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Thursday, July 30, 2020 | History

2 edition of Utilization of genetic algorithm in on-line tuning of fluid power servos found in the catalog.

Utilization of genetic algorithm in on-line tuning of fluid power servos

Jarkko Halme

Utilization of genetic algorithm in on-line tuning of fluid power servos

by Jarkko Halme

  • 88 Want to read
  • 25 Currently reading

Published by Lappeenranta University of Technology in Lappeenranta .
Written in English

    Subjects:
  • Fluid power technology -- Computer simulation.,
  • Genetic algorithms.,
  • Hydraulic control -- Data processing.

  • Edition Notes

    StatementJarkko Halme.
    SeriesResearch papers / Lappeenranta University of Technology -- 60, Research papers (Lappeenrannan teknillinen korkeakoulu) -- 60.
    Classifications
    LC ClassificationsTJ843 .H35 1997
    The Physical Object
    Pagination91 p. :
    Number of Pages91
    ID Numbers
    Open LibraryOL19477237M
    ISBN 109517641729

      The algorithm takes 93 milliseconds on detection the motor condition on a GHz Intel Core 2 processor. Table 4 shows the FPGA resource utilization, generated by Altera Quartus II software, in the percentage of each memory unit for negative : Ilhan Aydin, Mehmet Karakose, Ebru Karakose, Erhan Akin. As the result of this study the algorithm of tuning controllers and the procedure of determining the system’s performance in dependence of maximal stability value is proposed. Index Terms — the iterative algorithm, the maximal stability degree method, tuning of controllers REFERENCES [1] R. K. Dorf, R. X. Bishop.

    In this work, we shown and implement an on-line auto-tuning algorithm for PID controllers based on evolution-ary algorithms (EvoTune in the following). The EvoTune algorithm solve a constrained non-convex optimization statement for an identified FOPDT process to adjust the parameters of the PID controller. The remain of this work. fuzzy self tuning of PID controller. V. ADAPTATION OF PID CONTROLLER USING GENETIC ALGORITHM Genetic Algorithms (GA.s) are a stochastic global search method [] that mimics the process of natural evolution. It is one of the methods used for optimization. States in the at the University of Michigan. The.

    Laatra Yousfi, Amel Bouchemha, Mohcene Bechouat and Abdelhani Boukrouche ()., Vector control of induction machine using PI controller optimized by genetic algorithms., 16th International Power Electronics and Motion Control Conference and Exposition, , Antalya, Turkey, TPM v specification uses the SHA-1 hashing algorithm. More recent TPM versions (v+) call for SHA A desired characteristic of a cryptographic hash algorithm is that (for all practical purposes) the hash result (referred to as a hash digest or a hash) of any two modules will produce the same hash value only if the modules are identical.


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Utilization of genetic algorithm in on-line tuning of fluid power servos by Jarkko Halme Download PDF EPUB FB2

Only a segment of the Slovak power system comprising relevant blocks of the nuclear power plant Mochovce (Slovak republic) has been considered (Fig. The stabilizing signal π contains three measured signals Pe, If, fvt, as shown in Fig. String parameters decoding Power system model simulation Objective evaluation Genetic algorithm solutionFile Size: KB.

The genetic algorithm (GA) is used for cost optimization, and a software algorithm has been developed. As a result of the realized optimization, besides.

This paper investigates the tuning of power system stabilizers (PSS) using genetic algorithms (GA). A digital simulation of a linearized model of a single-machine infinite bus power system at some operating point is used in conjunction with the genetic algorithm optimization by: For instance, numerical experiments on the performance of RMT-based stochastic generators on other metaheuristics could be carried out -e.g.

particle swarm optimization (Mousavi et al., Electro-hydraulic servo system was widely used in industrial application for its good performance, but the dynamic behavior is highly nonlinear, structure uncertainty, and very difficult to control by conventional method.

PID control was applied into this system control and the parameters were tuned by improved genetic by: 2. system parameters and then trained neural network find the optimal PID tuning through the inputs ‘r’ and ‘To’ 4. GENETIC ALGORITHM Genetic algorithms are inspired by Darwin’s theory that is survival of the fittest and it is an evolutionary algorithm for optimization of a mathematical procedure or Size: KB.

Heuristic search techniques like genetic algorithm overcome the difficulties and limitations encountered by the conventional approaches for system identification and controller tuning.

