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Application of Fuzzy Control in Computational Intelligence

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 18135

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Special Issue Editors

Prof. Dr. Wen-Jer Chang

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Guest Editor

Department of Marine Engineering, National Taiwan Ocean University, Keelung 202, Taiwan
Interests: marine engineering; electrical engineering; system engineering; control engineering; intelligent control; fuzzy theory and control; multimedia application
Special Issues, Collections and Topics in MDPI journals

Dr. Hak Keung Lam

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Guest Editor

Department of Engineering, King’s College London, London WC2R 2LS, UK
Interests: fuzzy control; intelligent systems; computational intelligence; machine learning; deep learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yongming Li

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Guest Editor

College of Sciences, Liaoning University of Technology, Jinzhou 121001, China
Interests: nonlinear system; fuzzy control; adaptive control

Special Issue Information

Dear Colleagues,

Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems, and evolutionary neural systems. Fuzzy logic is widely used in machine control. The term "fuzzy" refers to the fact that the logic involved can deal with concepts that cannot be expressed as the "true" or "false" but rather as "partially true". Although alternative approaches such as genetic algorithms and neural networks can perform just as well as fuzzy logic in many cases, fuzzy logic has the advantage that the solution to the problem can be cast in terms that human operators can understand, so that their experience can be used in the design of the controller. This makes it easier to mechanize tasks that are already successfully performed by humans. As an intelligent control technology, fuzzy control provides a systematic method to incorporate the human experience and implement nonlinear algorithms, characterized by a series of linguistic statements, into the controller. In process control applications, some research has explored the potential of fuzzy control for machine drive applications. As an increasing trend, it is necessary to pay close attention to fuzzy control applications that will enable to identify the emerging trends in the domain.

This Special Issue on “Application of Fuzzy Control in Computational Intelligence” aims to curate novel advances in the development and application of fuzzy control in computational intelligence to address challenges in artificial intelligence and automation technology. Topics include, but are not limited to the following:

Development of novel fuzzy control technology;
Soft computing applications with fuzzy control;
Hybrid fuzzy control combined with intelligent computing methods;
Theory and application of fuzzy control for intelligent systems.

Prof. Dr. Wen-Jer Chang
Dr. Hak Keung Lam
Prof. Dr. Yongming Li
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

fuzzy control technology
hybrid intelligent systems
intelligent computing
neuro-fuzzy-genetic approaches
artificial intelligence
multi-objective optimization
soft computing
fuzzy systems design and optimization

Published Papers (13 papers)

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Editorial

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4 pages, 170 KiB

Open AccessEditorial

Special Issue “Application of Fuzzy Control in Computational Intelligence”

by Wen-Jer Chang

Processes 2022, 10(12), 2522; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10122522 - 28 Nov 2022

Cited by 2 | Viewed by 872

Abstract

Due to the fitted structure of fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems, and evolutionary neural systems, we can study computational intelligence [...] Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

Research

Jump to: Editorial

25 pages, 4297 KiB

Open AccessArticle

Application of Type 2 Fuzzy for Maximum Power Point Tracker for Photovoltaic System

by Nuraddeen Magaji, Mohd Wazir Bin Mustafa, Abdulrahman Umar Lawan, Alliyu Tukur, Ibrahim Abdullahi and Mohd Marwan

Processes 2022, 10(8), 1530; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10081530 - 04 Aug 2022

