Control parameters inversion using genetic algorithms applied to numerical impedance synthesis for woodwinds

Modern challenges in a post-industrial society require further development of management systems for complex technical and technological phenomena and processes. Effective control of an object is possible if a controller, or a fuzzy controller, correctly generates the required control action. Recently, fuzzy controllers have been very popular. Fuzzy logical statements in this case help considering various nonlinear relationships. The synthesis of the fuzzy controller parameters allows for more efficient operation of the control system. A possible option for obtaining the best set of parameters for a fuzzy controller is the use of genetic algorithms for its synthesis. The use of genetic algorithms for the fuzzy controllers synthesis can lead to the fact that the elements of its parameters array will change in such a way that an incorrect value of one or more elements will occur. This situation leads to impossibility of composing membership functions for the terms of the variables of the fuzzy controller. Incorrect value formation is excluded by constructing a limited functional dependency. This paper proposes a mathematical model of the parameters of the term-set of variables of a fuzzy controller of the Takagi — Sugeno — Kang type of the zero and first orders. The authors disclose the content of the conditions and conclusions of the rule base for the fuzzy controller of the above type. As a result of the simulation, some operations of the genetic algorithm are implemented using a random number generator. Graphical models of the membership functions of the input variables of the fuzzy controller of the type under consideration clearly illustrate the occurrence of all parameters in their range of possible values. A description of the control system operation with two control parameters and one control action at the specified values of the control parameters is presented.

Download Full-text

Operator Control Parameters and Fine Tuning of Genetic Algorithms (GAs)

Advances in Computational Intelligence and Robotics - Genetic Algorithms and Applications for Stock Trading Optimization ◽

10.4018/978-1-7998-4105-0.ch007 ◽

2021 ◽

pp. 115-127

Keyword(s):

Genetic Algorithms ◽

Black Box ◽

Fine Tuning ◽

Control Parameters ◽

Solution Quality ◽

Internal Working ◽

Size Number ◽

Crossover Probability ◽

The Mind ◽

Operator Control

Genetic algorithms (GAs) are heuristic, blind (i.e., black box-based) search techniques. The internal working of GAs is complex and is opaque for the general practitioner. GAs are a set of interconnected procedures that consist of complex interconnected activity among parameters. When a naive GA practitioner tries to implement GA code, the first question that comes into the mind is what are the value of GA control parameters (i.e., various operators such as crossover probability, mutation probability, population size, number of generations, etc. will be set to run a GA code)? This chapter clears all the complexities about the internal interconnected working of GA control parameters. GA can have many variations in its implementation (i.e., mutation alone-based GA, crossover alone-based GA, GA with combination of mutation and crossover, etc.). In this chapter, the authors discuss how variation in GA control parameter settings affects the solution quality.

Download Full-text

A method of accelerating convergence for Genetic Algorithms evolving morphological and control parameters for a biomimetic robot

2009 4th International Conference on Autonomous Robots and Agents ◽

10.1109/icara.2000.4803935 ◽

2009 ◽

Cited By ~ 1

Author(s):

Frank Saunders ◽

John Rieffel ◽

Jason Rife

Keyword(s):

Genetic Algorithms ◽

Control Parameters ◽

Biomimetic Robot ◽

And Control

Download Full-text

Dynamic Optimization Genetic Algorithms for Flight Control Laws Design

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.278-280.1581 ◽

2013 ◽

Vol 278-280 ◽

pp. 1581-1584

Author(s):

Xiao Xiong Liu ◽

Yan Wu ◽

Peng Hui Li ◽

Heng Xu

Keyword(s):

Genetic Algorithms ◽

Dynamic Optimization ◽

Flight Control ◽

Control Parameters ◽

Longitudinal Control ◽

Control Laws ◽

Longitudinal Function ◽

Adaptive Capability ◽

Dynamic Optimization Model ◽

Set Up

The general flight control laws are designed by static designs and dynamic fits. To improve the adaptive capability, the method of control laws design was introduced by using dynamic optimization genetic algorithms. The control parameters were adjusted online in the flight envelope. The dynamic optimization model was built for aircraft longitudinal function. The fitness was set up by applying order track. And then the control parameters were regulated by dynamic optimization genetic algorithms. Finally an example of a longitudinal control augmented stability system of an aircraft is used with a simulation.

Download Full-text