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Article
Publication date: 1 September 1994

R Bannatyne

Examines the growth of a new technology called fuzzy logic and itssignificance for microcontroller‐based embedded control solutions.Outlines the reasons for the emergence of fuzzy

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Abstract

Examines the growth of a new technology called fuzzy logic and its significance for microcontroller‐based embedded control solutions. Outlines the reasons for the emergence of fuzzy logic and explains the mathematic principles behind fuzzy set theory. Using the example of an oven temperature control system, describes how fuzzy logic is applied to the practical solution of a control problem rather than a conventional solution. Concludes that fuzzy logic has been used primarily in embedded control application as a software‐based methodology in closed‐loop control systems whilst a dedicated fuzzy hardware processor would optimally be based on a parallel architecture, allowing the entire rule base to be evaluated in a parallel fashion.

Details

Sensor Review, vol. 14 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 1 June 2005

Manish Kumar and Devendra P. Garg

The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neuro‐fuzzy scheme to control two cooperating robots.

Abstract

Purpose

The paper aims to advance methodologies to optimize fuzzy logic controller parameters via neural network and use the neuro‐fuzzy scheme to control two cooperating robots.

Design/methodology/approach

The paper presents a special neural network architecture that can be converted to fuzzy logic controller. Concepts of model predictive control (MPC) have been used to generate optimal signal to be used to train the neural network via backpropagation. Subsequently, a trained neural network is used to obtain fuzzy logic controller parameters.

Findings

The proposed neuro‐fuzzy scheme is able to precisely learn the control relation between input‐output training data generated by the learning algorithm. From the experiments performed on the industrial grade robots at Robotics and Manufacturing Automation (RAMA) Laboratory, it was found that the neuro‐fuzzy controller was able to learn fuzzy logic rules and parameters accurately.

Research limitations/implications

The backpropagation method, used in this research, is extremely dependent on initial choice of parameters, and offers no mechanism to restrict the parameters within specified range during training. Use of alternative learning mechanisms, such as reinforcement learning, needs to be investigated.

Practical implications

The neuro‐fuzzy scheme presented can be used to develop controller for plants for which it is difficult to obtain analytical model or sufficient information about input‐output heuristic relation is not available.

Originality/value

The paper presents the neural network architecture and introduces a learning mechanism to train this architecture online.

Details

Industrial Robot: An International Journal, vol. 32 no. 3
Type: Research Article
ISSN: 0143-991X

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Abstract

Details

Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry
Type: Book
ISBN: 978-1-78743-086-0

Article
Publication date: 2 June 2022

Himanshukumar R. Patel and Vipul A. Shah

In recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based…

Abstract

Purpose

In recent times, fuzzy logic is gaining more and more attention, and this is because of the capability of understanding the functioning of the system as per human knowledge-based system. The main contribution of the work is dynamically adapting the important parameters throughout the execution of the flower pollination algorithm (FPA) using concepts of fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

Design/methodology/approach

The fuzzy logic-based parameter adaptation in the FPA is proposed. In addition, type-2 fuzzy logic is used to design fuzzy inference system for dynamic parameter adaptation in metaheuristics, which can help in eliminating uncertainty and hence offers an attractive improvement in dynamic parameter adaption in metaheuristic method, and, in reality, the effectiveness of the interval type-2 fuzzy inference system (IT2 FIS) has shown to provide improved results as matched to type-1 fuzzy inference system (T1 FIS) in some latest work.

Findings

One case study is considered for testing the proposed approach in a fault tolerant control problem without faults and with partial loss of effectiveness of main actuator fault with abrupt and incipient nature. For comparison between the type-1 fuzzy FPA and interval type-2 fuzzy FPA is presented using statistical analysis which validates the advantages of the interval type-2 fuzzy FPA. The statistical Z-test is presented for comparison of efficiency between two fuzzy variants of the FPA optimization method.

Originality/value

The main contribution of the work is a dynamical adaptation of the important parameters throughout the execution of the flower pollination optimization algorithm using concepts of type-2 fuzzy logic. By adapting the main parameters of the metaheuristics, the performance and accuracy of the metaheuristic have been improving in a varied range of applications.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 16 no. 2
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 26 August 2014

Yanbin Liu, Keming Yao and Yuping Lu

The purpose of this paper is to present the flight control law based on the fuzzy logic control methods for Mars airplane, and the research emphasis is placed on the attitude hold…

Abstract

Purpose

The purpose of this paper is to present the flight control law based on the fuzzy logic control methods for Mars airplane, and the research emphasis is placed on the attitude hold and the command track using the fuzzy control.

Design/methodology/approach

The aircraft model is established with the combination of atmospheric environment, aerodynamic force and propulsive action. Then, the dynamic characteristics are analyzed in response to the different flight points for Mars airplane. Afterward, the flight control law is designed by applying the fuzzy logic theory to realize the attitude hold and the command track for Mars airplane.

