Evolving optimal Fuzzy Logic controllers by genetic algorithms
01 May 2004-Iete Journal of Research (Institution of Electronics and Telecommunication Engineers)-Vol. 50, Iss: 3, pp 179-190
TL;DR: GA-evolved FLC shows better closed-loop performance on linear and nonlinear stable and unstable systems as compared to a hand-designed FLC and a GA-designed PID and is better preserved by GAFLC than by PID for nonlinear systems.
Abstract: The objective of this paper is to present an optimal design of Fuzzy Logic Controllers (FLCs) by Genetic Algorithms (GAs). As part of this objective, the design of input and output Membership Functions (mfs) of FLC is carried out simultaneously with Input and output Scaling Factors (sfs). FLC since can be subjected to a range of set points, so the step size for mutation of alleles, and fitness function are adapted based upon Set Point Change (SPCs) commands, and besides, bounds of either input and output mfs or else of sfs are SPC-adapted; the latter two adaptations are demonstrated to be equivalent Tuning is based upon maximization of a comprehensive fitness function constructed as inverse of a weighted-average of Integral Square Error (ISE), Peak Overshoot (Mp), Rise Time (tr) and Settling Time (ts), wherein weights for ISE and Mp are adapted. GA-evolved FLC (henceforth GAFLC) shows better closed-loop performance on linear and nonlinear stable and unstable systems as compared to a hand-designed FLC and a GA-designed PID (Proportional-Integral-Derivative Controller). This performance is almost fully preserved by GAFLC, under a wide range of SPCs, for linear systems (and the PID is demonstrated to share this property) and is better preserved by GAFLC than by PID for nonlinear systems. Some future directions are listed.
Citations
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TL;DR: FopID controller, due to its extra tuned parameters, has shown extremely efficient results in comparison to the traditional IOPID controller.
Abstract: MAGnetic LEVitation (Maglev) is a multi-variable, non-linear and unstable system that is used to levitate a ferromagnetic object in free space This paper presents the stability control of a levitating object in a magnetic levitation plant using Fractional order PID (FOPID) controller Fractional calculus, which is used to design the FOPID controller, has been a subject of great interest over the last few decades FOPID controller has five tunning parameters including two fractional-order parameters ($\lambda $ and $\mu $ ) The mathematical model of the Maglev plant is obtained by using first principle modeling and the laboratory model (CE152) Maglev plant and FOPID controller both have been designed in MATLAB-Simulink The designed model of the Maglev system can be further used in the process of controller design for other applications The stability of the proposed system is determined via the Routh Hurwitz stability criterion Ant Colony Optimization (ACO) algorithm and Ziegler Nichols method has been used to fine-tune the parameters of FOPID controller FOPID controller output results are compared with the traditional IOPID controller for comparative analysis FOPID controller, due to its extra tuned parameters, has shown extremely efficient results in comparison to the traditional IOPID controller
44 citations
Cites methods from "Evolving optimal Fuzzy Logic contro..."
...Furthermore, neural networks [7], fuzzy logic control [8] and evolutionary algorithms [9] have also been used....
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TL;DR: The robustness analysis has been incorporated in this study which shows that the 1 and 2-DOF IOPID and FOPID controllers designed using different optimization algorithms for the Maglev plant are robust in nature.
Abstract: In this paper, 1-Degree of Freedom (1-DOF) and 2-Degree of Freedom (2-DOF) Integer Order (IO) and Fractional Order (FO) Proportional–Integral–Derivative (PID) Controller has been designed for the M...
16 citations
Cites background or methods from "Evolving optimal Fuzzy Logic contro..."
...The concept of GA [6] is based on the basic Darwinian natural biological selection rule and genetic mechanism....
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...The use of evolutionary algorithm [5], fuzzy logic [6] and neural network [7] can also be found in various literature....
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...In different research works, various optimization algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Hybrid PSO, Bee Colony Optimization (BCO) [5,13–17], etc. have been used for determining the controller parameters by optimizing the objective function....
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...The Genetic Algorithm [5,6,13,14,17] is a popular and highly robust heuristic optimization algorithm (proposed by John Holland in the 1970s) which mimics some of the processes involved in natural evolution....
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TL;DR: In this paper, a modified compensated matching method for designing a low-noise broadband microwave amplifier with guaranteed stability is proposed.
Abstract: In this paper, a modified compensated matching method for designing a low-noise broadband microwave amplifier with guaranteed stability is proposed. Compensated matching method is formulated as an ...
12 citations
TL;DR: The GA-optimized FLC (GAFLC) shows better performance as compared to a conventional proportional integral (PI) and a hand-designed fuzzy logic controller not only for a standard system (displaying frequency deviations) but also under parametric and load disturbances.
Abstract: This paper presents a genetic algorithm (GA)-based design and optimization of fuzzy logic controller (FLC) for automatic generation control (AGC) for a single area. FLCs are characterized by a set of parameters, which are optimized using GA to improve their performance. The design of input and output membership functions (mfs) of an FLC is carried out by automatically tuning (off-line) the parameters of the membership functions. Tuning is based on maximization of a comprehensive fitness function constructed as inverse of a weighted average of three performance indices, i.e., integral square deviation (ISD), the integral of square of the frequency deviation and peak overshoot (Mp), and settling time (ts). The GA-optimized FLC (GAFLC) shows better performance as compared to a conventional proportional integral (PI) and a hand-designed fuzzy logic controller not only for a standard system (displaying frequency deviations) but also under parametric and load disturbances.
