Smart Systems for Industrial Applications. Группа авторов

Smart Systems for Industrial Applications - Группа авторов


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2.15 (b) Control action for reference value 1,500.

Graph depicts the system output for reference value 1,500.

      Figure 2.15 (c) System output for reference value 1,500.

Distance Kp Ki Kd Settling time
Error 500 10 0.5 5 NIL
500 0.22192 1.32339 0.12735 11 seconds
1,000 0.93028 0.31099 0.75421 8 seconds
1,500 0. 38281 0.19672 0.24252 9 seconds

       2.6.2.4 Analysis Report

A photograph of hardware setup of position servo system. Schematic illustration of a CRO output waveform.

      CRO output consist of average output voltage of piston based on displacement.

Schematic illustration of CRO output waveform of 500 reference error. Schematic illustration of CRO output waveform of 500 reference.

      2.7.1 Reference = 500

      When the reference value is set as 500 and the Kp, Ki, and Kd values are taken by execution of iterations in GA, then the error is minimized and the displacement is settled at the reference value. The Kp, Ki, and Kd values obtained by GA are 0.221923828125,1.323396901967211, and 0.12735267270242523.

Schematic illustration of CRO output waveform of 1,500 reference.

      Figure 2.19 (b) CRO output waveform of 1,500 reference.

      2.7.2 Reference = 1,500

      When the reference value is set as 1,500 and the Kp, Ki, and Kd values are taken by execution of iterations in GA, then the error is minimized and the displacement is settled at the reference value. The Kp, Ki, and Kd values obtained by GA are 0. 38281, 0.19672, and 0.24252. Figure 2.19 b shows the average output waveform for reference value of 1500 with GA.

      Resembling the fractional-order dynamics of gas flow, the pneumatic system also has similar dynamics. Based on the analysis from the literature survey, it has been found that the fractional-order controllers provide enhanced control for the systems that possess fractional-order dynamics. Hence, in this work, FOPID controllers are used to control pneumatic position servo system. One of the challenging issues while applying FOPID controller is the tuning of parameters. In this paper, MMGA method based on Pareto rank is proposed for tuning FOPID parameters. It is evident from the results that FOPID is exceeding IPID in terms of greater accuracy and less energy consumption. It is worthwhile to point out that the dynamic behavior of FOPID controllers are superior to PID controllers. The efficacy of the proposed method is also exhibited by comparing its performance with six methods proposed in the literature. The pneumatic position control system using FOPID gives the best performance than IPID controller. GA is used to find the optimized values of FOPID controller. This paper discussed about the implementation of optimized FOPID controller using GA to find the maximum fitness solution. Simulation results in MATLAB and hardware results from PIC microcontroller validated the proposed algorithm thereby showing the superior performance of FOPID over IPID controller in a pneumatic position servo system.

      1. Ren, H.-P., Wang, X., Fan, J.-T., Kaynak, O., Adaptive backstepping Control of a Pneumatic System with unknown Model Parameters and Control Direction. IEEE Access, 7, 64471–64482, 2019.

      2. Salim, S.S.N., Rahmat, M.F., Faudzi, M., Ismail, Z.H., Sunar, N., Position control of pneumatic actuator using self-regulation nonlinear PID. Math. Probl. Eng., 2014, Article ID 957041, 12, 181–195, 2014.


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