scholarly journals New Improved Fractional Order Differentiator Models Based on Optimized Digital Differentiators

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Maneesha Gupta ◽  
Richa Yadav
Keyword(s):  
Fractional Order ◽  
Differential Operators ◽  
Fitness Function ◽  
Hybrid Optimization ◽  
Nyquist Frequency ◽  
Swarm Optimization ◽  
Optimization Simulation ◽  
Fractional Order Differentiator ◽  
Simulation Results ◽  
The Ideal

Different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA), and PSO-GA hybrid optimization, have been used to optimize digital differential operators so that these can be better fitted to exemplify their new improved fractional order differentiator counterparts. First, the paper aims to provide efficient 2nd and 3rd order operators in connection with process of minimization of error fitness function by registering mean, median, and standard deviation values in different random iterations to ascertain the best results among them, using all the abovementioned EAs. Later, these optimized operators are discretized for half differentiator models for utilizing their restored qualities inhibited from their optimization. Simulation results present the comparisons of the proposed half differentiators with the existing and amongst different models based on 2nd and 3rd order optimized operators. Proposed half differentiators have been observed to approximate the ideal half differentiator and also outperform the existing ones reasonably well in complete range of Nyquist frequency.

An efficient design of finite impulse response — Fractional-order differentiator using shuffled frog leaping algorithm heuristic

2019 ◽  
Vol 18 (01) ◽  
pp. 1941005
Author(s):  
G. S. S. S. S. V. Krishna Mohan ◽  
Yarravarapu Srinivasa Rao
Keyword(s):  
Signal Processing ◽  
Impulse Response ◽  
Fractional Order ◽  
Finite Impulse Response ◽  
Fitness Function ◽  
Infinite Length ◽  
Shuffled Frog Leaping Algorithm ◽  
Digital Differentiator ◽  
Shuffled Frog Leaping ◽  
Fractional Order Differentiator

A fractional-order digital differentiator is employed for the calculation of a time-derivative of the applied signal. In the recent few decades, this particular concept of a fractional derivative has been gaining a lot of attention in various applications concerning engineering, technology and science that includes image processing along with automatic control. Once there has been an effective use for this continuous-time Fractional-Order Differentiator (FOD), the trend in its research is primarily toward using a discrete-time fractional differentiator. All these conventional techniques tend to make use of a unimodal function for approximating an ideal FOD. For these techniques, there is a minimization of the fitness function that is accomplished by the algorithms which are based on the gradient. The fractional-order circuits along with their systems include an emerging area that has a high level of potential in aspects such as the biomedical instrumentation, control or signal processing. A digital differentiator is a tool that is extremely helpful in the determination and estimation of time derivatives of any given signal. Irrespective of the actual type of filter chosen (the Finite Impulse Response (FIR) or the Infinite-Length Impulse Response (IIR)), it is critical to bring down the complexity of computation needed for the implementation of the filter for a certain bandwidth and error of approximation. A metaheuristic algorithm normally has some advantages in the solving of problems which are Non-Deterministic Polynomial (NP)-hard. The Shuffled Frog Leaping Algorithm (SFLA) has been a new heuristic algorithm proposed in this work for the determination of optimal coefficients of the problem of FIR-FOD. A design for fractional-order-based digital differentiator is not a very important topic in research and signal processing.

Fractional Order Controller Design for Ball and Beam System

2013 ◽  
Vol 313-314 ◽  
pp. 544-548 ◽  
Author(s):  
Mehmet Korkmaz ◽  
Omer Aydogdu
Keyword(s):  
Genetic Algorithms ◽  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Fractional Order ◽  
Controller Design ◽  
Swarm Optimization ◽  
System Parameters ◽  
Beam System ◽  
Simulation Results ◽  
Fractional Order Controller

Fractional order controllers which has mostly used recently have investigated in this paper. It is benefit from ball & beam system to show effects of controllers. Fractional order controller and its integer form are compared with simulation results for the mentioned system. Parameters of controllers have obtained by using evolutionary algorithms techniques which are particle swarm optimization (PSO) and genetic algorithms (GAs). According to results, it is confirmed the advantage of fractional controllers. Beside, PSO has a little bit superiority over GAs technique for determining optimum values of controller parameters.

Multi-Task Coalition Generation of Multi-Agent System with Limited Resource

2014 ◽  
Vol 971-973 ◽  
pp. 1655-1658
Author(s):  
Ning Qiang ◽  
Feng Ju Kang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Limited Resource ◽  
Multi Agent System ◽  
Discrete Particle Swarm Optimization ◽  
Agent System ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Agent ◽  
Simulation Results

A new fitness function is introduced in order to maximize the number of task served by the multi-agent system (MAS) with limited resource, while the tasks information remains unknown until the system found them one by one. The new fitness function not only considers to maximize the profit of the system which can be seen as to maximize the remaining resource of the system in the case of the MAS with limited resource, but also takes the balance of remaining resource in to account and it can makes a compromise between them. This paper uses an improved discrete particle swarm optimization to optimize the coalition of MAS. In order to improve the performance of the algorithm we redefine the particle velocity and position update formula. The simulation results show the effectiveness and superiority of the proposed fitness function and optimization algorithm.

scholarly journals Stabilization and Tracking Control of Inverted Pendulum Using Fractional Order PID Controllers

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Sunil Kumar Mishra ◽  
Dinesh Chandra
Keyword(s):  
Fractional Calculus ◽  
Fractional Order ◽  
Tracking Control ◽  
Inverted Pendulum ◽  
Fitness Function ◽  
Pid Controllers ◽  
Simulink Model ◽  
System A ◽  
Simulation Results ◽  
Fractional Order Pid