9 citations
01 Nov 2016
TL;DR: In this paper, the control and stabilization of a quadcopter UAV using genetic algorithm tuned PID controller has been discussed, in which crossover fraction and mutation rate is made adaptive by using a Fussy logic controller.
Abstract: This paper deals with control and stabilization of a quadcopter UAV using Genetic algorithm tuned PID controller. In this work, conventional GA is improved in two ways: firstly, crossover fraction and mutation rate is made adaptive by using a Fussy logic controller. Secondly, an advanced randomness is provided in GA by changing half of its initial population with random candidates after a fixed generations. Simulation results proven to be more optimised with the proposed controller in respect to the doth transient response and robustness in presence of adverse condition or disturbances.
6 citations
Cites background from "Evolving optimal Fuzzy Logic contro..."
...Genetic algorithm GA is a versatile method in the field of optimization over the past few years& have already been used to solve the optimization problems with minimal problem information[7] e.g. Task of tuning of PID controller [8], evolving the structure of Artificial Neural Networks [9] & evolving the structure of Fuzzy Logic Controllers [10], Economic Load Dispatch Problems [11] & many other optimization problems....
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...Task of tuning of PID controller [8], evolving the structure of Artificial Neural Networks [9] & evolving the structure of Fuzzy Logic Controllers [10], Economic Load Dispatch Problems [11] & many other optimization problems....
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References
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TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Abstract: An Introduction to Genetic Algorithms is one of the rare examples of a book in which every single page is worth reading. The author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues, yet the book is concise (200 pages) and readable. Although Mitchell explicitly states that her aim is not a complete survey, the essentials of genetic algorithms (GAs) are contained: theory and practice, problem solving and scientific models, a \"Brief History\" and \"Future Directions.\" Her book is both an introduction for novices interested in GAs and a collection of recent research, including hot topics such as coevolution (interspecies and intraspecies), diploidy and dominance, encapsulation, hierarchical regulation, adaptive encoding, interactions of learning and evolution, self-adapting GAs, and more. Nevertheless, the book focused more on machine learning, artificial life, and modeling evolution than on optimization and engineering.
7,098 citations
TL;DR: Researchers at the U.S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLC’s) that are capable of effectively managing nonlinear, rapidly changing pH systems commonly found in industry.
Abstract: Abstruct- Establishing suitable control of pH, a requirement in a number of mineral and chemical industries, poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. Researchers at the U.S. Bureau of Mines have developed a technique for producing adaptive fuzzy logic controllers (FLC’s) that are capable of effectively managing such systems. In this technique, a genetic algorithm (GA) alters the membership functions employed by a conventional FLC, an approach that is contrary to the tactic generally used to provide FLC’s with adaptive capabilities in which the rule set is altered. GA’s are search algorithms based on the mechanics of natural genetics that are able to rapidly locate near-optimal solutions to difficult problems. The Bureau-developed technique is used to produce an adaptive GA-FLC for a laboratory acid-base experiment. Nonlinearities in the laboratory system are associated with the logarithmic pH scale (pH is proportional to the logarithm of HJO’ ions) and changing process dynamics are introduced by altering system parameters such as the desired set point and the concentration and buffering capacity of input solutions. Results indicate that FLC’s augmented with GA’s offer a powerful alternative to conventional process control techniques in the nonlinear, rapidly changing pH systems commonly found in industry.
714 citations
TL;DR: This paper examines the applicability of genetic algorithms in the simultaneous design of membership functions and rule sets for fuzzy logic controllers and examines the design of a robust controller for the cart problem and its ability to overcome faulty rules.
Abstract: This paper examines the applicability of genetic algorithms (GA's) in the simultaneous design of membership functions and rule sets for fuzzy logic controllers. Previous work using genetic algorithms has focused on the development of rule sets or high performance membership functions; however, the interdependence between these two components suggests a simultaneous design procedure would be a more appropriate methodology. When GA's have been used to develop both, it has been done serially, e.g., design the membership functions and then use them in the design of the rule set. This, however, means that the membership functions were optimized for the initial rule set and not the rule set designed subsequently. GA's are fully capable of creating complete fuzzy controllers given the equations of motion of the system, eliminating the need for human input in the design loop. This new method has been applied to two problems, a cart controller and a truck controller. Beyond the development of these controllers, we also examine the design of a robust controller for the cart problem and its ability to overcome faulty rules. >
673 citations
01 Jan 1993
TL;DR: An automatic fuzzy system design method that uses a genetic algorithm and integrates three design stages that was applied to the classic inverted-pendulum control problem and has been shown to be practical through a comparison with another method.
Abstract: The authors propose an automatic fuzzy system design method that uses a genetic algorithm and integrates three design stages. The method determines membership functions, the number of fuzzy rules, and the rule-consequent parameters at the same time. Because these design stages may not be independent, it is important to consider them simultaneously to obtain optimal fuzzy systems. The method includes a genetic algorithm and a penalty strategy that favors systems with fewer rules. The method was applied to the classic inverted-pendulum control problem and has been shown to be practical through a comparison with another method. >
419 citations
Proceedings Article•
01 Apr 1993403 citations