In this paper, we present the automatic PID tuning procedure based on the Method of Moments and AMIGO tuning rules. The advantage of the Method of Moments is that the time constant and transport delay are estimated at the areas rather than on the individual points. This results in high resistance to the measurement noises. The sensitivity to measurement noises is a serious problem in other autotuning methods. The second advantage of this method is that it approximates plant during identification process to first order model with time delay. We combined the Method of Moments with the AMIGO tuning rules and implemented this combination as a stand-alone autotuning procedure in Siemens S7-1200 PLC controller. Next, we compared this method with two built-in PID autotuning procedures which were available in Siemens S7-1200 PLC controller. The procedure was tested for three types of plant models: with lagdominated, balanced, and delay-dominated dynamics. We simulated the plants on a PC in Matlab R2013a. The connection between the PC and PLC was maintained through a National Instruments data acquisition board, NI PCI-6229. We conducted tests for step change in the set point, trajectory tracking, and load disturbances. To assess control quality, we used IAE index. We limited our research to PI algorithm. The results prove that proposed method was better than two built-in tuning methods provided by Siemens, oscillating between a few and even a dozen percent in most cases. The proposed method is universal and can be implemented in any PLC controller.
Support Vector Machines (SVM) are widely used in many fields of science, including system identification. The selection of feature vector plays a crucial role in SVM-based model building process. In this paper, we investigate the influence of the selection of feature vector on model’s quality. We have built an SVM model with a non-linear ARX (NARX) structure. The modelled system had a SISO structure, i.e. one input signal and one output signal. The output signal was temperature, which was controlled by a Peltier module. The supply voltage of the Peltier module was the input signal. The system had a non-linear characteristic. We have evaluated the model’s quality by the fit index. The classical feature selection of SVM with NARX structure comes down to a choice of the length of the regressor vector. For SISO models, this vector is determined by two parameters: nu and ny. These parameters determine the number of past samples of input and output signals of the system used to form the vector of regressors. In the present research we have tested two methods of building the vector of regressors, one classic and one using custom regressors. The results show that the vector of regressors obtained by the classical method can be shortened while maintaining the acceptable quality of the model. By using custom regressors, the feature vector of SVM can be reduced, which means also the reduction in calculation time.
More and more control systems are based on industry microprocessors like PLC controllers (Programmable Logic Controller). The most commonly used control algorithm is PID (Proportional-Integral-Derivative) algorithm. Autotuning procedure is not available in every PLC. These controllers are typically used in cooperation with HMI (Human Machine Interface) devices. In the study two procedures of autotuning of the PID controller were implemented in the HMI device: step method and relay method. Six tuning rules for step methods and one for relay method were chosen. The autotuning procedures on simulated controlled object and PLC controller without build-in autotuning were tested. The object of control was first order system plus time delay.
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