A memetic algorithm applied to trajectory control by tuning of Fractional Order Proportional-Integral-Derivative controllers

Mousavi, Yashar; Alfi, Alireza

doi:10.1016/j.asoc.2015.08.009

Cited by 84 publications

(38 citation statements)

References 59 publications

(91 reference statements)

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“…2). According to the maximal local density (max i γ i k ) the data sample is associated with that cloud and furthermore, the parameters of that cloud are updated using equations (8) and (9). Theoretically, it is possible to happen that the current data sample has the same density to two or more clouds.…”

Section: Robust Evolving Cloud-based Controller (Recco) a The Stmentioning

confidence: 99%

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A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

Andonovski

Angelov

Blažič

et al. 2016

Applied Soft Computing

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The RECCo control algorithm, presented in this article, is based on the fuzzy rule-based (FRB) system named ANYA which has non-parametric antecedent part. It starts with zero fuzzy rules (clouds) in the rule base and evolves its structure while performing the control of the plant. For the consequent part of RECCo PID-type controller is used and the parameters are adapted in an online manner. The RECCo does not require any off-line training or any type of model of the controlled process (e.g. differential equations). Moreover, in this article we propose a normalization of the cloud (data) space and an improved adaptation law of the controller. Due to the normalization some of the evolving parameters can be fixed while the new adaptation law improves the performance of the controller in the starting phase of the process control. To assess the performance of the RECCo algorithm, firstly an comparison study with classical PID controller was performed on a model of a plate heat-exchanger (PHE). Tunning the PID parameters was done using three different techniques (Ziegler-Nichols, Cohen-Coon and pole placement). Furthermore, a practical implementation of the RECCo controller for a real PHE plant is presented. The PHE system has nonlinear static characteristic and a time delay. Additionally, the real sensor's and actuator's limitations represent a serious problem from the control point of view. Besides this, the RECCo control algorithm autonomously learns and evolves the structure and adapts its parameters in an online unsupervised manner.

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Section: Robust Evolving Cloud-based Controller (Recco) a The Stmentioning

confidence: 99%

“…Another type of PID controllers are Fractional Order PID (FO PID) controllers that perform better than a classical PID-s [6] but require setting of two additional parameters. Similar to classical ones, tunning of this parameters can be solved by solving an optimization problem [7], [8], [9].…”

Section: Introductionmentioning

confidence: 99%

A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

Andonovski

Angelov

Blažič

et al. 2016

Applied Soft Computing

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“…As one of the most well-known and preferred EAs, particle swarm optimisation (PSO) has been rapidly and widely applied to solve different single-objective and MOO problems in recent years: a novel adaptive particle swarm optimisation (APSO) algorithm was developed by Alireza and Hamidreza in [15]; Yashar and Alireza proposed a novel fractional PSO-based memetic algorithm (FPSOMA) to solve trajectory control in [16]; a bare-bones multi-objective PSO for the environmental/economic dispatch problem was developed in [17]; a modified binary PSO-based reliability redundancy allocation method was introduced in [18] and a hybrid PSO-based MOO method was proposed to handle the flexible job-shop scheduling problem in [19]. For more works focusing on developing different PSO-based MOO methods, the reader can be referred to [5,20,21,22].…”

Section: Introductionmentioning

confidence: 99%

“…It is of great importance to overcome this convergence issue to improve the quality of the Pareto front and consequently enhance the performance of MOO [5,17]. There have been numerous researches focusing on overcoming the typical drawback of the basic PSO [9,23,24,25,16,26]. From these studies, it is clearly evident that adjusting the three control parameters, i.e., the inertial weight, the cognitive and social acceleration parameters, is a powerful remedy to the convergence issue in PSO.…”

Section: Introductionmentioning

confidence: 99%

A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm

Tang

Zhu

Shin

et al. 2017

Information Sciences

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This paper develops a particle swarm optimisation (PSO) based framework for multi-objective optimisation (MOO). As a part of development, a new PSO method, named self-adaptive PSO (SAPSO), is first proposed. Since the convergence of SAPSO determines the quality of the obtained Pareto front, this paper analytically investigates the convergence of SAPSO and provides a parameter selection principle that guarantees the convergence. Leveraging the proposed SAPSO, this paper then designs a SAPSO-based MOO framework, named SAMOPSO. To gain a welldistributed Pareto front, we also design an external repository that keeps the non-dominated solutions. Next, a circular sorting method, which is integrated with the elitist-preserving approach, is designed to update the external repository in the developed MOO framework. The performance of the SAMOPSO framework is validated through 12 benchmark test functions and a real-word MOO problem. For rigorous validation, the performance of the proposed framework is compared with those of four well-known MOO algorithms. The simulation results confirm that the proposed SAMOPSO outperforms its contenders with respect to the quality of the Pareto front over the majority of the studied cases. The non-parametric comparison results reveal that the proposed method is significantly better than the four algorithms compared at the confidence level of 90% over the 12 test functions.

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“…[20][21][22] Due to the simple concept, easy implementation, and quick convergence, nowadays PSO has been developed and applied in various fields, especially for wide-ranging optimization problems, 23,24 and then based on fractional PSO, a novel MA FPSOMA is introduced to solve optimization problem using fractional calculus concept. 25 Moreover, a variety of evolutionary algorithms are employed to obtain optimal performance for synchronization of bilateral teleoperation systems against uncertainties including model parameters. 26 Actually, parameter identification for piezoelectric positioning system could be essentially formulated as a multidimensional optimization problem; however, only a few applications of piezoelectric positioning system opted for PSO.…”

Section: Introductionmentioning

confidence: 99%

Parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system based on the improved particle swarm optimization method

Xie

Chen

2017

Advances in Mechanical Engineering

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An improved particle swarm optimization method that has better equilibrium characteristic between global search and local search is proposed for parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system. In this article, a general electromechanical-based piezoelectric positioning systems' hysteresis nonlinear dynamic model suitable for engineering optimization is introduced, and a parameter identification technique based on improved particle swarm optimization for this dynamic model is presented. For increasing the speed and precision of identification process, the hysteresis effect is described by the generalized Prandtl-Ishlinskii model, and the symmetric solutions of the system are applied to solve the hysteresis nonlinear dynamics equations and then the working process of the improved particle swarm optimization method is given. In order to verify the effectiveness of the proposed method, comparisons between experimental data and simulation data are carried out. The results demonstrate that improved particle swarm optimization method can make the hysteresis nonlinear dynamic model with identified parameters fit the experimental data with higher precision, and since the application of the symmetric solutions, the identification time is greatly reduced. Therefore, the improved particle swarm optimization method is an optional effective technique for identifying the parameters of hysteresis nonlinear dynamic model for the piezoelectric positioning system.

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A memetic algorithm applied to trajectory control by tuning of Fractional Order Proportional-Integral-Derivative controllers

Cited by 84 publications

References 59 publications

A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

A practical implementation of Robust Evolving Cloud-based Controller with normalized data space for heat-exchanger plant

A framework for multi-objective optimisation based on a new self-adaptive particle swarm optimisation algorithm

Parameter identification of hysteresis nonlinear dynamic model for piezoelectric positioning system based on the improved particle swarm optimization method

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