116,375 research outputs found
Anti-synchronization of discrete-time chaotic systems using optimization algorithms
In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result
Parameter Identification of a Fed-Batch Cultivation of S. Cerevisiae using Genetic Algorithms
Fermentation processes as objects of modelling and high-quality
control are characterized with interdependence and time-varying of process
variables that lead to non-linear models with a very complex structure. This
is why the conventional optimization methods cannot lead to a satisfied
solution. As an alternative, genetic algorithms, like the stochastic global
optimization method, can be applied to overcome these limitations. The
application of genetic algorithms is a precondition for robustness and reaching
of a global minimum that makes them eligible and more workable for
parameter identification of fermentation models. Different types of genetic
algorithms, namely simple, modified and multi-population ones, have been
applied and compared for estimation of nonlinear dynamic model parameters
of fed-batch cultivation of S. cerevisiae.* This work is partly supported by the National Science Fund Project MI – 1505/2005
APPLICATION OF GENETIC ALGORITHMS AND CFD FOR FLOW CONTROL OPTIMIZATION
Active flow control is an area of heightened interest in the aerospace community. Previous research on flow control design processes heavily depended on trial and error and the designers knowledge and intuition. Such an approach cannot always meet the growing demands of higher design quality in less time. Successful application of computational fluid dynamics (CFD) to this kind of control problem critically depends on an efficient searching algorithm for design optimization. CFD in conjunction with Genetic Algorithms (GA) potentially offers an efficient and robust optimization method and is a promising solution for current flow control designs. Current research has combined different existing GA techniques and motivation from the two-jet GA-CFD system previously developed at the University of Kentucky propose the applications of a real coded Continuous Genetic Algorithm (CGA) to optimize a four-jet and a synthetic jet control system on a NACA0012 airfoil. The control system is an array of jets on a NACA0012 airfoil and the critical parameters considered for optimization are the angle, the amplitude, the location, and the frequency of the jets. The design parameters of a steady four-jet and an unsteady synthetic jet system are proposed and optimized. The proposed algorithm is built on top of CFD code (GHOST), guiding the movement of jets along the airfoils upper surface. The near optimum control values are determined within the control parameter range. The current study of different Genetic Algorithms on airfoil flow control has been demonstrated to be a successful optimization application
Fuzzy logic controller parameter optimization using metaheuristic Cuckoo search algorithm for a magnetic levitation system
The main benefits of fuzzy logic control (FLC) allow a qualitative knowledge of the desired system’s behavior to be included as IF-THEN linguistic rules for the control of dynamical systems where either an analytic model is not available or is too complex due, for instance, to the presence of nonlinear terms. The computational structure requires the definition of the FLC parameters namely, membership functions (MF) and a rule base (RB) defining the desired control policy. However,
the optimization of the FLC parameters is generally carried out by means of a trial and error procedure or, more recently by using metaheuristic nature-inspired algorithms, for instance, particle swarm optimization, genetic algorithms, ant colony optimization, cuckoo search, etc. In this regard, the cuckoo search (CS) algorithm as one of the most promising and relatively recent developed nature-inspired algorithms, has been used to optimize FLC parameters in a limited variety of applications to determine the optimum FLC parameters of only the MF but not to the RB, as an extensive search in the literature has shown. In this paper, an optimization procedure based on the CS algorithm is presented to optimize all the parameters of the FLC, including the RB, and it is applied to a nonlinear magnetic
levitation system. Comparative simulation results are provided to validate the features improvement of such an approach which can be extended to other FLC based control systems.Peer ReviewedPostprint (published version
Comparison of Metaheuristic Optimization Algorithms for Quadrotor PID Controllers
