scholarly journals Application of the variation operator in a genetic algorithm for the synthesis of fuzzy controllers

2021 ◽  
Vol 47 (4) ◽  
pp. 92-100
Author(s):  
I. V. Kulikova
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Fuzzy Controller ◽  
Single Point ◽  
Controller Synthesis ◽  
Mutation Operator ◽  
Mutation Operators ◽  
Variation Operator ◽  
Variation Operators ◽  
Best Parameters

Abstract. Objective. This article studies the problem of increasing the efficiency of fuzzy controller synthesis in a control system using a genetic algorithm. The best parameters of the fuzzy controller are selected using the crossing-over and mutation operators in the genetic algorithm. The operation of the mutation operator can lead to the formation of an incorrect set of parameters, which complicates the procedure for synthesizing a fuzzy controller.Methods. Arrays of parameter sets of membership functions, conclusions, and rule weights that are included in the fuzzy controller are compiled using mathematical simulation. The mechanism of operation of single-point and two-point variation operators in the genetic algorithm is described by the simulation modeling.Results. Mathematical models of single-point and two-point variation operators for the genetic algorithm are proposed. The mechanism for changing the values of elements in the array of a set of parameters of a fuzzy controller with one input and output variable is presented.Conclusion. Replacing the mutation operator with the variation operator eliminates the formation of incorrect sets of parameters of the fuzzy controller in the control system.

scholarly journals Fuzzy logic for control Peltier module

2018 ◽  
Vol 19 (6) ◽  
pp. 704-707
Author(s):  
Emil Sadowski ◽  
Tomasz Marek ◽  
Roman Pniewski ◽  
Rafał Kowalik
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Control Algorithm ◽  
Fuzzy Controller ◽  
Control Process ◽  
Controller Synthesis ◽  
Cell Control ◽  
Control Value ◽  
Peltier Module ◽  
Current Value

The article presents the Peltier cell control system devel-oped by the authors. Due to the non-linear dependence of the cell's efficiency on the current, fuzzy logic was used to determine the control value. In the following parts of the article the actual characteristics of the Peltier cells, the method of determining the control current value (fuzzy controller synthesis). The controller of fuzzy logic and its relation to traditional control in a closed system and obtained results have been presented. The FUDGE software from Motorola was used to implement fuzzy logic. The control algorithm presented in the article will be used to develop cell control system that enables optimization of the Peltier cell control process.

scholarly journals Development of synthetic methodology of neuro-fuzzy controller adjusted by genetic algorithm

2018 ◽  
pp. 82-90
Author(s):  
N. A. Pervushina ◽  
D. E. Donovskiy ◽  
A. N. Khakimova
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Controller ◽  
Dynamic Control ◽  
Classical Problem ◽  
Control Object ◽  
Controller Synthesis ◽  
Synthetic Methodology ◽  
Neuro Fuzzy ◽  
Random Disturbances

The paper focuses on a synthetic methodology of a neuro-fuzzy controller adjusted by genetic algorithm for a dynamic control object. An algorithm for controller synthesis and a genetic algorithm for adjusting the controller's parameters have been developed. The methodology has been tested on the classical problem of stabilizing a vertical pendulum on a mobile trolley. The results obtained confirm the efficiency of the methodology and allow for the conclusion that the neuro-fuzzy controller when appropriately adjusted ensures high quality of the stabilization system, even if there are random disturbances on the dynamic object

Semi-active seismic control of buildings using MR damper and adaptive neural-fuzzy intelligent controller optimized with genetic algorithm

2018 ◽  
Vol 25 (2) ◽  
pp. 273-285 ◽  
Author(s):  
Masoud Bozorgvar ◽  
Seyed Mehdi Zahrai
Keyword(s):  
Genetic Algorithm ◽  
Control System ◽  
Fuzzy Controller ◽  
Mr Damper ◽  
Base Shear ◽  
Linear Quadratic ◽  
Inference System ◽  
Story Drift ◽  
Story Building ◽  
Neural Fuzzy

This research presents designing a control system to reduce seismic responses of structures. Semi-active control of a magnetorheological (MR) damper is used to improve seismic behavior of a 3-story building implementing neural-fuzzy controller made of adaptive neuro-fuzzy inference system (ANFIS) to determine damper input voltage. Both premise and consequent parameters of fuzzy membership and output functions of ANFIS have the ability for training and improvement but most researchers have focused on just consequent parameters. In order to optimize the controller performance, an approach is proposed in this paper where both premise and consequent parameters of fuzzy functions in an ANFIS network are adjusted simultaneously by genetic algorithm (GA). In order to assess the effectiveness of the designed control system, its function is numerically studied on a benchmark 3-story building and is compared to those of a neural network predictive control (NNPC) algorithm, linear quadratic Gaussian (LQG) and clipped optimal control (COC) systems in terms of seismic performance. The results showed desirable performance of the (ANFIS +GA + membership functions + result function) ANFIS–GA–MFR controller in considerably reducing the structure responses under different earthquakes. The proposed controller showed 30 and 39% reductions in peak story drift (J1) and normed story drift (J4) respectively compared to the NNPC controller, 32 and 44% reductions in J1 and J4 respectively compared to the LQG controller, and 27 and 38% reductions in J1 and J4 respectively compared to the COC controller. The proposed controller effectively reduced acceleration and base shear level compared to the uncontrolled state and had a performance relatively similar to those of three other controllers – for instance, it reduced the maximum level acceleration (J2) 10% higher than COC. Also, the results showed that the ANFIS–GA–MFR controller has more efficiency than the basic ANFIS controller, on average about 20%.

