith predicting controller is a controller specifically used for time-delay systems. It estimates the dynamic characteristics of the system’s basic disturbances in advance, and designs an estimator for compensation control, which can effectively improve the system response. But Smith predicting controller depends too much on the mathematical model of the controlled object. This paper proposed a control system based on Smith-PIDNN, which combines Smith controller with neural network PID (PIDNN), uses the neural network’s ability to simulate non-linear systems and self-learning ability, and adjusts control parameters online. And it was simulated by MATLAB software, the effectiveness of this combination method was proved.
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