SOSPD Controllers Tuning by Means of an Evolutionary Algorithm

2014 ◽  
Vol 4 (2) ◽  
pp. 40-58 ◽  
Author(s):  
Jesús-Antonio Hernández-Riveros ◽  
Jorge-Humberto Urrea-Quintero
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Operating Mode ◽  
Performance Criterion ◽  
Order System ◽  
Optimal Tuning ◽  
Current Trends ◽  
Controlling Processes ◽  
Better Than

The Proportional Integral Derivative (PID) controller is the most widely used industrial device to monitoring and controlling processes. There are numerous methods for estimating the controller parameters, in general, resolving particular cases. Current trends in parameter estimation minimize an integral performance criterion. Therefore, the calculation of the controller parameters is proposed as an optimization problem. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. In this paper, the evolutionary algorithm MAGO is used as a tool to optimize the controller parameters. The procedure is applied to a range of standard plants modeled as a Second Order System plus Time Delay. Better results than traditional methods of optimal tuning, regardless of the operating mode of the controller, are yielded.

SOSPD Controllers Tuning by Means of an Evolutionary Algorithm

2015 ◽  
pp. 856-873 ◽  
Author(s):  
Jesús-Antonio Hernández-Riveros ◽  
Jorge-Humberto Urrea-Quintero
Keyword(s):  
Parameter Estimation ◽  
Time Delay ◽  
Evolutionary Algorithm ◽  
Heuristic Algorithms ◽  
Operating Mode ◽  
Performance Criterion ◽  
Order System ◽  
Optimal Tuning ◽  
Current Trends ◽  
Controlling Processes

The Proportional Integral Derivative (PID) controller is the most widely used industrial device to monitoring and controlling processes. There are numerous methods for estimating the controller parameters, in general, resolving particular cases. Current trends in parameter estimation minimize an integral performance criterion. Therefore, the calculation of the controller parameters is proposed as an optimization problem. Although there are alternatives to the traditional rules of tuning, there is not yet a study showing that the use of heuristic algorithms it is indeed better than using the classic methods of optimal tuning. In this paper, the evolutionary algorithm MAGO is used as a tool to optimize the controller parameters. The procedure is applied to a range of standard plants modeled as a Second Order System plus Time Delay. Better results than traditional methods of optimal tuning, regardless of the operating mode of the controller, are yielded.

scholarly journals NEW HEURISTIC ALGORITHMS FOR ROLLING AIR FRAME AERODYNAMIC PARAMETERS ESTIMATION

Aviation ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Ayham Mohamad ◽  
Jalal Karimi ◽  
Alireza Naderi
Keyword(s):  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Parameters Estimation ◽  
Modified Particle Swarm Optimization ◽  
Smoothing Filter ◽  
Aerodynamic Parameters ◽  
New Strategies ◽  
Better Than

In this research, based on heuristic optimization algorithms, three new strategies are developed for Aerodynamic Parameters Estimation (APE) of one pair ON-OFF actuator rolling airframe. In the 1st method namely EAM-PSO the aerodynamic parameters are directly estimated. While, the next two algorithms called EBM-PSO and SEBM-PSO are two-step strategies. In the 1st step the aerodynamic forces and moments are estimated, then after passing through a designed smoothing filter, in the 2nd step aerodynamic parameters are estimated. In EBM-PSO all the aerodynamic parameters are estimated at once by solving one optimization problem. In SEBM-PSO the APE is converted to solve four separate optimization problems. A modified particle swarm optimization algorithm is developed and used in estimation process. The performance of proposed algorithms is compared with that of state of the art algorithm EKF. The simulation results show that SEBM-PSO and EBM-PSO are better than EAM-PSO in term of accuracy and run time.

