Particle Swarm Optimization of fuzzy models for Anti-Lock Braking Systems
02 Jun 2014-pp 1-6
TL;DR: A Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Anti-lock Braking Systems (ABSs) and the optimal T-S fuzzy models are suggested.
Abstract: This paper suggests a Particle Swarm Optimization (PSO) approach to the optimal tuning of fuzzy models for Anti-lock Braking Systems (ABSs). A set of ten local state-space models of the ABS is first obtained by the linearization of the nonlinear state-space model of the ABS process at ten operating points. The initial Takagi-Sugeno (T-S) fuzzy models are next obtained by the modal equivalence principle, namely by placing the local state-space models of the process in the rule consequents. The optimization problem targets the minimization of the objective function (OF) expressed as the mean squared modeling error, and the vector variable of the OF consists of the feet of the triangular input membership functions. A PSO algorithm solves the optimization problem and gives the optimal T-S fuzzy models. A set of real-time experimental results is included to validate the PSO approach and the optimal T-S fuzzy models for real-world ABS laboratory equipment.
Citations
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01 Feb 2015
TL;DR: In this paper, a synergy of fuzzy logic and nature-inspired optimization in terms of the natureinspired optimal tuning of the input membership functions of a class of Takagi-Sugeno-Kang (TSK) fuzzy models dedicated to Anti-lock Braking Systems (ABSs) is proposed.
Abstract: Real-time experimental results: wheel slip λ versus time for the TSK fuzzy model 3 after optimization by the SA algorithm and after optimization by the PSO algorithm considering the validation data set. New Takagi-Sugeno fuzzy models dedicated to Anti-lock Braking Systems (ABSs) are proposed.A set of three optimal fuzzy models is derived by an original fuzzy modeling approach.The approach involves Simulated Annealing or Particle Swarm Optimization algorithms.The models are tested and compared against experimental data on ABS laboratory equipment. This paper suggests a synergy of fuzzy logic and nature-inspired optimization in terms of the nature-inspired optimal tuning of the input membership functions of a class of Takagi-Sugeno-Kang (TSK) fuzzy models dedicated to Anti-lock Braking Systems (ABSs). A set of TSK fuzzy models is proposed by a novel fuzzy modeling approach for ABSs. The fuzzy modeling approach starts with the derivation of a set of local state-space models of the nonlinear ABS process by the linearization of the first-principle process model at ten operating points. The TSK fuzzy model structure and the initial TSK fuzzy models are obtained by the modal equivalence principle in terms of placing the local state-space models in the rule consequents of the TSK fuzzy models. An operating point selection algorithm to guide modeling is proposed, formulated on the basis of ranking the operating points according to their importance factors, and inserted in the third step of the fuzzy modeling approach. The optimization problems are defined such that to minimize the objective functions expressed as the average of squared modeling errors over the time horizon, and the variables of these functions are a part of the parameters of the input membership functions. Two representative nature-inspired algorithms, namely a Simulated Annealing (SA) algorithm and a Particle Swarm Optimization (PSO) algorithm, are implemented to solve the optimization problems and to obtain optimal TSK fuzzy models. The validation and the comparison of SA and PSO and of the new TSK fuzzy models are carried out for an ABS laboratory equipment. The real-time experimental results highlight that the optimized TSK fuzzy models are simple and consistent with both training data and validation data and that these models outperform the initial TSK fuzzy models.
95 citations
TL;DR: Independent model generalize predictive control (IMGPC) is introduced for antilock braking system, and through the numerical simulation, it is demonstrated that it can control the system in presence of sever noise and disturbances.
Abstract: Wheel slip control is a significant research topics in the field of car stability. Model predictive control is one of the most advanced controller which has received great attention and application in industries. In this paper independent model generalize predictive control (IMGPC) is introduced for antilock braking system. This controller is implemented on a linear model of anti-lock braking system, and through the numerical simulation, it is demonstrated that it can control the system in presence of sever noise and disturbances. The simulation results show that the proposed controller has better performance in comparison with other conventional linear controllers. General Terms Predictive control, Anti-lock braking system .
5 citations
Cites background from "Particle Swarm Optimization of fuzz..."
...presented a particle swarm optimization (PSO) for tuning the fuzzy controller which was designed for a linear dynamic model of ABS [5]....
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TL;DR: In this paper, both the usage and the size of hydraulic brake systems can be reduced by re-designing and re-sizing of the foundation brake system, which can particularly reduce the size and usage of the hydraulic brake system.
Abstract: Electrification of road vehicle powertrains involves significant re-design and re-sizing of foundation brakes. Both the usage and the size of hydraulic brake systems can particularly be reduced tha...
5 citations
Cites background from "Particle Swarm Optimization of fuzz..."
...Even if few research works can be found in literature concerning the application of PSO in the field of brake systems [42] and BEV chassis [43], the obtained results demonstrate, in our opinion, its effectiveness....
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Dissertation•
01 Aug 2015
TL;DR: The modified ABC algorithm is modified by incorporating pheromone which is one of the major components of Ant Colony Optimization (ACO) algorithm and introduced a new operation in which successive bees communicate to share their findings.