In the present study, different solution methods are discussed in order to control the quadrotor with the most optimal PID parameters for the determined purposes. One of these methods is to make use of meta-heuristic algorithms in control systems. There are some limitations of using a PID controller as a classical construct. However, it is thought that more successful results will be obtained by optimizing its parameters through meta-heuristic algorithms. Initially, the mathematical model of the vehicle was created in MATLAB/Simulink. Then, genetic algorithms (GA), artificial bee colony (ABC), particle swarm optimization (PSO) and firefly algorithms (FA) were determined respectively as optimization methods. And these optimization methods used to determine the PID control parameters are applied to the developed mathematical model in the MATLAB/Simulink environment. In addition, the performances of the optimization methods are evaluated according to the comparison criteria. As a result of the comparison carried out according to ITAE (Integral Time Absolute Error) fitness criteria, ABC (1.2% - 4.4%) in terms of altitude, FA (4% - 13%) in terms of roll angle, GA (13% - %21) in terms of pitch angle, and PSO (4% - %15) in terms of yaw angle has been more successful than other methods
The modelling and control of the drive system of an Ackermann Robot using GA optimization
This paper provides the mathematical modelling and control optimization, of the drive system of an Ackermann four wheeled autonomous robot, with Genetic algorithm used for tuning the proportional, integral and derivative (PID) Controller parameters. The aim and main objective of this work is focused on the control of the driving speed input from the rear wheels of the robot and control. The robot drive in proportion to obstacle input ahead of the four wheeled chassis using genetic algorithms. A controlled platform that can be deployed for driverless vehicle in the nearest future and military unmanned vehicle is our major concern. The controlled system response stabilized in 0.675 seconds, after exciting the system with a step response. Variation for the system also shows, that the cost function was minimized or adjusted to obtain optimal PID parameters as Proportional (P) = 12.671, Integral (I) = -0.399, Derivative (D) = 1477561, at a value of 9.6778*10-4.Keywords: Ackermann steering, modelling, optimization, Genetic Algorithm, control, PID, driverless vehicl
Constrained Nonlinear Model Predictive Control of an MMA Polymerization Process via Evolutionary Optimization
In this work, a nonlinear model predictive controller is developed for a
batch polymerization process. The physical model of the process is
parameterized along a desired trajectory resulting in a trajectory linearized
piecewise model (a multiple linear model bank) and the parameters are
identified for an experimental polymerization reactor. Then, a multiple model
adaptive predictive controller is designed for thermal trajectory tracking of
the MMA polymerization. The input control signal to the process is constrained
by the maximum thermal power provided by the heaters. The constrained
optimization in the model predictive controller is solved via genetic
algorithms to minimize a DMC cost function in each sampling interval.Comment: 12 pages, 9 figures, 28 reference
Criteria for identifying failure optimization algorithms in building energy optimization and case studies
Optimization algorithms plays a vital role in the Building Energy Optimization (BEO) technique. Although many algorithms are currently used in BEO, it is difficult to find an algorithm that performs well for all optimization problems. Some algorithms may fail in some cases. This study specifically focuses on failure algorithms in BEO and the possible causes. Several criteria are proposed for identifying failure algorithms. Four optimization problems base d on the DOE small and large office buildings are developed. Three commonly used algorithms in BEO, namely, Pattern Search (PS ) algorithm, Genetic Algorithm (GA ) and Particle Swarm Optimization (PSO) algorithm, are applied to the four problems to investigate possible rea sons for their failure. Results indicate that the effectiveness of the three selected algorithms is highly dependent on the optimization problems to be addressed. Besides, the control parameter setting of the PS algorithm appears to be a significant factor that may cause the algorithm to lose effectiveness. However, it does not seem to be the main reason for the failure of the GA and PSO algorithm. In General, the results gained from this study can deepen our understanding of optimization algorithms used in BEO. Besides, understanding the reasons why optimization algorithms are ineffective can help architects, engineers, and consultants select the appropriate optimization algorithms and set their parameters to achieve a better BEO design that is less vulnerable to failure
Optimal PSO for Collective Robotic Search Applications
Unmanned vehicles/mobile robots are of particular interest in target tracing applications since there are many areas where a human cannot explore. Different means of control have been investigated for unmanned vehicles with various algorithms like genetic algorithms, evolutionary computations, neural networks etc. This work presents the application of particle swarm optimization (PSO) for collective robotic search. The performance of the PSO algorithm depends on various parameters called quality factors and these parameters are determined using a secondary PSO. Results are presented to show that the performance of PSO algorithm and search is improved for a single and multiple target searches
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