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.
The purpose of this project is to design and implement a portable yet high resolution color sensor which can identify the color, and can discern the color difference between a new paint and the old one. The resolution is 1/256 in digit. This device can give the provider a handful tool to get the right color of paint when body job is needed. Also, this device can ensure customers their paint jobs are well carried out with the objective digital readout from the device. This device uses the infrared photodiode pair as the color sensor. One constant current source with feedback loop from one photodiode receiver to ensure a pre-fixed brightness of infrared light is emitted from the transmitter. Another photodiode picks up the reflected signal from surface in a pre-determined distance. The color difference can be seen as the amplitude variation of the reflected signal. This amplitude variation then feeds into A/D converter to quantifilize the color into digits, i.e. 0 to 255 in the resolution of 1/256. A single chip microprocessor takes in the information via its I/O port and compares the data with a built-in color look-up table. Finally, the identified color along with its digitalized brightness readout is shown on a LCD display controlled by the micro-processor to carry out the color scrutiny scheme.
In this paper, we propose to add Grey prediction model GM(1,2) into the self-tuning Neuro-PID controller based on radial basis function (RBF) algorithm to improve the performance of the controller. Initially, the prediction of system output by the simple GM(1,2) model is added to the RBF algorithm as one of the inputs to enhance the performance of RBF neural network system identifier. The output of this GM(1,2)-RBF on-line learning system model is subsequently used to establish a set of updating algorithms for the gains of self-tuning PID controller. The detailed description of the proposed system structure and the design algorithm is given in this paper. The proposed auto-tuning PID controller via GM(1,2)-RBF algorithm is put into tests by Matlab simulations and motor speed control experiments by using LabVIEW. The system responses of self-tuning PID controller based on GM(1,2)-RBF and RBF are compared. Both simulations and motor test results confirm that the proposed self-tuning PID controller based on GM(1,2)-RBF performs better than the one based on RBF.
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