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Article

Fouad Allouani, Djamel Boukhetala, Fares Boudjema and Gao Xiao-Zhi

The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which…

Abstract

Purpose

The two main purposes of this paper are: first, the development of a new optimization algorithm called GHSACO by incorporating the global-best harmony search (GHS) which is a stochastic optimization algorithm recently developed, with the ant colony optimization (ACO) algorithm. Second, design of a new indirect adaptive recurrent fuzzy-neural controller (IARFNNC) for uncertain nonlinear systems using the developed optimization method (GHSACO) and the concept of the supervisory controller.

Design/methodology/approach

The novel optimization method introduces a novel improvization process, which is different from that of the GHS in the following aspects: a modified harmony memory representation and conception. The use of a global random switching mechanism to monitor the choice between the ACO and GHS. An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The developed optimization method is applied for parametric optimization of all recurrent fuzzy neural networks adaptive controller parameters. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the IARFNNC global structure.

Findings

First, to analyze the performance of GHSACO method and shows its effectiveness, some benchmark functions with different dimensions are used. Simulation results demonstrate that it can find significantly better solutions when compared with the Harmony Search (HS), GHS, improved HS (IHS) and conventional ACO algorithm. In addition, simulation results obtained using an example of nonlinear system shows clearly the feasibility and the applicability of the proposed control method and the superiority of the GHSACO method compared to the HS, its variants, particle swarm optimization, and genetic algorithms applied to the same problem.

Originality/value

The proposed new GHS algorithm is more efficient than the original HS method and its most known variants IHS and GHS. The proposed control method is applicable to any uncertain nonlinear system belongs in the class of systems treated in this paper.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 8 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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Article

Junfei Qiao, Gaitang Han, Honggui Han and Wei Chai

The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.

Abstract

Purpose

The purpose of this paper is to present an on-line modeling and controlling scheme based on the dynamic recurrent neural network for wastewater treatment system.

Design/methodology/approach

A control strategy based on rule adaptive recurrent neural network (RARFNN) is proposed in this paper to control the dissolved oxygen (DO) concentration and nitrate nitrogen (SNo) concentration. The structure of the RARFNN is self-organized by a rule adaptive algorithm, and the rule adaptive algorithm considers the overall information processing ability of neural network. Furthermore, a stability analysis method is given to prove the convergence of the proposed RARFNN.

Findings

By application in the control problem of wastewater treatment process (WWTP), results show that the proposed control method achieves better performance compared to other methods.

Originality/value

The proposed on-line modeling and controlling method uses the RARFNN to model and control the dynamic WWTP. The RARFNN can adjust its structure and parameters according to the changes of biochemical reactions and pollutant concentrations. And, the rule adaptive mechanism considers the overall information processing ability judgment of the neural network, which can ensure that the neural network contains the information of the biochemical reactions.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

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Article

Chih-Ming Hong, Cong-Hui Huang and Fu-Sheng Cheng

This paper aims to present the analysis, design and implementation of functional link-based recurrent fuzzy neural network (FLRFNN) for the control of variable-speed…

Abstract

Purpose

This paper aims to present the analysis, design and implementation of functional link-based recurrent fuzzy neural network (FLRFNN) for the control of variable-speed switched reluctance generator (SRG).

Design/methodology/approach

The node connecting weights of the FLRFNN are trained online by back-propagation (BP) algorithms. The proposed estimator requires less processing time than traditional methods and can be fully implemented using a low-cost digital signal processor (DSP) with MATLAB toolboxes. The DSP-based hybrid sensor presented in this paper can be applied to a wind energy-conversion system where the SRG is used as a variable-speed generator. The current transducer is used to monitor the energized current and proximity sensors for rotor salient.

Findings

The authors have found that optimal based on FLRFNN with Grey controller can resolve the regulation of the system with uncertainty model and unknown disturbances. This technique can maintain the system stability and reach the desired performance even with parameter uncertainties.

Originality/value

This design will improve the performance of SRG to operate more smoothly. This application is currently being studied because the SRG has well-known advantages such as robustness, low manufacturing cost and good size-to-power ratio. Performance of the proposed controller can offer better stability characteristics. Finally, the SRG has a very good efficiency in the whole operating range.

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Article

Jafar Tavoosi

The purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor.

Abstract

Purpose

The purpose of this paper is to present a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor.

Design/methodology/approach

A novel recurrent radial basis function network (RBFN) is used to is used to approximate unknown nonlinear functions in permanent magnet synchronous motor (PMSM) dynamics. Then, using the functions obtained from the neural network, it is possible to design a model-based and precise controller for PMSM using the immersive modeling method.

Findings

Experimental results indicate the appropriate performance of the proposed method.

Originality/value

This paper presents a novel intelligent backstepping sliding mode control for an experimental permanent magnet synchronous motor. A novel recurrent RBFN is used to is used to approximate unknown nonlinear functions in PMSM dynamics.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering , vol. 39 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

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Article

A. Boucheta, I.K. Bousserhane, A. Hazzab, B. Mazari and M.K. Fellah

The purpose of this paper is to propose mover position control of linear induction motor (LIM) using an adaptive backstepping approach based on field orientation.

Abstract

Purpose

The purpose of this paper is to propose mover position control of linear induction motor (LIM) using an adaptive backstepping approach based on field orientation.

Design/methodology/approach

First, the indirect field‐oriented control LIM is derived. Then, an adaptive backstepping approach based on field‐oriented control of LIM is proposed to compensate the uncertainties which occur in the control. Mover position amplitude tracking objective is formulated, under the assumption of unknown total mass of the moving element, viscous friction, and load force, so that the position regulation is achieved.

