scholarly journals Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 54 ◽  
Author(s):  
Juan Templos-Santos ◽  
Omar Aguilar-Mejia ◽  
Edgar Peralta-Sanchez ◽  
Raul Sosa-Cortez
Keyword(s):  
Search Algorithm ◽  
Permanent Magnet Synchronous Motor ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Parameter Tuning ◽  
Cuckoo Search Algorithm ◽  
Flower Pollination Algorithm ◽  
Speed Regulation ◽  
Pi Controllers ◽  
Nature Inspired Algorithms

This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.

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.

Multiloop FOPID Controller Design for TITO Process Using Evolutionary Algorithm

2019 ◽  
Vol 8 (3) ◽  
pp. 117-130 ◽  
Author(s):  
Lakshmanaprabu S.K. ◽  
Najumnissa Jamal D. ◽  
Sabura Banu U.
Keyword(s):  
Evolutionary Algorithm ◽  
Pid Controller ◽  
Controller Design ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Internal Model Control ◽  
Model Control ◽  
Fractional Order Pid

In this article, the tuning of multiloop Fractional Order PID (FOPID) controller is designed for Two Input Two Output (TITO) processes using an evolutionary algorithm such as the Genetic algorithm (GA), the Cuckoo Search algorithm (CS) and the Bat Algorithm (BA). The control parameters of FOPID are obtained using GA, CS, and BA for minimizing the integral error criteria. The main objective of this article is to compare the performance of the GA, CS, and BA for the multiloop FOPID controller problem. The integer order internal model control based PID (IMC-PID) controller is designed using the GA and the performance of the IMC-PID controller is compared with the FOPID controller scheme. The simulation results confirm that BA offers optimal controller parameter with a minimum value of IAE, ISE, ITAE with faster settling time.

A Survey of Computational Intelligence Algorithms and Their Applications

2020 ◽  
pp. 1886-1929
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Computational Intelligence ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Evolutionary Strategy ◽  
General Process ◽  
Fireworks Algorithm ◽  
Starting Point ◽  
New Algorithms

This chapter subscribes in the framework of an analytical study about the computational intelligence algorithms. These algorithms are numerous and can be classified in two great families: evolutionary algorithms (genetic algorithms, genetic programming, evolutionary strategy, differential evolutionary, paddy field algorithm) and swarm optimization algorithms (particle swarm optimisation PSO, ant colony optimization (ACO), bacteria foraging optimisation, wolf colony algorithm, fireworks algorithm, bat algorithm, cockroaches colony algorithm, social spiders algorithm, cuckoo search algorithm, wasp swarm optimisation, mosquito optimisation algorithm). We have detailed each algorithm following a structured organization (the origin of the algorithm, the inspiration source, the summary, and the general process). This paper is the fruit of many years of research in the form of synthesis which groups the contributions proposed by various researchers in this field. It can be the starting point for the designing and modelling new algorithms or improving existing algorithms.

Hybridizing cuckoo search algorithm with bat algorithm for global numerical optimization

2018 ◽  
Vol 75 (5) ◽  
pp. 2395-2422 ◽  
Author(s):  
Mohammad Shehab ◽  
Ahamad Tajudin Khader ◽  
Makhlouf Laouchedi ◽  
Osama Ahmad Alomari
Keyword(s):  
Numerical Optimization ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Global Numerical Optimization

A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 50-62 ◽  
Author(s):  
Ramadan Babers ◽  
Aboul Ella Hassanien
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Krill Herd ◽  
Artificial Fish Swarm ◽  
Lion Optimization Algorithm

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

Parameter identification of a reduced nonlinear model for an activated sludge process based on cuckoo search algorithm

2019 ◽  
Vol 41 (12) ◽  
pp. 3352-3363
Author(s):  
Taoufik Ladhari ◽  
Intissar Khoja ◽  
Faouzi Msahli ◽  
Anis Sakly
Keyword(s):  
Activated Sludge ◽  
Parameter Identification ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Identification Problem ◽  
Cuckoo Search Algorithm ◽  
Activated Sludge Process ◽  
Modeling And Control ◽  
Parameter Identification Problem ◽  
Nature Inspired Algorithms

Parameter identification plays a key role in systems’ modeling and control. This paper deals with a parameter identification problem for an activated sludge process used in wastewater treatment. The considered model is a nonlinear one inspired from the well-known ASM1. Nature-inspired algorithms have gained significant attention over the last years as useful means to solve parameter identification problem. The proposed approach in this paper is the cuckoo search algorithm based on both the fascinating brood parasitic behavior and the lévy flights. The advantages of this method are its simplicity and robustness, but it requires a good tuning of its parameters to have the best results. The comparison of the simulation results with the Nelder-Mead method, genetic algorithm, and particle swarm optimization proves the capability of this method to identify the model’s parameters with high precision.

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