Findings

The simulation results demonstrate that the proposed control law based on the fuzzy logic control methods is effective to guarantee system stability and relieve coupling dynamics. In addition, this control system can provide strong robustness and good tracking performance for Mars airplane.

Practical implications

The current work offers a new approach for the control law design of Mars airplane. The presented fuzzy control system can be applied to the other unconventional airplanes which will fly under unknown and uncertain environment to implement the complicated tasks such as deep space exploration.

Originality/value

This paper provides the new methods for Mars airplane to design the fuzzy control system which consists of three implementation steps: the fuzzy quantization control step, the fuzzy decoupling control step and the fuzzy attitude control step. Through the progressive design, this presented control system of Mars airplane has strongly nonlinear and robust control ability due to the application of the fuzzy expert concepts.

Details

Aircraft Engineering and Aerospace Technology: An International Journal, vol. 86 no. 5
Type: Research Article
ISSN: 0002-2667

Keywords

Article
Publication date: 1 May 1995

Linda Stotts and Brian H. Kleiner

Sets out to provide an understanding of the theory of fuzzy logicby supplying background details concerning its evolution in mathematicsand computer science. Once a basic…

200

Abstract

Sets out to provide an understanding of the theory of fuzzy logic by supplying background details concerning its evolution in mathematics and computer science. Once a basic understanding of the theory is obtained, then it is easier to understand the implications for computer applications. Fuzzy logic processors and compilers have facilitated the development of expert systems that typically use a lot of imprecise data. These expert systems have been used successfully as control units in industrial settings and as decision support systems in hospital settings. Fuzzy logic has been found to be a practical and viable form of artificial intelligence that mitigates the current drawbacks of other forms of artificial intelligence. But the really exciting development that is poised to emerge is the introduction of fuzzy logic appliances. These appliances employ an expert system on a chip that is able to mimic the range of flexibility of the human mind, while utilizing resources more efficiently.

Details

Industrial Management & Data Systems, vol. 95 no. 4
Type: Research Article
ISSN: 0263-5577

Keywords

Abstract

Details

Prioritization of Failure Modes in Manufacturing Processes
Type: Book
ISBN: 978-1-83982-142-4

Article
Publication date: 1 September 1994

Rudi Raber

Examines the problems of applying fuzzy logic to control applicationswhere each application is different from the previous one and looks at thedevelopment of a unique system of…

476

Abstract

Examines the problems of applying fuzzy logic to control applications where each application is different from the previous one and looks at the development of a unique system of fuzzy logic based on an existing software package. The system comes in two alternative forms; the first is a fully configurable fuzzy set for the OEM designer which, for example, could be used to control the temperature of a large oven and the second reduces the fuzzy function down to the most basic level where no process knowledge is required. Describes the tests carried out in order to determine the linguistic variables which apply to general control applications. Concludes that fuzzy logic begins to take effect in those situation to which normal control algorithms PD, PID functions have difficulty adjusting.

Details

Sensor Review, vol. 14 no. 3
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 1 April 1999

George Thomas Friedlob and Lydia L.F. Schleifer

Auditors generally describe risk in terms of probabilities. Risk arises from lack of information which in turn leads to uncertainty. Since uncertainty exists when information is…

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Abstract

Auditors generally describe risk in terms of probabilities. Risk arises from lack of information which in turn leads to uncertainty. Since uncertainty exists when information is deficient and information can be deficient in different ways, it follows that auditors deal with different types of uncertainty. This article describes different types of uncertainty and a relatively new method of dealing with uncertainty referred to as fuzzy logic. Fuzzy logic and fuzzy set theory have contributed greatly to the development of artificial intelligence and have the potential to facilitate internal auditors’ measurement and management of risk and uncertainty in the audit environment.

Details

Managerial Auditing Journal, vol. 14 no. 3
Type: Research Article
ISSN: 0268-6902

Keywords

Article
Publication date: 13 November 2017

Lie Yu, Jia Chen, Yukang Tian, Yunzhou Sun and Lei Ding

The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems (UASs). In…

Abstract

Purpose

The purpose of this paper is to present a control strategy which uses two independent PID controllers to realize the hovering control for unmanned aerial systems (UASs). In addition, the aim of using two PID controller is to achieve the position control and velocity control simultaneously.

Design/methodology/approach

The dynamic of the UASs is mathematically modeled. One PID controller is used for position tracking control, while the other is selected for the vertical component of velocity tracking control. Meanwhile, fuzzy logic algorithm is presented to use the actual horizontal component of velocity to compute the desired position.

Findings

Based on this fuzzy logic algorithm, the control error of the horizontal component of velocity tracking control is narrowed gradually to be zero. The results show that the fuzzy logic algorithm can make the UASs hover still in the air and vertical to the ground.

Social implications

The acquired results are based on simulation not experiment.

Originality/value

This is the first study to use two independent PID controllers to realize stable hovering control for UAS. It is also the first to use the velocity of the UAS to calculate the desired position.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 10 no. 4
Type: Research Article
ISSN: 1756-378X

Keywords

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