Accuracy improvement of 6-UPS Stewart forward kinematics solution based on self-aggregating MFO algorithm

2021 ◽  
Vol 40 (5) ◽  
pp. 8831-8846
Author(s):  
Wanzheng Liu ◽  
Ying Xu ◽  
Meng Shao ◽  
Guodong Yue ◽  
Dong An
Keyword(s):  
Positive Solution ◽  
Optimization Model ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Forward Kinematics ◽  
Local Optimum ◽  
Solution Quality ◽  
Moth Flame Optimization Algorithm ◽  
Global And Local ◽  
Better Than

In this paper, a Stewart’s positive solution optimization model is proposed, for obtaining the complex solution to a Stewart’s forward kinematics problem, considering the existence of multiple solutions. The model converts the positive kinematics problem into an optimization problem, in which the value of the objective function is used to represent the precision of Stewart’s positive solution. A self-aggregating moth–flame optimization algorithm (SMFO) is used to improve the accuracy of Stewart’s forward kinematics solution. Two features were added to the conventional MFO algorithm to obtain a more stable balance between global and local explorations. First, Gaussian distribution was used for the flame population to select suitable individuals for Levy Flight operation, increase the diversity of the population, and enhance the algorithm’s ability to jump out of a local optimum. Second, in the middle and late iterations, the positions of the flames were periodically adjusted using the light intensity-attraction characteristic (LIAC) to strengthen the connection between individual flames and enhance the local exploration ability of the algorithm. The proposed SMFO algorithm is compared with three classic meta-heuristic algorithms for eight benchmark functions. Experimental results indicate that the SMFO algorithm is significantly better than the other three algorithms in terms of solution quality and convergence rate. To verify the effectiveness of the SMFO algorithm in solving the Stewart positive kinematics optimization model, values of eight sets of conventional position and posture parameters as well as limiting position and posture parameters were randomly obtained, and values of 16 sets of position and posture parameters were obtained using four algorithms. The results indicate that the SMFO algorithm can improve the accuracy of the forward kinematics solution to 4.05E-09 mm.

scholarly journals Automatic Design of Heuristic Algorithms for Binary Optimization Problems

2021 ◽  
Author(s):  
Marcelo de Souza
Keyword(s):  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
State Of The Art ◽  
Algorithm Design ◽  
Automatic Design ◽  
Binary Optimization ◽  
Automatic Algorithm ◽  
Better Than ◽  
Design Techniques

In this work we present AutoBQP, a heuristic solver for binary optimization problems. It applies automatic algorithm design techniques to search for the best heuristics for a given optimization problem. Experiments show that the solver can find algorithms which perform better than or comparable to state-of-the-art methods, and can even find new best solutions for some instances of standard benchmark sets.

Modified Posicast Control Design Method Based on the Parameters of a Fractional Order System

2021 ◽  
Vol 143 (4) ◽  
Author(s):  
Erhan Yumuk ◽  
Müjde Güzelkaya ◽  
İbrahim Eksin
Keyword(s):  
Real Time ◽  
Fractional Order ◽  
Control Method ◽  
Design Method ◽  
Order System ◽  
Half Cycle ◽  
Empirical Formulas ◽  
Real Time System ◽  
Novel Design ◽  
Better Than

Abstract In this study, a novel design method for half-cycle and modified posicast controller structures is proposed for a class of the fractional order systems. In this method, all required design variable values, namely, the input step magnitudes and their application times are obtained as functions of fractional system parameters. Moreover, empirical formulas are obtained for the overshoot values of the compensated system with half-cycle and modified posicast controllers designed utilizing this method. The proposed design methodology has been tested via simulations and ball balancing real-time system. It is observed that the derived formulas are in coherence with outcomes of the simulation and real-time application. Furthermore, the performance of modified posicast controller designed using proposed method is much better than other posicast control method.

An improved sparrow search algorithm based on levy flight and opposition-based learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Danni Chen ◽  
JianDong Zhao ◽  
Peng Huang ◽  
Xiongna Deng ◽  
Tingting Lu
Keyword(s):  
Parameter Optimization ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Convergence Speed ◽  
Support Vector ◽  
Lévy Flight ◽  
Levy Flight ◽  
Content Type ◽  
Opposition Based Learning

