Master Thesis Genetic Algorithm with all your Master Thesis Genetic Algorithm essay writing needs. We understand you need help now with quick essay paper writing and we are at your service, Master Thesis Genetic Algorithm delivering you % custom essays. We’re not just any essay website Master Thesis Optimization of the operation of a Distribution Network with Distributed Generation using Genetic Algorithm A Thesis submitted by Sergi Cabr e Ramos for the degree of MSc in Electrical Engineering in the Universitat Polit ecnica de Catalunya January Supervised by: Andreas Sumper M aster d’Enginyeria en Energia especialitat Thesis statements are some of the mandatory aspects of academic writing Master Thesis Genetic Algorithm that you`ll be required to master in college. However, most students find it challenging as they have no idea of how to go about these Read more>> On-time delivery
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Download Download PDF Full PDF Package Download Full PDF Package This Paper. A short summary of this paper. This thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree. I am heartily thankful to my supervisor, Dr. Hazlina binti Selamat and co-supervisor, Dr. Hairi bin Zamzuri for their support and guidance from the initial to the final stage of my research, master thesis genetic algorithm.
Only Allah alone can repay their kindness. Also thanks to the folks at the CAIRO for working together and being fun with. They have kept me in good spirits throughout my study.
I would like to express my deepest thanks and appreciations to my late father, my mother, family and friends for master thesis genetic algorithm encouragement, cooperation and support along my journey to complete this project. May Allah bless them all. ABSTRACT Proportional-Integral-Derivative PID controller is one of the most popular controllers applied in industries.
However, despite the simplicity in its structure, the PID parameter tuning for high-order, unstable and complex plants is difficult. When dealing with such plants, empirical tuning methods become ineffective while analytical approaches require tedious mathematical works. As a result, the control community shifts its attention to stochastic optimisation techniques that require less interaction from the controller designers.
Although these approaches manage to optimise the PID parameters, the combination of multiple objectives in one single objective function is not straightforward. This work presents the development of a multi-objective genetic algorithm to optimise the PID controller parameters for master thesis genetic algorithm complex master thesis genetic algorithm unstable system.
A new genetic algorithm, called the Global Criterion Genetic Algorithm GCGA has been proposed in this work and is compared with the state-of-the-art Non-dominated Sorting Genetic Algorithm NSGA-II in several standard test problems, master thesis genetic algorithm. The results show the GCGA has convergence property with an average of The proposed algorithm has master thesis genetic algorithm applied and implemented on a rotary inverted pendulum, master thesis genetic algorithm, which is a nonlinear and under-actuated plant, suitable for representing a complex and unstable high-order system, to test its effectiveness.
The set of pareto solutions for PID parameters generated by the GCGA has good control performances settling time, overshoot and integrated time absolute errors with closed-loop stable property. ABSTRAK Pengawal Perkadaran-Kamiran-Pembezaan PID adalah salah satu daripada pengawal-pengawal yang banyak digunakan di industri, master thesis genetic algorithm.
Walau bagaimanapun, selain memiliki struktur yang ringkas, penalaan parameter-parameter PID untuk sistem yang tidak stabil, kompleks dan bertertib tinggi menjadi sukar untuk disempurnakan.
Apabila berhadapan dengan sistem sedemikian, kaedah-kaedah empirikal menjadi tidak berkesan dan kaedah-kaedah analitik memerlukan jalan kerja matematik yang rumit. Kesannya, komuniti kawalan cuba mengalihkan perhatian kepada kaedah-kaedah stokastik yang kurang memerlukan interaksi daripada jurutera. Walaupun kaedah-kaedah ini berjaya menalakan parameter- parameter PID, penggabungan pelbagai objektif dalam satu fungsi objektif masih tidak begitu jelas.
Tesis ini memperincikan pembangunan satu algoritma evolusi pelbagai objektif untuk mengoptimumkan parameter-parameter PID bagi satu sistem yang kompleks dan tidak stabil. Algoritma yang dicadangkan iaitu Algoritma Genetik Berkreteria Global GCGA akan dibandingkan dengan algoritma yang popular, Algoritma Genetik Master thesis genetic algorithm Tak-didominasi NSGA-II dalam beberapa permasalahan. Keputusan menunjukkan GCGA mempunyai purata Algoritma cadangan telah diaplikasikan ke atas bandul songsang berputar yang merupakan satu sistem tidak linear yang sesuai untuk mewakili sistem yang kompleks, bertertib tinggi dan tidak stabil.
Set penyelesaian-penyelesaian pareto yang diperolehi melalui GCGA mempunyai sifat-sifat kawalan masa pengenapan, kelajakan dan ralat masa mutlak bersepadu yang baik dengan mematuhi sifat kestabilan sistem tertutup. TITLE PAGE 2. TITLE PAGE 1. of objectives m1 - Mass of the arm m2 - Mass of the pendulum MOEA - Multi-objective Evolutionary Algorithm N - No. Master thesis genetic algorithm controller or control law describes the algorithm or the signal processing employed by the control processor to generate the actuator signal from the sensors and command signals it receives Chen, master thesis genetic algorithm, Figure 1.
The controller receives command signal and after that compares it with the present output measured by the sensor. The controller then send the appropriate signal to the actuator in order to ensure the plant produces the same output as the command signal. The adjustment of the controller parameters or sometimes called controller tuning is a critical element in the controller design process. These complicated controllers however are developed in such way so it will produce optimum control signal Polyak and Tempo, The controller design only has to decide on the value of the weights associated with the various signals in the system.
