A novel modified Lévy flight distribution algorithm to tune proportional, integral, derivative and acceleration controller on buck converter system

Abstract

In this paper, an optimal proportional, integral, derivative and acceleration (PIDA) controller design based on Bode’s ideal reference model and a novel modified Lévy flight distribution (mLFD) algorithm is proposed for buck converter system. The modification of the original Lévy flight distribution (LFD) algorithm was achieved by improving exploration and exploitation capabilities of the algorithm through incorporation of opposition-based learning mechanism and hybridizing with simulated annealing algorithm, respectively. The modified algorithm was used to tune the gains of the PIDA controller in order to operate a buck converter system that is mimicking the response of the Bode’s ideal reference model. Both the proposed novel algorithm and its PIDA controller design implementation for buck converter were confirmed through various tests and extensive analyses of statistical and non-parametric tests, convergence profile, transient and frequency responses, disturbance rejection, robustness, and time delay response. The comparative results with the state-of-the-art algorithms of manta ray foraging optimization, arithmetic optimization algorithm and the original LFD algorithm have shown that the proposed mLFD algorithm performs better than the compared ones in all assessments even when different well-known performance indices are used. The proposed Bode’s ideal reference model-based optimal PIDA control design with novel mLFD algorithm was also compared with other design approaches using the same buck converter system available in the literature. The proposed mLFD algorithm-based design approach has also shown greater effectiveness compared to other available methods, as well.

Determination of Different Types of Controller Parameters Using Metaheuristic Optimization Algorithms for Buck Converter Systems

“…12. In addition, integral absolute error (IAE) [39], integral square error (ISE) [40], integral time absolute error (ITAE) [41] and integral time squared error (ITSE) [42] performance indices are used to evaluate the performance of the algorithms in more detail. The formulas of the indices are given in the equations ( 26)- (29).…”

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

“…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. 16 Considering the above-mentioned facts, it is obvious that an appropriate tuning method must be employed in order to benefit from the advantages of FOPID controllers. In that sense, the metaheuristic algorithms 17 are excellent candidates to perform the tuning task 18,19 since their performances have been demonstrated to be unbeatable in terms of improving the ability of the controllers that have been employed in magnetic ball suspension systems.…”

“…The latter fact has already been demonstrated for different applications 11–15 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 16 …”

Improved model-free fractional-order intelligent proportional–integral fractional-order sliding mode control with anti-windup compensator

“…In addition to the difficulty of system modeling, input saturation is common, as it is practically impossible for an actuator to deliver infinite energy to real systems. However, different PID controllers (Izci, 2021; Izci et al, 2021), which are widely used in industry, suffer from a significant loss of performance due to the saturation of the actuator. Generally, the presence of this saturation can degrade system performance and even lead to a situation of closed-loop system instability.…”

“…Furthermore, to increase the controller’s performance, the fractional order (FO) may be incorporated into different controllers. As a matter of fact, FO controllers have been used in combination with well-known control methods like PID control (Izci et al, 2021; Shah and Agashe, 2016), adaptive control (Aguila-Camacho and Duarte-Mermoud, 2013; Ladaci and Charef, 2006), optimal control (Idiri et al, 2016), nonlinear synergetic control (Ardjal et al, 2019a, 2019b; Mehiri et al, 2018), and sliding mode control (SMC; Ardjal et al,019c; Djeghali et al,, 2021; Ebrahimkhani, 2016; Xiong et al, 2017).…”