A multivariable on-line adaptive PID controller using auto-tuning neurons

Abstract In this paper, we present a new PID control technique based on auto-tuning neurons for multivariable systems. The main difference between an auto-tuning neuron and a general neuron is that there are adjustable parameters of the activation function used in an auto-tuning neuron. In this paper, a modified hyperbolic tangent function is used as the activation function of an auto-tuning neuron, which provides two adjustable parameters to flexibly determine the magnitude and the shape of function. We then use such auto-tuning neurons to find gains of the multivariable PID controller, which is tuned on-line according to certain adaptation laws. Finally, two illustrative examples will be used to compare the performance by using our proposed method and other methods.

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