On the other hand, master thesis genetic algorithm, this research aims to find an approach to optimize the performances of the PID controllers. Despite the simplicity in its structure and being the most popular master thesis genetic algorithm of controller employed, master thesis genetic algorithm, the level of difficulty in the PID controller tuning mainly depends on the plant behaviours Åström and Hägglund, Therefore this research used a rotational inverted pendulum RIP to demonstrate the difficulties in tuning the PID control parameters for a very nonlinear and under- actuated system.
The under-actuated two degree of freedoms, one actuator property of RIP also demonstrates the tuning example of two PID controllers simultaneously, master thesis genetic algorithm. This condition will add to the difficulties in PID tuning. Referring to the above conditions, the existing PID tuning methods are not capable to tune the combination of PID parameters when facing such plants.
Thus this research tries to propose an algorithm that automatically gives the user the optimized PID parameters for the objectives like steady-state error, settling time and overshoot in the system. Moreover, a study from Van Overschee et al. These situations implies the tuning PID controllers are the vexing problems to the tuning operators which maybe the tuning rules available are not well compatible for their tuning problems in industry.
Hence this research tries to provide an alternative approach for tuning PID controllers. The developed algorithm in this research will automatically provide the designers with the optimized PID parameters with less rules of tuning.
To develop a master thesis genetic algorithm optimization algorithm based on evolutionary techniques for tuning PID controller parameters. To compare the proposed algorithm with the well known multi-objective GA. To apply the optimized PID controller to an under-actuated plant, rotational inverted pendulum RIP in the simulation and real plant. Developing a multi-objective optimization algorithm to optimally tune the PID controller performances like settling time, steady state errors and overshoot using multi-objective genetic algorithms MOGAs approach.
Analysing the optimization algorithm using several test problems borrowed from literature and comparing to a well-known algorithm.
Applying the results of optimized PID controller simulation to the real plant in order to validate the algorithm in the real implementation. Introduction of a variant of MOGAs called Master thesis genetic algorithm Criterion Genetic Algorithm GCGA.
Optimization of PID controller tuning using GCGA. Simulation and experimental validation of optimized PID controller tuning. Chapter 2 provides a discussion of the fundamentals of PID controller and a number of popular tuning methods for PID controller. Both conventional and alternative approaches are covered in this chapter. Chapter 3 discusses the literature review for evolutionary algorithm EAmaster thesis genetic algorithm, the application of EA in the controller tuning problem and the multi-objective genetic algorithms MOGAs.
Previous work done by the researchers in the area of MOGAs will be used as the basis for the proposed algorithm in Chapter 4. Moreover, the modelling of the rotational inverted pendulum through derivation from the equations of motion is presented. Chapter 5 analyzes the GCGA through several popular test problems and compares its performances with the well known Non-dominated Sorting Genetic Algorithm II NSGA-II. This chapter also shows the optimization work of the PID controller using GCGA in the simulation and real RIP.
Chapter 6 concludes the thesis and suggests several further investigations of the optimization work. Both deterministic and stochastic tuning methods are discussed in this chapter. PID controllers are widely used as the chosen controller strategy due to their design simplicity and its reliable operation.
A simple PID structure consists of three terms which are Kp, Ki and Kd referring to proportional, integration and derivative gains respectively. In a PID controller structure, the parallel architecture like Figure 2. Figure 2. The tuning approaches can be divided into two categories which are the conventional and the alternative approaches. The conventional approaches include the empirical methods and the analytical methods which widely used by control designers, master thesis genetic algorithm.
The alternative approaches are limited to methods that employ the stochastic process in the tuning rules. Stochastic process refers to one whose behaviour is non-deterministic, where any of its sub-system determined by the process of deterministic action master thesis genetic algorithm a random behaviour. The details of the stochastic techniques are described in the subchapter 2.
The most popular empirical PID tuning method is the classical Ziegler and Nichols method where the PID parameters are experimentally tuned in order to get the best outcome. To perform this method, the gains KI and KD are set to zero while the gain KP is increasing until it reaches the ultimate gain value, master thesis genetic algorithm, Ku. Ku is determined when the output response is oscillating with constant amplitude which is Ku at the ultimate period, Tu. Then the gains of PID controller are given in Table 2, master thesis genetic algorithm.
PID Gains Equation Kp 0. Since the objective of this research is master thesis genetic algorithm find a solution to tune a highly unstable plant like an under- actuated system, the Ziegler and Nichol method may not master thesis genetic algorithm suitable.
The same limitations went to another popular PID tuning approach, Cohen and Coon method. This method required the users to model the plant as first order plus dead time process. The steps to perform the Cohen and Coon as the following i.
Mod-01 Lec-38 Genetic Algorithms
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STUDY ON GENETIC ALGORITHM IMPROVEMENT AND APPLICATION by Yao Zhou A Thesis Submitted to the Faculty of the WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Master of Science in Manufacturing Engineering by Yao Zhou May APPROVED: Dr. Yiming (Kevin) Rong, Major Advisor The main idea of this Master Thesis is to check the applicability of Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) to risk management. A portfolio containing multiple assets reduces the overall risk by diversifying away the idiosyncratic risk. It is therefore good to consider as many assets as possible, with the limitations View blogger.com from CENG at Cavite State University Main Campus (Don Severino de las Alas) Indang. Using genetic algorithms as a
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