Cited by 3 | Viewed by 1357

Abstract

Photovoltaic systems (PV) are becoming more popular as a way to make electricity because they offer so many benefits, such as free solar irradiation to harvest and low maintenance costs. Moreover, the system is environmentally friendly because it neither emits noxious gases nor generates environmental noise. Consequently, during the operation of a PV system, the working environment is free of all types of pollution. Despite the aforementioned advantages, a photovoltaic (PV) system’s performance is significantly impacted by the fluctuation in electrical charges from the panel, such as shading conditions (PSC), weather conditions, and others, which significantly lowers the system’s efficiency. To operate the PV modules at their peak power, maximum-power point tracking (MPPT) is employed. As a result of the various peaks present during fluctuating irradiance, the P-V curves become complex. Traditional methods, such as Perturb and Observe (P and O) have also failed to monitor the Global Maximum Power Point (GMPP), therefore they usually live in the Local Maximum Power Point (LMPP), which drastically lowers the efficiency of the PV systems. This study compares type 2 fuzzy logic (T2-FLC) with the traditional Perturb and Observe Method (P and O) in three different scenarios of irradiance, temperature, and environmental factors, in order to track the maximum power point of photovoltaics. Type 1 fuzzy logic (T1-FLC) is not appropriate for systems with a high level of uncertainty (complex and non-linear systems). By modelling the vagueness and unreliability of information, type 2 fuzzy logic is better equipped to deal with linguistic uncertainties, thereby reducing the ambiguity in a system. The result for three conditions in terms of four variables; efficiency, settling time, tracking time, and overshoot, proves that this strategy offers high efficiency, dependability, and resilience. The performance of the proposed algorithm is further validated and compared to the other three tracking techniques, which include the Perturb and Observe methods (P and O). The particle swarm algorithm (PSO) and incremental conductance method results show that type 2 fuzzy (IT2FLC) is better than the three methods mentioned above. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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13 pages, 5971 KiB

Open AccessArticle

The Design and the Development of a Biped Robot Cooperation System

by Chia-Wen Chang and Chin-Wang Tao

Processes 2022, 10(7), 1350; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10071350 - 12 Jul 2022

Cited by 2 | Viewed by 1731

Abstract

The aim of this paper is to design a fuzzy motion control algorithm for a developed monocular vision system based on a cooperative transportation system of two humanoid robots. The control strategies of the cooperation transportation system contain three stages, including object searching, walking toward the transported object, and cooperatively moving the transported object. To have different moving speeds, the gait step size was pre-planned as two different modes, i.e., one of the gaits is selected to let the HR have large variations of motion and another gait is to make the HR with small variations. The fuzzy motion control algorithm is utilized to select the appropriate mode of gait. Both humanoid robots can actively search and move to the front of the target object, then cooperatively lift the target and carry it to the platform. The task of synchronous movement is controlled with fuzzy techniques through the control terminal. From the experimental results, it can be seen that both robots can distinguish the orientation of the target, move to the appropriate position, and then successfully raise the target together. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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15 pages, 605 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

A Fuzzy System Based Iterative Learning Control for Nonlinear Discrete-Time Systems with Iteration-Varying Uncertainties

by Chiang-Ju Chien and Ying-Chung Wang

Processes 2022, 10(7), 1275; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10071275 - 29 Jun 2022

Cited by 2 | Viewed by 1098

Abstract

In this paper, we consider an iterative learning control problem for a class of unknown discrete-time nonlinear systems with iteration-varying initial error, iteration-varying system parameters, iteration-varying external disturbance, iteration-varying desired output, and iteration-varying control direction. These iteration-varying uncertainties are not required to take any particular structure such as the high-order internal model and only need to satisfy certain boundedness conditions. We propose an iterative learning control law with an adaptive iteration-varying fuzzy system to overcome all the uncertainties and achieve the learning control objective. Furthermore, we present a sufficient condition for designing adaptive gains and prove the convergence of the learning error to a small value as the trial number is large enough. Finally, we use two simulation examples to demonstrate all the theoretical results. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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29 pages, 11754 KiB

Open AccessArticle

Solving the Formation and Containment Control Problem of Nonlinear Multi-Boiler Systems Based on Interval Type-2 Takagi–Sugeno Fuzzy Models

by Yann-Horng Lin, Wen-Jer Chang and Cheung-Chieh Ku

Processes 2022, 10(6), 1216; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10061216 - 17 Jun 2022