Findings

The effectiveness and robustness of the proposed control scheme are verified by numerical simulation using Matlab/Simulink model. The numerical validation results of the proposed scheme have presented good transient control performances and robustness to uncertainties compared to the conventional backstepping control design.

Originality/value

The paper presents an adaptive backstepping approach for LIM control that achieves mover position amplitude tracking objective under mechanical parameter variation.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 29 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

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Article

Chi-Chung Chen, Li Ping Shen, Chien-Feng Huang and Bao-Rong Chang

The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization…

Abstract

Purpose

The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design.

Design/methodology/approach

The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to approach the best solution. The operations of assimilation, accommodation, and mutation in Mutual Learning scheme are used for the middle-ranked group to exchange and accommodate the partial information between groups and, globally, search information. Only the N top-best-performance solutions are reserved after each iteration of learning.

Findings

The proposed algorithm outperforms some reported ACO algorithms for the fuzzy system design with the same number of rules. The performance comparison with various previously published neural fuzzy systems also shows its superiority even with a smaller number of fuzzy rules to those neural fuzzy systems.

Research limitations/implications

Future work will consider the application of the proposed ACACO to the recurrent fuzzy network.

Originality/value

The originality of this work is to mix the work of the well-known psychologist Jean Piaget and the continuous ACO to propose a new population-based optimization algorithm whose superiority is demonstrated.

Details

Engineering Computations, vol. 33 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

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Article

Yakup Demir and Ayşegül Uçar

Recently, the modelling and simulation of switched systems containing new nonlinear components in electronics and power electronics industry have gained importance. In…

Abstract

Recently, the modelling and simulation of switched systems containing new nonlinear components in electronics and power electronics industry have gained importance. In this paper, both feed‐forward artificial neural networks (ANN) and adaptive network‐based fuzzy inference systems (ANFIS) have been applied to switched circuits and systems. Then their performances have been compared in this contribution by developed simulation programs. It has been shown that ANFIS require less training time and offer better performance than those of ANN. In addition, ANFIS using “clustering algorithm” to generate the rules and the numbers of membership functions gives a smaller number of parameters, better performance and less training time than those of ANFIS using “grid partition” to generate the rules. The work not only demonstrates the advantage of the ANFIS architecture using clustering algorithm but also highlights the advantages of the architecture for hardware realizations.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 22 no. 2
Type: Research Article
ISSN: 0332-1649

Keywords

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Article

Xianzhi Jiang, Zenghuai Wang, Chao Zhang and Liangliang Yang

– The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN).

Abstract

Purpose

The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN).

Design/methodology/approach

The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot.

Findings

The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration.

Research limitations/implications

This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online.

Originality/value

Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.

Details

Industrial Robot: An International Journal, vol. 42 no. 1
Type: Research Article
ISSN: 0143-991X

Keywords

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Article

Soheil Ganjefar and Mojtaba Alizadeh

The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low…

Abstract

Purpose

The power system is complex multi‐component dynamic system with many operational levels made up of a wide range of energy sources with many interaction points. Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie lines. These oscillations may sustain and grow to cause system separation if no adequate damping is available. The present paper aims to propose an on‐line self‐learning PID (OLSL‐PID) controller in order to damp the low frequency power system oscillations in a single‐machine system.

Design/methodology/approach

The proposed OLSL‐PID is used as a controller in order to damp the low frequency power system oscillations. It has a local nature because of its powerful adaption process based on back‐propagation (BP) algorithm that is implemented through an adaptive self‐recurrent wavelet neural network identifier (ASRWNNI). In fact PID controller parameters are updated in on‐line mode, using BP algorithm based on the information provided by the ASRWNNI which is a powerful fast‐acting identifier because of its local nature, self‐recurrent structure and stable training algorithm with ALRs based on discrete lyapunov stability theorem.

Findings

The proposed control scheme is applied to a single machine infinite bus power system under different operating conditions and disturbances. The nonlinear time‐domain simulation results are promising and show the effectiveness and robustness of the proposed controller and also reveal that: because of the high adaptability, the local behavior and high flexibility of the OLSL‐PID controller, it can be damp the low frequency oscillations in the best possible manner and significantly improves the stability performance of the system.

Originality/value

The proposed controller adaption process is done in each sampling period using a powerful adaption law based on BP algorithm. Also during the process the system sensitivity is provided by a powerful fast‐acting identifier. As an alternative to multi‐layer perceptron neural network, self‐recurrent wavelet neural networks (SRWNNs) which combine the properties such as attractor dynamics of recurrent neural network and the fast convergence of the wavelet neural network were proposed to identify synchronous generator. Also to help the OLSL‐PID stability first, PID parameters tuning problem under a wide range of operating conditions is converted to an optimization problem which solved by a chaotic optimization algorithm (COA), and afterwards PID controller is hooked up in the system and on‐line tuning is done in each sampling period.

Details

COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 31 no. 6
Type: Research Article
ISSN: 0332-1649

Keywords

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Article

Hojjat Adeli

This paper reviews innovative research done during the past few years on automatic detection of traffic incidents by the author and his associates using data obtained from…

Abstract

This paper reviews innovative research done during the past few years on automatic detection of traffic incidents by the author and his associates using data obtained from sensors embedded in intelligent freeways. A multi‐paradigm intelligent system approach is employed to solve the complicated and chaotic pattern recognition problem using neural networks, fuzzy logic, and wavelets. Wavelet‐based de‐noising and feature extraction techniques are employed to eliminate undesirable fluctuations in observed data from traffic sensors. The result is reliable algorithms with high incident detection and very low false alarm rates.

Details

Sensor Review, vol. 22 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

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