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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

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

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Article

Cheng-De Zheng

The purpose of this paper is to develop a methodology for the stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and…

Abstract

Purpose

The purpose of this paper is to develop a methodology for the stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.

Design/methodology/approach

The authors perform Briat Lemma, multiple integral approach and linear convex combination technique to investigate a class of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay. New sufficient criterion is established by linear matrix inequalities conditions.

Findings

It turns out that the obtained methods are easy to be verified and result in less conservative conditions than the existing literature. Two examples show the effectiveness of the proposed results.

Originality/value

The novelty of the proposed approach lies in establishing a new Wirtinger-based integral inequality and the use of the Lyapunov functional method, Briat Lemma, multiple integral approach and linear convex combination technique for stochastically asymptotic stability of fuzzy Markovian jumping neural networks with time-varying delay and continuously distributed delay in mean square.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 11 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

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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

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

Lydie Myriam Marcelle Amelot, Ushad Subadar Agathee and Yuvraj Sunecher

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The…

Abstract

Purpose

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Design/methodology/approach

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

Findings

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

Research limitations/implications

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

Originality/value

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

Details

African Journal of Economic and Management Studies, vol. 12 no. 1
Type: Research Article
ISSN: 2040-0705

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

Chun‐Fei Hsu, Chien‐Jung Chiu and Jang‐Zern Tsai

The proportional‐integral‐derivative (PID) controller has been a practical application in industry due to its simple architecture, being easily designed and its parameter…

Abstract

Purpose

The proportional‐integral‐derivative (PID) controller has been a practical application in industry due to its simple architecture, being easily designed and its parameter tuning without complicated computation. However, the traditional PID controller usually needs some manual retuning before being used for practical application in industry. The purpose of this paper is to propose an auto‐tuning PID controller (ATPIDC) which can automatically tune the controller parameters based on the gradient descent method and the Lyapunov stability theorem. Finally, a field‐programmable gate array (FPGA) chip is adopted to implement the proposed ATPIDC scheme for possible low‐cost and high‐performance industrial applications, and it is applied to a DC servomotor to show its effectiveness.

Design/methodology/approach

To ensure the stability of the intelligent control system, a compensator usually should be designed. The most frequently used compensator is designed as a sliding‐mode control, which results in substantial chattering in the control effort. To tackle this problem, the proposed ATPIDC system is composed of a PID controller and a fuzzy compensator. The PID controller can automatically tune the gain factors of the controller gains based on the gradient descent method, and the fuzzy compensator is utilized to eliminate approximation error based on the Lyapunov stability theorem. The proposed fuzzy compensator not only can remove the chattering phenomena of conventional sliding‐mode control completely, but also can guarantee the stability of the closed‐loop system.

Findings

The proposed ATPIDC system is applied to a DC servomotor on a FPGA chip. The hardware implementation of the ATPIDC scheme is developed in a real‐time mode. Using the FPGA to implement, the ATPIDC system can achieve the characteristics of small size, fast execution speed and less memory. A comparison among the fuzzy sliding‐mode control, adaptive robust PID control and the proposed ATPIDC is made. Experimental results verify a better position tracking response can be achieved by the proposed ATPIDC method after control parameters training.

Originality/value

The proposed ATPIDC approach is interesting for the design of an intelligent control scheme. An on‐line parameter training methodology, using the gradient descent method and the Lyapunov stability theorem, is proposed to increase the learning capability. The experimental results verify the system stabilization, favorable tracking performance and no chattering phenomena can be achieved by using the proposed ATPIDC system. Also, the proposed ATPIDC methodology can be easily extended to other motors.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 4 no. 1
Type: Research Article
ISSN: 1756-378X

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

A. Omran, A. Kassem, G. El‐Bayoumi and M. Bayoumi

The purpose of this paper is to show the merit of using mission information in tuning the controller gains for Stewart manipulator instead of the generic inputs previously…

Abstract

Purpose

The purpose of this paper is to show the merit of using mission information in tuning the controller gains for Stewart manipulator instead of the generic inputs previously developed in literature.

Design/methodology/approach

The paper introduces two optimization techniques based on mission information. The first technique, a partial‐information technique, uses gain scheduling that applies different controllers for different mission tracks. The second technique, a full‐information technique uses a single robust controller by considering the full mission data. For demonstrating these techniques' feasibility, a nonlinear numerical simulation for a Stewart manipulator was built and tested using a generic mission. This mission consists of two piecewise trajectories (tracks). The proposed techniques were compared with one of the previous optimization techniques in literature, no‐information technique, in which a step response is used to search for optimal controller gains without any information about the mission. Genetic algorithms were used to search for the optimal controller gain in each case with different cost functions.

Findings

Based on the numerical simulations, the proposed mission‐based optimization techniques have superior performances compared with no‐information technique.

Research limitations/implications

The proposed techniques were applied in a joint space or for a decentralized control. The work can be extended to be applied in a task space or for a centralized control.

Originality/value

The paper proposes two novel optimization techniques: partial‐ and full‐information techniques for tuning the controller gains of a Stewart manipulator, where mission information was imbedded into the cost function. These two techniques are generally applicable for other nonlinear systems such as aircraft stability and control augmentation systems.

Details

Aircraft Engineering and Aerospace Technology, vol. 81 no. 3
Type: Research Article
ISSN: 0002-2667

Keywords

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