Cited by 10 | Viewed by 1773

Abstract

An interval type-2 (IT-2) fuzzy control design method is developed to solve the formation and containment problem of nonlinear multi-boiler systems. In most practical industrial systems such as airplanes, vessels, and power plants, the boiler system often exists as more than one piece of equipment. An efficient control theory based on the leader-following multi-agent system is applied to achieve the control purpose of multiple boiler systems simultaneously. Moreover, a faithful mathematical model of the nonlinear boiler system is extended to construct the multi-boiler system so that the dynamic behaviors can be accurately presented. For the control of practical multi-agent systems, the uncertainties problem, which will deteriorate the performance of the whole system greatly, must be considered. Because of this, the IT-2 Takagi–Sugeno (T–S) fuzzy model is developed to represent the nonlinear multi-boiler system with uncertainties more completely. Based on the fuzzy model, the IT-2 fuzzy formation and containment controllers are designed with the imperfect premise matching scheme. Thus, the IT-2 fuzzy control method design can be more flexible for the nonlinear multi-boiler system. Solving the formation problem, a control method without the communication between leaders differs from the previous research. Since leaders achieve the formation objective, the followers can be forced into the specific range formed by leaders. Via the IT-2 fuzzy control method in this paper, not only can the more flexible process of the controller design method be developed to solve the uncertainties problem magnificently, but a more cost-effective control purpose can also be achieved via applying the lower rules of fuzzy controllers. Finally, the simulation results of controlling a nonlinear multi-boiler system with four agents are presented to demonstrate the effectiveness of the proposed IT-2 fuzzy formation and containment control method. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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19 pages, 4263 KiB

Open AccessArticle

Research on Fault Diagnosis of PST Electro-Hydraulic Control System of Heavy Tractor Based on Support Vector Machine

by Huiting Ni, Liqun Lu, Meng Sun, Xin Bai and Yongfang Yin

Processes 2022, 10(4), 791; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10040791 - 18 Apr 2022

Cited by 4 | Viewed by 1926

Abstract

Due to the harsh working environment of the tractor, the transmission can often be faulty. In order to ensure the reliability of its operation, it must be monitored and the fault discovered. In this paper, the support vector machine (SVM) method is used. The eigenvector conversion of the original data uses the following eigenvectors: Three fault modes (leakage fault of shift clutch hydraulic cylinder, blockage fault of oil passage, and blockage fault of proportional valve spool) are identified in matrix and laboratory (MATLAB) with the help of the library for support vector machines (LibSVM) toolkit, and the classification accuracy of test samples is 90%. The normal mode of the PST electro-hydraulic system and the three kinds of fault modes mentioned above are discriminated against, and the correct rate of fault diagnosis reaches 95%, which meets the needs of practical engineering. Analysis of the fault recording data of the power shifting transmission shift solenoid valve shows that the difference between fault pressure data and normal data is small, and the value of traffic data is greater. This method can realize the fault mode online recognition based on controller area network (CAN) communication, and the research results provide a theoretical basis for the fault diagnosis of the PST electro-hydraulic control system. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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20 pages, 7211 KiB

Open AccessFeature PaperArticle

Development of Fuzzy Observer Gain Design Algorithm for Ship Path Estimation Based on AIS Data

by Chin-Lin Pen, Wen-Jer Chang and Yann-Horng Lin

Processes 2022, 10(1), 33; https://0-doi-org.brum.beds.ac.uk/10.3390/pr10010033 - 24 Dec 2021

