scholarly journals Optimization of nonlinear controller with an enhanced biogeography approach

2014 ◽  
Vol 4 (2) ◽  
pp. 77-87
Author(s):  
Mohammed Salem ◽  
Mohamed Fayçal Khelfi
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Systems ◽  
Pid Controller ◽  
Inverted Pendulum ◽  
Optimal Solution ◽  
Optimization Process ◽  
Nonlinear Controller ◽  
Local Optima ◽  
Nonlinear Controllers ◽  
Mass Spring

This paper is dedicated to the optimization of nonlinear controllers basing of an enhanced Biogeography Based Optimization (BBO) approach. Indeed, The BBO is combined to a predator and prey model where several predators are used with introduction of a modified migration operator to increase the diversification along the optimization process so as to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems. Simulations are carried out over a Mass spring damper and an inverted pendulum and has given remarkable results when compared to genetic algorithm and BBO. 

Design and robustness analysis of fuzzy PID controller for automatic voltage regulator system using genetic algorithm

2022 ◽  
pp. 014233122110667
Author(s):  
Tufan Dogruer ◽  
Mehmet Serhat Can
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Pid Controller ◽  
Robustness Analysis ◽  
Optimal Solution ◽  
Voltage Regulator ◽  
Fuzzy Pid ◽  
Multi Objective ◽  
Fuzzy Pid Controller ◽  
Automatic Voltage Regulator

In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes.

scholarly journals Pareto Design of Decoupled Sliding-Mode Controllers for Nonlinear Systems Based on a Multiobjective Genetic Algorithm

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
M. J. Mahmoodabadi ◽  
A. Bagheri ◽  
N. Nariman-zadeh ◽  
A. Jamali ◽  
R. Abedzadeh Maafi
Keyword(s):  
Genetic Algorithm ◽  
Nonlinear Systems ◽  
Fourth Order ◽  
Inverted Pendulum ◽  
Sliding Mode ◽  
Sliding Mode Controller ◽  
Objective Functions ◽  
Software Environment ◽  
Multiobjective Genetic Algorithm ◽  
Sliding Mode Controllers

This paper presents Pareto design of decoupled sliding-mode controllers based on a multiobjective genetic algorithm for several fourth-order coupled nonlinear systems. In order to achieve an optimum controller, at first, the decoupled sliding mode controller is applied to stablize the fourth-order coupled nonlinear systems at the equilibrium point. Then, the multiobjective genetic algorithm is applied to search the optimal coefficients of the decoupled sliding-mode control to improve the performance of the control system. Considered objective functions are the angle and distance errors. Finally, the simulation results implemented in the MATLAB software environment are presented for the inverted pendulum, ball and beam, and seesaw systems to assure the effectiveness of this technique.

Developments in Nonlinear Controller Synthesis: An Overview

1978 ◽  
Vol 100 (1) ◽  
pp. 59-69 ◽  
Author(s):  
Devendra P. Garg
Keyword(s):  
Functional Analysis ◽  
Nonlinear Systems ◽  
Performance Improvement ◽  
Performance Index ◽  
Controller Synthesis ◽  
Analysis Approach ◽  
Nonlinear Controller ◽  
System Synthesis ◽  
Synthesis Techniques ◽  
Nonlinear Controllers

In this paper developments in nonlinear controller synthesis techniques are surveyed. First, the use of functional analysis approach for system synthesis is discussed. Next, the application of intentional nonlinear controllers for performance improvement and optimization via performance index minimization is presented. This is followed by a discussion of signal stabilization using artificial dither. Finally, approaches are given for design of controllers to meet stability requirements for both single and multiple-loop nonlinear systems.

Local Optima Avoidance in GA Biclustering using Map Reduce

2016 ◽  
Vol 6 (1) ◽  
pp. 37-47
Author(s):  
Gowri R. ◽  
Rathipriya R.
Keyword(s):  
Gene Expression ◽  
Genetic Algorithm ◽  
Population Size ◽  
Optimal Solution ◽  
Search Space ◽  
Gene Expression Dataset ◽  
Map Reduce ◽  
Mapreduce Framework ◽  
Local Optima ◽  
Novel Approach

