An Enhanced Tuning of PID Controller via Hybrid Stochastic Fractal Search Algorithm for Control of DC Motor

2022 ◽  
pp. 185-194
Author(s):  
Rita Saini ◽  
Girish Parmar ◽  
Rajeev Gupta ◽  
Afzal Sikander
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Dc Motor ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

Design and application of an optimally tuned PID controller for DC motor speed regulation via a novel hybrid Lévy flight distribution and Nelder–Mead algorithm

2021 ◽  
pp. 014233122110196
Author(s):  
Davut Izci
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Absolute Error ◽  
Dc Motor ◽  
Cuckoo Search Algorithm ◽  
Lévy Flight ◽  
Motor Speed ◽  
Levy Flight ◽  
Speed Regulation

This paper deals with the design of an optimally performed proportional–integral–derivative (PID) controller utilized for speed control of a direct current (DC) motor. To do so, a novel hybrid algorithm was proposed which employs a recent metaheuristic approach, named Lévy flight distribution (LFD) algorithm, and a simplex search method known as Nelder–Mead (NM) algorithm. The proposed algorithm (LFDNM) combines both LFD and NM algorithms in such a way that the good explorative behaviour of LFD and excellent local search capability of NM help to form a novel hybridized version that is well balanced in terms of exploration and exploitation. The promise of the proposed structure was observed through employment of a DC motor with PID controller. Optimum values for PID gains were obtained with the aid of an integral of time multiplied absolute error objective function. To verify the effectiveness of the proposed algorithm, comparative simulations were carried out using cuckoo search algorithm, genetic algorithm and original LFD algorithm. The system behaviour was assessed through analysing the results for statistical and non-parametric tests, transient and frequency responses, robustness, load disturbance, energy and maximum control signals. The respective evaluations showed better performance of the proposed approach. In addition, the better performance of the proposed approach was also demonstrated through experimental verification. Further evaluation to demonstrate better capability was performed by comparing the LFDNM-based PID controller with other state-of-the-art algorithms-based PID controllers with the same system parameters, which have also confirmed the superiority of the proposed approach.

scholarly journals Adaptive Tuning of PID Controller using Crow Search Algorithm for DC motor

2021 ◽  
Vol 1076 (1) ◽  
pp. 012001
Author(s):  
Amer Alkrwy ◽  
Arkan Ahmed Hussein ◽  
Thamir H. Atyia ◽  
Muntadher Khamees
Keyword(s):  
Pid Controller ◽  
Search Algorithm ◽  
Dc Motor ◽  
Adaptive Tuning

scholarly journals Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic Using Robust Multiobjective Optimization and Stochastic Fractal Search

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Fran Sérgio Lobato ◽  
Gustavo Barbosa Libotte ◽  
Gustavo Mendes Platt
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Real Data ◽  
The Novel ◽  
Estimation Process ◽  
Design Variables ◽  
Novel Coronavirus ◽  
Stochastic Fractal Search Algorithm ◽  
Stochastic Fractal Search

Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables, and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead, and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multiobjective optimization problem is formulated, considering the minimization of uncertainties associated with the estimation process and the maximization of the robustness parameter. To solve this problem, the Multiobjective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.

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