Cited by 3 | Viewed by 1864

Abstract

This paper develops a Takagi-Sugeno fuzzy observer gain design algorithm to estimate ship motion based on Automatic Identification System (AIS) data. Nowadays, AIS data is widely applied in the maritime field. To solve the problem of safety, it is necessary to accurately estimate the trajectory of ships. Firstly, a nonlinear ship dynamic system is considered to represent the dynamic behaviors of ships. In the literature, nonlinear observer design methods have been studied to estimate the ship path based on AIS data. However, the nonlinear observer design method is challenging to create directly since some dynamic ship systems are more complex. This paper represents nonlinear ship dynamic systems by the Takagi-Sugeno fuzzy model. Based on the Takagi-Sugeno fuzzy model, a fuzzy observer design method is developed to solve the problem of estimating using AIS data. Moreover, the observer gains of the fuzzy observer can be adjusted systemically by a novel algorithm. Via the proposed algorithm, a more suitable or better observer can be obtained to achieve the objectives of estimation. Corresponding to different AIS data, the better results can also be obtained individually. Finally, the simulation results are presented to show the effectiveness and applicability of the proposed fuzzy observer design method. Some comparisons with the previous nonlinear observer design method are also given in the simulations. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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13 pages, 1217 KiB

Open AccessFeature PaperArticle

Adaptive Fuzzy Sliding Mode Control of Omnidirectional Mobile Robots with Prescribed Performance

by Jeng-Tze Huang and Chun-Kai Chiu

Processes 2021, 9(12), 2211; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9122211 - 08 Dec 2021

Cited by 8 | Viewed by 2163

Abstract

Adaptive fuzzy sliding-mode control design for omnidirectional mobile robots with prescribed performance is presented in this work. First, an error transformation which transforms the constrained variable into an unconstrained one is carried out. Next, a fuzzy logic system (FLS) for approximating the unknown dynamics is constructed. Based on such a model, a nominal adaptive linearizing controller incorporating a serial-parallel model (SPM)-based composite algorithm, which improves the tracking performance of the overall closed-loop system, is synthesized. To solve the so-called “loss of controllability” problem, a smooth-switching algorithm is embedded which hands over the control authority to an auxiliary sliding-mode controller until the danger is safely bypassed. The proposed design ensures the semi-globally uniformly ultimately bounded stability of the closed-loop signals. Simulation works demonstrating the validity of the proposed design are presented in the final. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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13 pages, 936 KiB

Open AccessFeature PaperArticle

Exchange Rate Forecasting Based on Combined Fuzzification Strategy and Advanced Optimization Algorithm

by Jie Yin, He Zhang, Aqeela Zahra, Muhammad Tayyab, Xiaohua Dong, Ijaz Ahmad and Nisar Ahmad

Processes 2021, 9(12), 2204; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9122204 - 07 Dec 2021

Cited by 1 | Viewed by 2101

Abstract

Exchange rate forecasting is a crucial but challenging task due to the uncertainty and fuzziness of the associated data caused by complex influence factors. However, most traditional forecasting methods ignore the ambiguity of the data itself. Thus, in this paper, a novel fuzzy time series forecasting system based on a combined fuzzification strategy and an advanced optimization algorithm was proposed for use in exchange rate forecasting, and was proven to have an excellent ability to deal with the uncertainties and ambiguities in data. Concretely, the data “decomposition and ensemble” strategy was applied to carry out the data preprocessing process. The combined fuzzification strategy was used in the fuzzification of the observed data, and the advanced optimization algorithm was developed to determine the optimal parameters in the models. The analysis of this experiment verified the effectiveness of the proposed forecasting system, which will benefit future research and decision-making related to investments. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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21 pages, 2458 KiB

Open AccessFeature PaperArticle

Novel Delay-Dependent Stabilization for Fuzzy Stochastic Systems with Multiplicative Noise Subject to Passivity Constraint

by Cheung-Chieh Ku, Wen-Jer Chang and Kuan-Wei Huang

Processes 2021, 9(8), 1445; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9081445 - 19 Aug 2021

