Performance evaluation of a novel improved slime mould algorithm for direct current motor and automatic voltage regulator systems

Abstract

This study deals with the controlling the speed of a direct current (DC) motor via a fractional order proportional–integral–derivative (FOPID) controller and maintaining the terminal voltage level of an automatic voltage regulator (AVR) via a proportional–integral–derivative plus second order derivative (PIDD2) controller. To adjust the parameters of those controllers, a novel improved slime mould algorithm (ISMA) is proposed. The latter is a novel metaheuristic algorithm developed in this work. The proposed algorithm aims to improve the original SMA in terms of exploration with the aid of a modified opposition-based learning scheme and in terms of exploitation with the aid of the Nelder–Mead simplex search method. A time domain objective function, which includes time response specifications of steady state error and maximum overshoot along with rise and settling times, is used as a performance index to design the FOPID controller-based DC motor system and PIDD2 controller-based AVR system. The performance of the proposed novel approaches for both systems are assessed through time and frequency domain simulations along with statistical tests which show the greater performance of the improved algorithm. Further to this, the efficacy of the proposed approaches for both systems is compared with other available and effective approaches in the literature. The extensive comparative results demonstrate the proposed method to be superior to those state-of-the-art approaches for both DC motor speed and AVR control systems.

A novel modified opposition‐based hunger games search algorithm to design fractional order proportional‐integral‐derivative controller for magnetic ball suspension system

“…Due to utilization of fractional terms, FOPID controllers provide additional degree of freedom which makes them a better option for dynamic systems compared to conventional IOPID controllers. 10 The latter fact has already been demonstrated for different applications [11][12][13][14][15] which confirm a more promising ability of the FOPID controllers. However, it is worth noting that adjusting the parameters of the FOPID controller is a more challenging task despite the advantage of being more flexible for dynamic systems since it requires an approximation method which causes more computational load.…”

“…On the other hand, a fractional counterpart of the IOPID controllers (FOPID) 9 can also be encountered in the literature, which has an increased capability compared to IOPID controllers as the FOPID controllers add more flexibility to the design. Due to utilization of fractional terms, FOPID controllers provide additional degree of freedom which makes them a better option for dynamic systems compared to conventional IOPID controllers 10 . The latter fact has already been demonstrated for different applications 11–15 which confirm a more promising ability of the FOPID controllers.…”

“…The dynamic behavior of the system needs to be modeled in order to construct such a feedback control system. Considering the second law of Newton, the net force that the ball experiences can be given in (10) 47 :…”

Cooperation search algorithm for tuning automatic voltage regulator system

“…AVR is formed from four parts, namely: amplifier, exciter, generator, and sensor. 𝑉 𝑡 Is the synchronous generator terminal voltage [22], [23]. The AVR modeling can be seen in Figure 1.…”

Hybridizing slime mould algorithm with simulated annealing algorithm: a hybridized statistical approach for numerical and engineering design problems

“…Levy flight–slime mould (LF-SMA) algorithm [ 8 ] is clout by the actions of slime mould which is further mixed up with Levy distribution and excelled in obtaining better results in the exploration phase. Improved slime mould algorithm (ISMA) is developed from traditional SMA in two aspects: the exploration phase is aided with a modified opposition-based learning technique as well the phase of exploitative is aided with Nelder–Mead simplex search method to adjust the parameters of the controllers to control the velocity of a DC motor by a fractional-order PID (FOPID) controller as well as requires to maintain the automatic voltage regulator (AVR) at its level of terminal voltage through a PID in addition with second-order derivative (PIDD 2 ) controller [ 9 ]. The combination of slime mould and Harris hawks optimization (HHO-SMA) implemented in [ 10 ] stood better technique to improve slow convergence speed.…”