In this paper, a new controller structure called the radius basis function (RBF) neural network auto-tuning PID controller with Kalman filter is presented to manipulate a linearized magnetic levitation system. The proposed RBF neural network auto-tuning PID controller with Kalman filter makes use of Kalman filter to deal with the uncertainties and noises induced by the process of linearization of magnetic levitation system as well as the noise problems induced by the position feedback sensor device. To validate the proposed new design structure, the MATLAB simulations under different types of noise problems are presented. Furthermore, results confirm that the output transient response and steady-state error of magnetic levitation system by the proposed controller with Kalman filter can be improved and assured while the results of auto-tuning PID controller are inclined to be unstable.
It is the purpose of this paper to introduce the advantages of Grey predictor controllers. We adopt grey prediction to obtain simple and effective estimated values, and, with the aid of first-order low-pass α Filter, greatly improve the accuracy subsequently used in the prediction for system response. The result will be in turn used to predict error and furthermore automatically adjust the parametrical values of PID controller, and accordingly will be able to deal with the possible variation of system responses at the very first stage. It can not only actively promote the responses efficiency of transient response, but also passively prevent disturbance. As a matter of fact, the highest demand of "plug in and play" can be met without any need to adjust the parameter. This paper will give a detailed specification of the system structure, the design, and the concept, as well as prove the modulation function of Grey predictor controllers in unit step response by means of Matlab program simulation and mathematical argumentation. In transient response, it will effectively fasten rising time, shorten settling time, and oppress overshoot; meanwhile, in steady state response, it is able to reduce steady state error to zero and achieve what traditional PID cannot perform.Index Terms-Grey Predictor Controllers, Low-pass α Filter, PID Controller, Plug in and Play.
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