This work focuses on the use of fractional calculus to design robust fractional-order PID (PIλDμ) controller for stabilization and tracking control of inverted pendulum (IP) system. A particle swarm optimisation (PSO) based direct tuning technique is used to design two PIλDμcontrollers for IP system without linearizing the actual nonlinear model. The fitness function is minimized by running the SIMULINK model of IP system according to the PSO program in MATLAB. The performance of proposed PIλDμcontrollers is compared with two PID controllers. Simulation results are also obtained by adding disturbances to the model to show the robustness of the proposed controllers.

scholarly journals Optimization of Integer Order Integrators for Deriving Improved Models of Their Fractional Counterparts

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Maneesha Gupta ◽  
Richa Yadav
Keyword(s):  
Continued Fraction ◽  
Fitness Function ◽  
Pso Algorithm ◽  
Nyquist Frequency ◽  
Continued Fraction Expansion ◽  
Third Order ◽  
Swarm Optimization ◽  
Integer Order ◽  
Frequency Responses ◽  
Digital Integrators

Second and third order digital integrators (DIs) have been optimized first using Particle Swarm Optimization (PSO) with minimized error fitness function obtained by registering mean, median, and standard deviation values in different random iterations. Later indirect discretization using Continued Fraction Expansion (CFE) has been used to ascertain a better fitting of proposed integer order optimized DIs into their corresponding fractional counterparts by utilizing their refined properties, now restored in them due to PSO algorithm. Simulation results for the comparisons of the frequency responses of proposed 2nd and 3rd order optimized DIs and proposed discretized mathematical models of half integrators based on them, with their respective existing operators, have been presented. Proposed integer order PSO optimized integrators as well as fractional order integrators (FOIs) have been observed to outperform the existing recently published operators in their respective domains reasonably well in complete range of Nyquist frequency.

scholarly journals A Novel Feature Selection Method Based on Extreme Learning Machine and Fractional-Order Darwinian PSO

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yuan-Yuan Wang ◽  
Huan Zhang ◽  
Chen-Hui Qiu ◽  
Shun-Ren Xia
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Extreme Learning Machine ◽  
Fractional Order ◽  
Fitness Function ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Feature Subset ◽  
Swarm Optimization ◽  
Learning Machine

The paper presents a novel approach for feature selection based on extreme learning machine (ELM) and Fractional-order Darwinian particle swarm optimization (FODPSO) for regression problems. The proposed method constructs a fitness function by calculating mean square error (MSE) acquired from ELM. And the optimal solution of the fitness function is searched by an improved particle swarm optimization, FODPSO. In order to evaluate the performance of the proposed method, comparative experiments with other relative methods are conducted in seven public datasets. The proposed method obtains six lowest MSE values among all the comparative methods. Experimental results demonstrate that the proposed method has the superiority of getting lower MSE with the same scale of feature subset or requiring smaller scale of feature subset for similar MSE.

Design of Fractional Order PID Controller Using Genetic Algorithm Optimization Technique for Nonlinear System

2020 ◽  
Vol 15 (2) ◽  
Author(s):  
Shivanky Jaiswal ◽  
Chiluka Suresh Kumar ◽  
Murali Mohan Seepana ◽  
G. Uday Bhaskar Babu
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear System ◽  
Fractional Order ◽  
Pid Controller ◽  
Fitness Function ◽  
Optimization Technique ◽  
Algorithm Optimization ◽  
Simulation Results ◽  
Fractional Order Pid ◽  
Fractional Order Pid Controller

AbstractIn this paper, fractional order PID controller, as well as integer order PID controller, is designed for non-linear system to enhance the system’s performance and gain the stability. The novelty of the work is achieved by the development of a new methodology for integer order PID and fractional order PID control tuning by optimizing the parameters of controllers using the Genetic Algorithms optimization technique. The performance of any system mainly depends upon how efficiently the controller will be working and hence that’s how most crucial part of the designing of FOPID controller or any controller is the tuning of its parameters. The uniquely designed and tuned parameters of the FOPID controller which is obtained by optimizing all the five parameters by using an evolutionary algorithm optimization technique i. e. a genetic algorithm which is a very powerful search tool and carrying heuristic characteristics. This method of tuning the FOPID controller which is designed and has been applied over the conical tank (nonlinear) system. The most important step in applying genetic algorithm is the selection of the fitness function and hence Integral of time multiplied by absolute error (ITAE) have been used here as the fitness function. Each chromosome comprised of all the five parameters of FOPID controller, which have been further optimised using above mentioned fitness function. From the simulation results, it can be observed that the solutions which are obtained optimally, presents an excellent performance for the system studied, by improving the behaviour of the system satisfactorily. Simulation results also show that the proposed FOPID controller gives improved performance over classical PID controller in terms of IAE and TV.

A potential field-based PSO approach to multi-robot cooperation for target search and hunting

2017 ◽  
Vol 65 (12) ◽  
Author(s):  
Xiang Cao ◽  
Changyin Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Potential Field ◽  
Fitness Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Target Search ◽  
Field Function ◽  
Simulation Results ◽  
Robot Cooperation ◽  
Multi Robot

AbstractThe control design of target search and hunting using multi-robot remains a challenge in recent years. In this paper, we propose a control algorithm of multi-robot for target search and hunting inspired by potential field-based particle swarm optimization (PPSO). Firstly, a potential field function is established according to the initial positions of the obstacles, un-search area and targets. Then, the fitness function of PSO's (particle swarm optimization) is determined by the potential function of the work area. Lastly, multi-robot start performing target search and hunting missions driven by the proposed PPSO algorithm. Simulation results demonstrate that the PPSO algorithm is applicable and feasible for multi-robot cooperation to search and hunting targets. Compared with other commonly used methods for control of multi-robot, simulation results indicate that the PPSO algorithm has more stability and higher efficiency.

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