Abstract: Development of cancer diagnostic models by utilizing microarray data has become a topic of great interest in the field of bioinformatics and medicine. Only a small number of gene expression data compared to the total number of genes explored possess a significant correlation with a certain phenotype. Gene selection enables researchers to obtain substantial insight into the genetic nature of the disease and the mechanisms responsible for it. Besides improvement of the performance of cancer classification, it can also cut down the time and cost of medical diagnosis. This study presents a modified Artificial Bee Colony Algorithm (ABC) to select a minimum number of genes that are deemed to be significant for cancer along with improvement of predictive accuracy. The search equation of ABC is said to be good at exploration but poor at exploitation. To overcome this limitation we have modified the ABC algorithm by incorporating pheromone which is one of the major components of Ant Colony Optimization (ACO) algorithm and introduced a new operation in which successive bees communicate to share their findings. The proposed algorithm is evaluated using a suite of ten publicly available datasets after the parameters are scientifically tuned with one of the datasets. The obtained results are compared to other works which used the same datasets. The performance of the proposed method has been proved to be superior. The method presented in this paper can provide a subset of genes leading to more accurate classification results while the number of selected genes is smaller. The proposed modified ABC Algorithm could conceivably be applied to problems in other areas.
4 citations
Cites methods from "Particle Swarm Optimization of fuzz..."
...PSO has been successfully applied in many areas including function optimization [71, 251], artificial neural network training [166], fuzzy system control [31,188], business optimization [266], scheduling problems [113], and other application problems [71]....
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"Particle Swarm Optimization of fuzz..." refers background in this paper
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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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35,104 citations
"Particle Swarm Optimization of fuzz..." refers methods in this paper
...P P (11) Using the notation μ for the iteration index, the next particle velocity ) 1 ( + μ d i v and the next particle position ) 1 ( + μ d i x are obtained by the state-space equations [23], [24]:...
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...Our PSO algorithm applied in the step 3 is adapted from the general formulation given in [23] and [24] and from the PSO algorithm applied in [20] to the optimal tuning of fuzzy controllers....
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04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
Abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
14,477 citations
"Particle Swarm Optimization of fuzz..." refers methods in this paper
...P P (11) Using the notation μ for the iteration index, the next particle velocity ) 1 ( + μ d i v and the next particle position ) 1 ( + μ d i x are obtained by the state-space equations [23], [24]:...
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...Our PSO algorithm applied in the step 3 is adapted from the general formulation given in [23] and [24] and from the PSO algorithm applied in [20] to the optimal tuning of fuzzy controllers....
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01 Feb 2014
TL;DR: The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.
Abstract: This paper surveys neuro fuzzy systems (NFS) development using classification and literature review of articles for the last decade (2002-2012) to explore how various NFS methodologies have been developed during this period. Based on the selected journals of different NFS applications and different online database of NFS, this article surveys and classifies NFS applications into ten different categories such as student modeling system, medical system, economic system, electrical and electronics system, traffic control, image processing and feature extraction, manufacturing and system modeling, forecasting and predictions, NFS enhancements and social sciences. For each of these categories, this paper mentions a brief future outline. This review study indicates mainly three types of future development directions for NFS methodologies, domains and article types: (1) NFS methodologies are tending to be developed toward expertise orientation. (2) It is suggested that different social science methodologies could be implemented using NFS as another kind of expert methodology. (3) The ability to continually change and learning capability is the driving power of NFS methodologies and will be the key for future intelligent applications.
286 citations
"Particle Swarm Optimization of fuzz..." refers background in this paper
...A discussion on fuzzy models for ABSs is included in [6]....
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TL;DR: The aim of this paper is to show how to automatically build this fuzzy controller, and the proposed design methodology is detailed for the synthesis of a Sugeno or Mamdani type fuzzy controller precisely equivalent to a given PI controller.
Abstract: It has been proved that fuzzy controllers are capable of approximating any real continuous control function on a compact set to arbitrary accuracy. In particular, any given linear control can be achieved with a fuzzy controller for a given accuracy. The aim of this paper is to show how to automatically build this fuzzy controller. The proposed design methodology is detailed for the synthesis of a Sugeno or Mamdani type fuzzy controller precisely equivalent to a given PI controller. The main idea is to equate the output of the fuzzy controller with the output of the PI controller at some particular input values, called modal values. The rule base and the distribution of the membership functions can thus be deduced. The analytic expression of the output of the generated fuzzy controller is then established. For Sugeno-type fuzzy controllers, precise equivalence is directly obtained. For Mamdani-type fuzzy controllers, the defuzzification strategy and the inference operators have to be correctly chosen to provide linear interpolation between modal values. The usual inference operators satisfying the linearity requirement when using the center of gravity defuzzification method are proposed. >
239 citations
"Particle Swarm Optimization of fuzz..." refers background or methods in this paper
...Discrete-time dynamic T-S fuzzy models of ABS based on the modal equivalence principle [2] and on the sector nonlinearity approach [3] are proposed in [4]....
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...s which are exactly the coordinates of the operating points in terms of the modal equivalence principle [2]....
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