Tuning of fractional complex‐order direct current motor controller using frequency domain analysis

Abstract

For the speed control of direct current motor, a fractional complex‐order controller (FCOC) is proposed in this paper. The FCOC is an extension of the fractional‐order controller. It is known that fractional‐order controllers exhibit strong robustness to open‐loop gain variations. However, in actual situations, due to external interference, the phase angle and gain of the system may change at the same time. The FCOC proposed in this paper adds an additional parameter, which makes the system show strong robustness when the phase angle and gain change simultaneously. Existing methods for parameter tuning of FCOCs only consider the preset gain crossover frequency and the expected phase margin. This may not allow the system to exhibit good dynamic and stable performance. In this paper, based on the frequency domain analysis, the parameters of the FCOC are tuned by integrating the gain crossover frequency, cutoff frequency, phase margin, and amplitude margin. The optimal solution satisfying a set of constraint equations is obtained by optimization algorithm. Finally, the simulation results are compared with those of the fractional‐order controller and the integer‐order controller. At the same time, the robustness of the system under disturbance is analyzed. The results show that the complex fractional‐order control presented in this paper has better dynamic performance and robustness.

Complex Order PIa+jbDc+jd Controller Design for a Fractional Order DC Motor System

“…The genesis of COPID controller is related to the CRONE controller (third generation) [47,48]. The complex order PID controller structure for the DC motor fractional order model in the current study is given as follows [14,20,49].…”

Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System

“…While researchers have explored various techniques for LFC in microgrid power systems, recent efforts have focused on soft computing methods that have shown promise in enhancing load frequency control in complex energy systems. These methods include neural networks [3], fuzzy logic [4], adaptive neurofuzzy logic control [5], fractional order controller [6]- [9], complex order controller [10]- [13], Grey wolf optimization [14], differential evolution [15], particle swarm optimization [16], ant colony optimization [17], artificial bee colony [18], hybrid optimization [19], imperialist competitive algorithm [20], genetic algorithm [21], [22], teaching-learning based optimization [23]- [26], cohort intelligence optimization [27]- [31]. Researchers have investigated various aspects of complex and fractional order modeling and control in various systems [32]- [45].…”

An examination of complex fractional order physical phenomena in IOPD controller design

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