Purpose Sparrow search algorithm (SSA) is a novel global optimization method, but it is easy to fall into local optimization, which leads to its poor search accuracy and stability. The purpose of this study is to propose an improved SSA algorithm, called levy flight and opposition-based learning (LOSSA), based on LOSSA strategy. The LOSSA shows better search accuracy, faster convergence speed and stronger stability. Design/methodology/approach To further enhance the optimization performance of the algorithm, The Levy flight operation is introduced into the producers search process of the original SSA to enhance the ability of the algorithm to jump out of the local optimum. The opposition-based learning strategy generates better solutions for SSA, which is beneficial to accelerate the convergence speed of the algorithm. On the one hand, the performance of the LOSSA is evaluated by a set of numerical experiments based on classical benchmark functions. On the other hand, the hyper-parameter optimization problem of the Support Vector Machine (SVM) is also used to test the ability of LOSSA to solve practical problems. Findings First of all, the effectiveness of the two improved methods is verified by Wilcoxon signed rank test. Second, the statistical results of the numerical experiment show the significant improvement of the LOSSA compared with the original algorithm and other natural heuristic algorithms. Finally, the feasibility and effectiveness of the LOSSA in solving the hyper-parameter optimization problem of machine learning algorithms are demonstrated. Originality/value An improved SSA based on LOSSA is proposed in this paper. The experimental results show that the overall performance of the LOSSA is satisfactory. Compared with the SSA and other natural heuristic algorithms, the LOSSA shows better search accuracy, faster convergence speed and stronger stability. Moreover, the LOSSA also showed great optimization performance in the hyper-parameter optimization of the SVM model.

scholarly journals An Innovative Optimization Design Procedure for Mechatronic Systems with a Multi-Criteria Formulation

2021 ◽  
Vol 11 (19) ◽  
pp. 8900
Author(s):  
Cuauhtémoc Morales-Cruz ◽  
Marco Ceccarelli ◽  
Edgar Alfredo Portilla-Flores
Keyword(s):  
Optimization Design ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Design Procedure ◽  
Concurrent Design ◽  
Mechatronic Systems ◽  
Design Variables ◽  
One Step ◽  
Task Oriented ◽  
And Task

This paper presents an innovative Mechatronic Concurrent Design procedure to address multidisciplinary issues in Mechatronics systems that can concurrently include traditional and new aspects. This approach considers multiple criteria and design variables such as mechanical aspects, control issues, and task-oriented features to formulate a concurrent design optimization problem that is solved using but not limited to heuristic algorithms. Furthermore, as an innovation, this procedure address all considered aspects in one step instead of multiple sequential stages. Finally, this work discusses an example referring to Mechatronic Design to show the procedure performed and the results show its capability.

scholarly journals Heliostat Field Layout Optimization with Evolutionary Algorithms

10.29007/7p6t ◽  
2018 ◽  
Author(s):  
Pascal Richter ◽  
David Laukamp ◽  
Levin Gerdes ◽  
Martin Frank ◽  
Erika Ábrahám
Keyword(s):  
Power Plant ◽  
Convex Optimization ◽  
Evolutionary Algorithms ◽  
Convergence Rate ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Energy Supply ◽  
Optimization Problem ◽  
Convex Optimization Problem ◽  
New Genotype

The exploitation of solar power for energy supply is of increasing importance. While technical development mainly takes place in the engineering disciplines, computer science offers adequate techniques for optimization. This work addresses the problem of finding an optimal heliostat field arrangement for a solar tower power plant.We propose a solution to this global, non-convex optimization problem by using an evolutionary algorithm. We show that the convergence rate of a conventional evolutionary algorithm is too slow, such that modifications of the recombination and mutation need to be tailored to the problem. This is achieved with a new genotype representation of the individuals.Experimental results show the applicability of our approach.

scholarly journals Design of SkSP-R Variables Sampling Plans

2015 ◽  
Vol 38 (2) ◽  
pp. 413-429 ◽  
Author(s):  
Muhammad Aslam ◽  
Saminathan Balamurali ◽  
Chi-Hyuck Jun ◽  
Batool Hussain
Keyword(s):  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Sampling Plan ◽  
Producer’S Risk ◽  
Sample Number ◽  
Sampling Plans ◽  
Average Sample Number ◽  
Nonlinear Optimization Problem ◽  
Better Than ◽  
Average Sample

In this paper, we present the designing of the skip-lot sampling plan including the re-inspection  called SkSP-R. The plan parameters of the proposed plan are determined through a  nonlinear optimization problem by minimizing the average sample number satisfying both the producer's risk and the consumer's risks. The proposed plan is shown to perform better than the existing sampling plans in terms of the average sample number. The application of the proposed plan is explained with the help of illustrative examples.

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