Design and Robust Performance Analysis of Low-Order Approximation of Fractional PID Controller Based on an IABC Algorithm for an Automatic Voltage Regulator System

Abstract

In this paper, a low-order approximation (LOA) of fractional order PID (FOPID) for an automatic voltage regulator (AVR) based on the modified artificial bee colony (ABC) is proposed. The improved artificial bee colony (IABC) high-order approximation (HOA)-based fractional order PID (IABC/HOA-FOPID) controller, which is distinguished by a significant order approximation and by an integer order transfer function, requires the use of a large number of parameters. To improve the AVR system’s performance in terms of transient and frequency response analysis, the memory capacity of the IABC/HOA-FOPID controller was lowered so that it could fit better in the corrective loop. The new robust controller is named the improved artificial bee colony (IABC) low-order approximation (LOA)-based fractional order PID (IABC/LOA-FOPID). The performance of the proposed IABC/LOA-FOPID controller was compared not only to the original ABC algorithm-tuned PID controller, but also to other controllers tuned by state-of-the-art meta-heuristic algorithms such as the improved whale optimization algorithm (IWOA), particle swarm optimization (PSO), cuckoo search (CS), many optimizing liaisons (MOL), genetic algorithm (GA), local unimodal sampling (LUS), and the tree seed algorithm (TSA). Step response, root locus, frequency response, robustness test, and disturbance rejection abilities are all compared. The simulation results and comparisons with the proposed IABC/LOA-FOPID controller and other existing controllers clearly show that the proposed IABC/LOA-FOPID controller outperforms the optimal PID controllers found by other algorithms in all the aforementioned performance tests.

Optimizing AVR system performance via a novel cascaded RPIDD2-FOPI controller and QWGBO approach

“…In addition to the comparative assessment against the algorithms used in the above analyses, further comparisons were made with recently reported optimization techniques in the literature (See Table 11 ). This techniques include hybrid atom search particle swarm optimization (h-ASPSO) based PID controller [ 46 ], improved marine predators algorithm (MP-SEDA)-tuned FOPID controller [ 47 ], modified artificial bee colony (IABC) based LOA-FOPID [ 48 ], equilibrium optimizer (EO) based TI λ DND 2 N 2 [ 23 ], whale optimization algorithm (WOA) based PIDA [ 49 ], symbiotic organism search (SOS) algorithm-based PID-F controller [ 50 ], mayfly optimization algorithm based PI λ1 I λ2 D μ1 D μ2 controller [ 25 ], Levy flight improved Runge-Kutta optimizer (L-RUN) based PIDD 2 controller with master/slave approach [ 51 ], particle swarm optimization based 2DOF-PI controller with amplifier feedback [ 52 ], modified artificial rabbits optimizer (m-ARO) based FOPIDD 2 controller [ 53 ], genetic algorithm (GA) based fuzzy PID controller [ 54 ], sine-cosine algorithm (SCA) based FOPID controller with fractional filter [ 55 ], imperialist competitive algorithm (ICA) based gray PID controller [ 56 ], Rao algorithm based multi‐term FOPID controller [ 57 ], whale optimization algorithm (WOA) based 2DOF-FOPI [ 58 ], chaotic yellow saddle goatfish algorithm (C-YSGA) based FOPID controller [ 59 ] and crow search algorithm (CSA) based FOPI controller [ 31 ]. The results indicate that the QWGBO algorithm outperforms several state-of-the-art optimization methods, demonstrating its effectiveness in AVR system control.…”

Novel Speed Sensorless DTC Design for a Five-Phase Induction Motor with an Intelligent Fractional Order Controller Based-MRAS Estimator

“…In this paper, an integral of the time-weighted absolute error (ITAE) has been chosen as an objective function to evaluate the performance of classical PI controllers. There are a large number of published investigations proving the effectiveness of the error function (ITAE) in optimisation algorithms (Idir et al, 2022). Eq.…”

Optimal tuning of the novel voltage regulation controller considering the real model of the automatic voltage regulation system

“…[ 12 ]. Seeking ways to improve the transient response of the AVR system with a PID controller, the investigation of the research led to the application of a fractional-order PID controller (FOPID) instead of a classic PID controller [ [13] , [14] , [15] , [16] ]. Such a controller is more complicated for hardware realization, but can significantly improve the dynamic performances of the AVR system.…”

“…[ 13 , 15 , 24 , 31 ] for optimal tuning of the controller parameters. Furthermore, in the available literature, various metaheuristic algorithms have been applied: tree seed algorithm [ 3 ], equilibrium optimizer (EO) [ 4 ], improved kidney inspired (IKA) [ 5 ], whale optimization algorithm (WOA) [ 6 , 29 ], nonlinear threshold accepting (NLTA) [ 7 ], cuckoo search (CS) [ 10 ], ant colony optimization-Nelder Mead (ACO-NM) [ 11 ], improved artificial bee colony (IABC) [ 14 ], chaotic BWO [ 16 ], chaotic yellow saddle goatfish (C-YSGA) [ 18 ], equilibrium optimizer-evaporation rate water cycle algorithm (EO-ERWCA) [ 23 ], coyote optimization algorithm (COA) [ 28 ], simulated annealing-manta ray foraging optimizer (SA-MRFO) [ 32 ], and improved Levy flight distribution (I-LFD) algorithm. Fractional order differ-integrations in the fuzzy logic controller in Ref.…”

“…The amplifier, exciter, and sensor are presented with first-order transfer functions in Refs. [ [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] ]. The generator is also presented with a first-order transfer function in Refs.…”