Cited by 6 | Viewed by 1271

Abstract

A novel delay-dependent stability criterion for Takagi-Sugeno (T-S) fuzzy systems with multiplicative noise is addressed in this paper subject to passivity performance. The general case of interval time-varying delay is considered for the practical control issue. For the criterion, an integral Lyapunov-Krasovskii function is proposed to derive some sufficient relaxed conditions and to avoid the derivative of the membership function. Moreover, a free-matrix inequality is adopted to deal with the delay terms such that the available derivative of time-varying delay is bigger than one. In order to employ a convex optimization algorithm to find the control gain, a projection lemma is applied to acquire the Linear Matrix Inequality (LMI) form of the sufficient conditions. With the obtained gains, a fuzzy controller is designed by the concept of Parallel Distributed Compensation (PDC) such that the delayed T-S fuzzy systems with multiplicative noise are asymptotically stable and passive in the mean square. Finally, a stabilization problem of the ship’s autopilot dynamic system and some comparisons are discussed during the simulation results. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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24 pages, 3995 KiB

Open AccessFeature PaperArticle

Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

by Wen-Jer Chang, Yu-Wei Lin, Yann-Horng Lin, Chin-Lin Pen and Ming-Hsuan Tsai

Processes 2021, 9(5), 823; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9050823 - 08 May 2021

Cited by 18 | Viewed by 2068

Abstract

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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26 pages, 4217 KiB

Open AccessFeature PaperArticle

Pole Location and Input Constrained Robust Fuzzy Control for T-S Fuzzy Models Subject to Passivity and Variance Requirements

by Hongyu Qiao, Wen-Jer Chang, Yann-Horng Lin and Yu-Wei Lin

Processes 2021, 9(5), 787; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9050787 - 29 Apr 2021

Cited by 5 | Viewed by 1403

Abstract

In this paper, a robust fuzzy controller for stochastic nonlinear systems subject to multiple performance constraints is discussed. To solve the problem of stochastic behaviors in nonlinear systems, the covariance control theory and passive control theory are applied based on the concept of energy. Additionally, the pole placement method is considered for the better transient behaviors of the system responses. However, it is known that the control inputs and maximum overshoot of the system responses may become bigger at the same time when the settling time or converge rate of the system is required to be faster. Due to this reason, the input constraint is also considered in the fuzzy controller design method to limit the value of the control gain. Moreover, an effective robust control method is applied to deal with the perturbation of the nonlinear systems. Based on the above performance constraints, the sufficient conditions can be obtained to achieve the stability in the sense of mean square and the multi-performance requirements. Finally, simulation results of the nonlinear synchronous generator system are presented to verify the feasibility and efficiency of the proposed control method. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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13 pages, 2392 KiB

Open AccessFeature PaperArticle

Fuzzy Static Output Control of T–S Fuzzy Stochastic Systems via Line Integral Lyapunov Function

by Cheung-Chieh Ku, Yun-Chen Yeh, Yann-Hong Lin and Yu-Yen Hsieh

Processes 2021, 9(4), 697; https://0-doi-org.brum.beds.ac.uk/10.3390/pr9040697 - 15 Apr 2021

Cited by 7 | Viewed by 1480

Abstract

Considering some unmeasurable states, a fuzzy static output control problem of nonlinear stochastic systems is discussed in this paper. Based on a modelling approach, a Takagi–Sugeno (T–S) fuzzy system, constructed by a family of stochastic differential equations and membership functions, is applied to represent nonlinear stochastic systems. Parallel distributed compensation (PDC) technology is used to construct the static output controller. A line-integral Lyapunov function (LILF) is used to derive some sufficient conditions for guaranteeing the asymptotical stability in the mean square. From the LILF, a potential conservatism produced by the derivative of the membership function is eliminated to increase the relaxation of sufficient conditions. Furthermore, those conditions are transferred into linear matrix inequality (LMI) form via projection lemma. According to the convex optimization algorithm, the feasible solutions are directly obtained to establish the static output fuzzy controller. Finally, a numerical example is applied to demonstrate the effectiveness and usefulness of the proposed design method. Full article

(This article belongs to the Special Issue Application of Fuzzy Control in Computational Intelligence)

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