Performance Evaluation of Ant Lion Optimizer–Based PID Controller for Speed Control of PMSM

2019 ◽  
Vol 49 (2) ◽  
pp. 20180892
Author(s):  
Navaneethan Soundirarrajan ◽  
Kanthalakshmi Srinivasan
Keyword(s):  
Performance Evaluation ◽  
Pid Controller ◽  
Speed Control ◽  
Ant Lion Optimizer ◽  
Ant Lion

scholarly journals Performance Evaluation of PID Controller for an Automobile Cruise Control System using Ant Lion Optimizer

2017 ◽  
Vol 21 (5) ◽  
pp. 347-361 ◽  
Author(s):  
Rosy Pradhan ◽  
Santosh Kumar Majhi ◽  
Jatin Kumar Pradhan ◽  
Bibhuti Bhusan Pati
Keyword(s):  
Performance Evaluation ◽  
Control System ◽  
Pid Controller ◽  
Cruise Control ◽  
Ant Lion Optimizer ◽  
Cruise Control System ◽  
Ant Lion

scholarly journals Optimal fractional order PID controller design using Ant Lion Optimizer

2020 ◽  
Vol 11 (2) ◽  
pp. 281-291 ◽  
Author(s):  
Rosy Pradhan ◽  
Santosh Kumar Majhi ◽  
Jatin Kumar Pradhan ◽  
Bibhuti Bhusan Pati
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Controller Design ◽  
Ant Lion Optimizer ◽  
Pid Controller Design ◽  
Ant Lion ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

Design of PID controller for automatic voltage regulator system using Ant Lion Optimizer

2018 ◽  
Vol 15 (3) ◽  
pp. 373-387 ◽  
Author(s):  
Rosy Pradhan ◽  
Santosh Kumar Majhi ◽  
Bibhuti Bhusan Pati
Keyword(s):  
Pid Controller ◽  
Parametric Uncertainty ◽  
Frequency Domain Analysis ◽  
Voltage Regulator ◽  
Content Type ◽  
Ant Lion Optimizer ◽  
Automatic Voltage Regulator ◽  
Avr System ◽  
Nature Inspired Algorithm ◽  
Ant Lion

Purpose Now days, various techniques are used for controlling the plants. New ideas are evolving day by day to get better-quality control for various industrial processes to produce high-quality products. Currently, the focus of this research is being emphasized on application of nature-inspired algorithms in control systems. The purpose of this paper is to apply a nature-inspired algorithm called Ant Lion Optimizer (ALO) for the design of proportional-integrator-derivative (PID) controller for an automatic voltage regulator (AVR) system. Design/methodology/approach For the design of the PID controller, the ALO algorithm is considered as a designing tool for obtaining the optimal values of the controller parameter. All the simulations are carried out in Simulink/MATLAB environment. A comparative study is carried out with some modern nature-inspired algorithm to describe the advantages of this tuning method. Findings The proposed method has superiority value in transient and frequency domain analysis than the other published heuristic optimization algorithms. The presented approach has almost no variation in transient response when varying time constants of the system parameter, such as exciter, generator, amplifier and sensor from −50 per cent to +50 per cent. In addition, the close loop system is robust against any disturbances such as input–output disturbances and parametric uncertainty, as the sensitivity values are nearly equal to one. Originality/value The proposed method presents the design and performance analysis of proportional integral derivate (PID) controller for an AVR system using the recently proposed ALO.

scholarly journals An Effective Technique for Tuning the Time Delay System with PID Controller-Ant Lion Optimizer Algorithm with ANN Technique

2020 ◽  
Vol 9 (3) ◽  
pp. 2421-2429
Keyword(s):  
Time Delay ◽  
Pid Controller ◽  
Gravitational Search Algorithm ◽  
Delay System ◽  
Delay Systems ◽  
Fast Time ◽  
Time Delay System ◽  
Ant Lion Optimizer ◽  
Controller Performance ◽  
Ant Lion

Nowadays, the PID controller is very common controller as well as very important controller in industrial utilizations. In the paper, proposed an ALO algorithm and ANN controller is utilized to enhance PID controller performance and control the tuning of TDS. TDS stands for Time delay system. ALO stands for Ant lion optimizer and ANN stands for Artificial neural network. In terms of parameters controlling, the time delay system is controlled and for different delay events low overshoot and fast time settling is reached. The novelty of the presented method is enhancing the PID controller performance by optimizing the PID gain parameters and controlling the highorder TDS. The performance of time delay system can be enhanced through decreasing error, tracking, time delay & error, rapid and exactly for their corresponding reference values. For parameter controlling of time delay system along optimal values, can be significantly enhanced the performance. To analyze the characteristics of the presented method, the various time delay systems are analyzed. The input and gain parameters were utilized to evaluate the objective function from tuning system. Based on proposed method, the optimal result is achieved and evaluated the increae time, settling time, overshoot as well as steady state error in TDS. The suggested controller is executed in MATLAB/Simulink work site and the presented technique performance examined through performance indexes and time domain specifications are evaluated using presented method compared to previous methods like ABC (Artificial Bee colony) algorithm, GSA (Gravitational Search Algorithm) ,FA (Firefly Algorithm).

Preventive maintenance scheduling using analysis of variance-based ant lion optimizer

2018 ◽  
Vol 15 (2) ◽  
pp. 254-272 ◽  
Author(s):  
Umamaheswari Elango ◽  
Ganesan Sivarajan ◽  
Abirami Manoharan ◽  
Subramanian Srikrishna
Keyword(s):  
Analysis Of Variance ◽  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Statistical Tests ◽  
Population Based ◽  
Maintenance Scheduling ◽  
Content Type ◽  
Ant Lion Optimizer ◽  
Ant Lion ◽  
Selection Of

Purpose Generator maintenance scheduling (GMS) is an essential task for electric power utilities as the periodical maintenance activity enhances the lifetime and also ensures the reliable and continuous operation of generating units. Though numerous meta-heuristic algorithms have been reported for the GMS solution, enhancing the existing techniques or developing new optimization procedure is still an interesting research task. The meta-heuristic algorithms are population based and the selection of their algorithmic parameters influences the quality of the solution. This paper aims to propose statistical tests guided meta-heuristic algorithm for solving the GMS problems. Design/methodology/approach The intricacy characteristics of the GMS problem in power systems necessitate an efficient and robust optimization tool. Though several meta-heuristic algorithms have been applied to solve the chosen power system operational problem, tuning of their control parameters is a protracting process. To prevail over the previously mentioned drawback, the modern meta-heuristic algorithm, namely, ant lion optimizer (ALO), is chosen as the optimization tool for solving the GMS problem. Findings The meta-heuristic algorithms are population based and require proper selection of algorithmic parameters. In this work, the ANOVA (analysis of variance) tool is proposed for selecting the most feasible decisive parameters in algorithm domain, and the statistical tests-based validation of solution quality is described. The parametric and non-parametric statistical tests are also performed to validate the selection of ALO against the various competing algorithms. The numerical and statistical results confirm that ALO is a promising tool for solving the GMS problems. Originality/value As a first attempt, ALO is applied to solve the GMS problem. Moreover, the ANOVA-based parameter selection is proposed and the statistical tests such as Wilcoxon signed rank and one-way ANOVA are conducted to validate the applicability of the intended optimization tool. The contribution of the paper can be summarized in two folds: the ANOVA-based ALO for GMS applications and statistical tests-based performance evaluation of intended algorithm.

Sign in / Sign up

Export Citation Format

Share Document