scholarly journals Path Planning of Mobile Robot Based on Improved Multiobjective Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Kairong Li ◽  
Qianqian Hu ◽  
Jinpeng Liu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Mobile Robot ◽  
Path Length ◽  
Single Point ◽  
Single Point Mutation ◽  
Local Optimum ◽  
Efficient Operation ◽  
Multiobjective Genetic Algorithm ◽  
Mutation Operators

Path planning is the core technology of mobile robot decision-making and control and is also a research hotspot in the field of artificial intelligence. Aiming at the problems of slow response speed, long planning path, unsafe factors, and a large number of turns in the conventional path planning algorithm, an improved multiobjective genetic algorithm (IMGA) is proposed to solve static global path planning in this paper. The algorithm uses a heuristic median insertion method to establish the initial population, which improves the feasibility of the initial path and generates a multiobjective fitness function based on three indicators: path length, path security, and path energy consumption, to ensure the quality of the planned path. Then, the selection, crossover, and mutation operators are designed by using the layered method, the single-point crossover method, and the eight-neighborhood-domain single-point mutation method, respectively. Finally, the delete operation is added, to further ensure the efficient operation of the mobile robot. Simulation experiments in the grid environment show that the algorithm can improve the defects of the traditional genetic algorithm (GA), such as slow convergence speed and easy to fall into local optimum. Compared with GA, the optimal path length obtained by planning is shortened by 17%.

Speeding Up Evolutionary Algorithms through Asymmetric Mutation Operators

2007 ◽  
Vol 15 (4) ◽  
pp. 401-410 ◽  
Author(s):  
Benjamin Doerr ◽  
Nils Hebbinghaus ◽  
Frank Neumann
Keyword(s):  
Lower Bound ◽  
Evolutionary Algorithms ◽  
Upper Bounds ◽  
Point Of View ◽  
Theoretical Point ◽  
Mutation Operator ◽  
Linear Factor ◽  
Mutation Operators ◽  
Variation Operators

Successful applications of evolutionary algorithms show that certain variation operators can lead to good solutions much faster than other ones. We examine this behavior observed in practice from a theoretical point of view and investigate the effect of an asymmetric mutation operator in evolutionary algorithms with respect to the runtime behavior. Considering the Eulerian cycle problem we present runtime bounds for evolutionary algorithms using an asymmetric operator which are much smaller than the best upper bounds for a more general one. In our analysis it turns out that a plateau which both algorithms have to cope with changes its structure in a way that allows the algorithm to obtain an improvement much faster. In addition, we present a lower bound for the general case which shows that the asymmetric operator speeds up computation by at least a linear factor.

SYNTHESIS AND OPTIMIZATION OF FUZZY CONTROLLER FOR CONTROL SYSTEM OF FLOATING DOCK DRAFT

2016 ◽  
Vol 23 (99) ◽  
pp. 113-120
Author(s):  
Yuri P. Kondratenko ◽  
◽  
Alexey V. Korobko ◽  
Alexey V. Kozlov ◽  
Andrej N Topalov ◽  
...  
Keyword(s):  
Control System ◽  
Fuzzy Controller

Path Tracking Controller Design of Automatic Guided Vehicle Based on Four-Wheeled Omnidirectional Motion Model

2020 ◽  
Vol 17 (2) ◽  
pp. 7996-8010
Author(s):  
X. Wu ◽  
Y. Yang
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Threshold Value ◽  
Position Angle ◽  
Path Tracking ◽  
Global Coordinate System ◽  
Automatic Guided Vehicle ◽  
Input Variables ◽  
Industrial Pc

This paper presents a new design of omnidirectional automatic guided vehicle based on a hub motor, and proposes a joint controller for path tracking. The proposed controller includes two parts: a fuzzy controller and a multi-step predictive optimal controller. Firstly, based on various steering conditions, the kinematics model of the whole vehicle and the pose (position, angle) model in the global coordinate system are introduced. Secondly, based on the modeling, the joint controller is designed. Lateral deviation and course deviation are used as the input variables of the control system, and the threshold value is switched according to the value of the input variable to realise the correction of the large range of posture deviation. Finally, the joint controller is implemented by using the industrial PC and the self-developed control system based on the Freescale minimum system. Path tracking experiments were made under the straight and circular paths to test the ability of the joint controller for reducing the pose deviation. The experimental results show that the designed guided vehicle has excellent ability to path tracking, which meets the design goals.

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