Genetic algorithm [10], [11] is a general-purpose optimization algorithm based on the mechanics of natural selection and by: to tuning the PI parameters. Tuning PI parameters using different optimal algorithms such as the simulated annealing, genetic algorithm, and particle swarm optimization algorithm.

In this paper a scheduling PI tuning paramet ers using genetic algorithm strategy for an induction motor speed control is proposed. Genetic Algorithm & Ziegler-Nichols Tuning Criteria. Tuning methods for PID controllers are very important for the process industries.

Traditional methods such as Ziegler-Nichols method often do not provide adequate tuning. Genetic Algorithm (GA) as an intelligent approach has also been widely used to tune the parameters of Size: KB. different structures, power system utilities still prefer the conventional lead-lag PSS structure.

The reasons behind that might be the ease of on-line tuning and the lack of assurance of the stability related to some adaptive or variable structure techniques. Unlike other optimization techniques, Genetic Algorithm.

Abstract. The control design, based on self-adaptive PID with genetic algorithms (GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer (IMDRDWM) and with multivariable nonlinear interaction of microwave and by: 8.

inference algorithm, the computer makes ia query table off-line in advance and stores it in the memory of DSP. In a practical control, the control value can be obtained according to the query table, and tuning the K, K, and K, on-line.

The design of the fuzzy controller is, base on the genetic algorithm. Fig. a PID-based feedback controller for a buck converter using the ITAE controller performance index.

The controller parameters are optimized to ensure that a reasonable transient response can be achieved whilst retaining stable operation. Experimental results demonstrate the versatility of the on-line tuning methodology. optimal tuning of TCSC controller in a SMIB power system was presented where the MATLAB/SIMULINK based model was developed and genetic algorithm (GA) was employed to design the TCSC controller.

However, the model only takes into account the generator main field winding and the synchronous machine was represented by model For more.

"Genetic Algorithm Applied to State Feedback Control Design", IEEE/PES [8] Nuraddeen Magaji, Mukhtar F. Hamza, Ado Dan-Isa" Comparison of GA and LQR Tuning of Static Var Compensator for Damping Oscillations", Jan AUTHOR BIOGRAPHY Jamal M.

Ahmed. Lecturer in Electronic Engineering Department. Genetic Algorithm Genetic Algorithms (GA) are search procedures inspired by the laws of natural selection and genetics. They can be viewed as a general-purpose optimization method and have been successfully applied to search, optimization and machine learning tasks.

GA has the ability to solve difficult, multi dimensional problems with. Industrial processes are subjected to variation in parameters and parameter perturbations, which when significant makes the system unstable.

In order to overcome this problem of parameter variation the PI controllers are widely used in industrial plants because it is simple and robust.

However there is a problem in tuning PI parameters. So the control engineers are on look for. Optimal Fuzzy Self-Tuning of PI Controller Using Genetic Algorithm for Induction Motor Speed Control Ismail K.

Bouserhane *,**, Abdeldjebar Hazzab *, Abdelkrim Boucheta *, Benyounes Mazari **, and Rahli Mostefa ** * University Center of Bechar, B.P BecharAlgeria ** Laboratoire de Développement et des Entraînements Electriques LDEE, University Cited by: Keywords: Dynamic encoding algorithm for searches, gain tuning, multiobjective optimization, PID control.

INTRODUCTION Proportional-integral-derivative (PID) controllers are widely used in the process control industry owing to their relatively simple structures and robust performances.

This popularity has stimulated. hybrid speed control of induction motor using pi and fuzzy controller Proposed Three-phase current measurement using only PID controller can drive the induction motor system more effectively with.

speed control of induction motor using pid controller Compensating signal is added to PID by using LabVIEW. and genetic algorithm to design and tuning of PID controller to get an output with better dynamic and static performance.

The application of fuzzy logic to the PID controller imparts it the ability of tuning itself automatically in an on-line .Application of self-tuning PID control with genetic algorithm to a semi-batch polystyrene reactor Ayla Altinten 1*, which controller parameters are adjusted on-line according to the dynamic behaviour and signals of the self-tuning PID controller with GA was used for by: 2.Tuning of PI Speed Controller in DTC of Induction Motor Based on Genetic Algorithms and Fuzzy Logic Schemes Shady M.

Gadoue, D. Giaouris and J. W. Finch University of Newcastle upon Tyne Power Electronics, Drives and Machines Group, School of Electrical, Electronic and Computer Engineering Merz Court NE1 7RU Newcastle upon Tyne, UKFile Size: KB.