INTEGRATION OF PARTICLE SWARM OPTIMIZATION (PSO) TECHNIQUE INTO DC MOTOR CONTROL

Particle Swarm Optimization (PSO), an artificial method to determine the optimal proportional- integral- derivative (PID) controller parameters to be integrated into a brushed DC motor is presented. Particle Swarm Optimization (PSO), developed by Eberhart and Kennedy in 1995 was inspired by swarming patterns occurring in nature such as flocking birds. It was observed that each individual exchanges previous experience, hence knowledge of the "best position" attained by an individual becomes globally known. In the study, the problem of identifying the PID controller parameters is considered as an optimization problem. An attempt has been made to determine the PID parameters employing the PSO technique. This technique is used to improve the step response of a second order system. The step response of the given system is defined in rise time, settling time and peak overshoot. The best parameters to be used for PSO that can optimize the performance of a DC Motor (e.g.: population size, acceleration constant and inertia weight factor) is evaluated. First chapter discusses the types of DC motor available in industry nowadays and the origination of Particle Swarm Optimization technique itself. Next, the following chapter continues with the implementation of DC motor control and the tuning available that has been researched before. The usage of Particle Swarm Optimization technique is briefly explained which comprises the 6-steps of selection process. For this study, the software used is MATLAB/Simulink, where the implementation of the chosen DC motor model is represented and Particle Swarm Optimization is integrated into the PID controller of the motor, to observe the performance of chosen parameters. The results of PID controller tuning and also the results for the implementation ofPSO based PID controller is presented on the Result & Discussion chapter. Comparison then is made and discussed to see whether the results are as expected. Lastly, recommendation and conclusion pertaining to the completion of this project is presented

INTEGRATION OF PARTICLE SWARM OPTIMIZATION (PSO) TECHNIQUE INTO DC MOTOR CONTROL

Particle Swarm Optimization (PSO), an artificial method to determine the optimal proportional- integral- derivative (PID) controller parameters to be integrated into a brushed DC motor is presented. Particle Swarm Optimization (PSO), developed by Eberhart and Kennedy in 1995 was inspired by swarming patterns occurring in nature such as flocking birds. It was observed that each individual exchanges previous experience, hence knowledge of the "best position" attained by an individual becomes globally known. In the study, the problem of identifying the PID controller parameters is considered as an optimization problem. An attempt has been made to determine the PID parameters employing the PSO technique. This technique is used to improve the step response of a second order system. The step response of the given system is defined in rise time, settling time and peak overshoot. The best parameters to be used for PSO that can optimize the performance of a DC Motor (e.g.: population size, acceleration constant and inertia weight factor) is evaluated. First chapter discusses the types of DC motor available in industry nowadays and the origination of Particle Swarm Optimization technique itself. Next, the following chapter continues with the implementation of DC motor control and the tuning available that has been researched before. The usage of Particle Swarm Optimization technique is briefly explained which comprises the 6-steps of selection process. For this study, the software used is MATLAB/Simulink, where the implementation of the chosen DC motor model is represented and Particle Swarm Optimization is integrated into the PID controller of the motor, to observe the performance of chosen parameters. The results of PID controller tuning and also the results for the implementation ofPSO based PID controller is presented on the Result & Discussion chapter. Comparison then is made and discussed to see whether the results are as expected. Lastly, recommendation and conclusion pertaining to the completion of this project is presented

Nonlinear system identification and control using state transition algorithm

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testified that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence rate and more stable performance.Comment: 20 pages, 18 figure

Comparative Studies on Decentralized Multiloop PID Controller Design Using Evolutionary Algorithms

Decentralized PID controllers have been designed in this paper for simultaneous tracking of individual process variables in multivariable systems under step reference input. The controller design framework takes into account the minimization of a weighted sum of Integral of Time multiplied Squared Error (ITSE) and Integral of Squared Controller Output (ISCO) so as to balance the overall tracking errors for the process variables and required variation in the corresponding manipulated variables. Decentralized PID gains are tuned using three popular Evolutionary Algorithms (EAs) viz. Genetic Algorithm (GA), Evolutionary Strategy (ES) and Cultural Algorithm (CA). Credible simulation comparisons have been reported for four benchmark 2x2 multivariable processes.Comment: 6 pages, 9 figure

Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

Centralized wide area damping controller for power system oscillation problems

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, three different centralized control designs that vary on complexity are presented to damp inter-area oscillations in large power systems. All the controls are based on phasor measurements. The first two proposed architectures use simple proportional gains that consider availability of measurements from different areas of the system and fulfill different optimization functions. The third controller is based on a more sophisticated Linear Quadratic Gaussian approach which requires access to the state space model of the system under investigation. The novelty of the proposed scheme resides in designing a single control to command the most influence group of machines in the system. To illustrate the effectiveness of the proposed algorithms, simulations results in the IEEE New England model are presented

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Optimal Control of DC Motors Using PSO Algorithm for Tuning PID Controller

The DC motors are widely used in the mechanisms that require control of speed. Different speed can be obtained by changing the field voltage and the armature voltage. The classic PID controllers are widely used in industrial process for speed control. But they aren’t suitable for high performance cases, because of the low robustness of PID controller. So many researchers have been studying various new control techniques in order to improve the system performance and tuning PID controllers. This paper presents particle swarm optimization (PSO) method for determining the optimal PID controller parameters to find the optimal parameters of DC Motor speed control system. The DC Motor system drive is modeled in MATLAB/SIMULINK and PSO algorithm is implemented using MATLAB toolbox. The results obtained through simulation show that the proposed controller can perform an efficient search for the optimal PID controller. Simulation results show performance improvement in time domain specifications for a step response (no overshoot, minimal rise time, steady state error = 0)