One of the prominent issues in Genetic Algorithm (GA) is premature convergence on local optima. This restricts the enhanced optimal solution searching in the entire search space. Population size is one of the influencing factors in Genetic Algorithm. Increasing the population size will improvise the randomized searching and maintains the diversity in the population. It also increases its computational complexity. Especially in GA Biclustering (GABiC), the search should be randomized to find more optimal patterns. In this paper, a novel approach for population setup in MapReduce framework is proposed. The maximal population is split into population sets, and these groups will proceed searching in parallel using MapReduce framework. This approach is attempted for biclustering the gene expression dataset in this paper. The performance of this proposed work seems promising on comparing its results with those obtained from previous hybridized optimization approaches. This approach will also handle data scalability issues and applicable to the big data biclustering problems.

scholarly journals A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Optimal Solution ◽  
Global Optimum ◽  
Optimization Techniques ◽  
Adaptive Mutation ◽  
Local Optima ◽  
3 Dimensional ◽  
Successive Generations

Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that is derived from comparing the fitness values of successive generations. We propose a novel pseudoderivative-based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Using computation to enhance diagnosis and therapy: a novel mutation operator for real-coded adaptive genetic algorithms

F1000Research ◽  
2013 ◽  
Vol 2 ◽  
pp. 139
Author(s):  
Maxinder S Kanwal ◽  
Avinash S Ramesh ◽  
Lauren A Huang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mutation Rate ◽  
Novel Mutation ◽  
Fitness Function ◽  
Genetic Disorders ◽  
Optimal Solution ◽  
Global Optimum ◽  
Adaptive Mutation ◽  
Local Optima

The fields of molecular biology and neurobiology have advanced rapidly over the last two decades. These advances have resulted in the development of large proteomic and genetic databases that need to be searched for the prediction, early detection and treatment of neuropathologies and other genetic disorders. This need, in turn, has pushed the development of novel computational algorithms that are critical for searching genetic databases. One successful approach has been to use artificial intelligence and pattern recognition algorithms, such as neural networks and optimization algorithms (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate based on the fitness function of passing generations. We propose a novel pseudo-derivative based mutation rate operator designed to allow a genetic algorithm to escape local optima and successfully continue to the global optimum. Once proven successful, this algorithm can be implemented to solve real problems in neurology and bioinformatics. As a first step towards this goal, we tested our algorithm on two 3-dimensional surfaces with multiple local optima, but only one global optimum, as well as on the N-queens problem, an applied problem in which the function that maps the curve is implicit. For all tests, the adaptive mutation rate allowed the genetic algorithm to find the global optimal solution, performing significantly better than other search methods, including genetic algorithms that implement fixed mutation rates.

scholarly journals Automatic Generation Control using Genetic Algorithm Based PID Controller

2020 ◽  
Vol 9 (5) ◽  
pp. 1047-1053
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Thermal Power ◽  
Optimal Solution ◽  
Automatic Generation ◽  
Automatic Generation Control ◽  
Parameter Uncertainties ◽  
Survival Of The Fittest ◽  
Generation Control ◽  
Plant Parameter

In this paper Automatic Generation Control (AGC) of a single-area thermal power plant without reheat turbine is introduced using a Proportional Integral Derivative (PID) controller. The gains of the controller are optimized using Genetic Algorithm (GA). The problem of tuning the PID controller is formulated as optimization problem with constraints on proportional, derivative and integral gains. The proposed algorithm uses Darwin’s law of natural selection and survival of the fittest to reach the optimal solution. The simulation results confirm the system’s ability to retain frequency while handling sudden load disturbances. The second part of the investigation includes robustness testing of the system against plant parameter variations. The results are verified and the system performance is found to be robust against parameter uncertainties

scholarly journals Robust Control of Uncertain Nonlinear Systems: A Nonlinear DOBC Approach

2016 ◽  
Vol 138 (7) ◽  
Author(s):  
Wen-Hua Chen ◽  
Jun Yang ◽  
Zhenhua Zhao
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Disturbance Observer ◽  
External Disturbance ◽  
Uncertain Nonlinear Systems ◽  
Nonlinear Controller ◽  
Nonlinear Disturbance Observer ◽  
Spring System ◽  
Mass Spring ◽  
Distinct Features

This paper advocates disturbance observer-based control (DOBC) for uncertain nonlinear systems. Within this framework, a nonlinear controller is designed based on the nominal model in the absence of disturbance and uncertainty where the main design specifications are to stabilize the system and achieve good tracking performance. Then, a nonlinear disturbance observer is designed to not only estimate external disturbance but also system uncertainty/unmodeled dynamics. With described uncertainty, rigorous stability analysis of the closed-loop system under the composite controller is established in this paper. Finally, the robust control problems of a missile roll stabilization and a mass spring system are addressed to illustrative the distinct features of the nonlinear DOBC approach.

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