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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.

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.

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.

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.

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.

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.

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.

This paper seeks to propose a systematic method to design multi fuzzy FACTS based stabilizers in a multi‐machine power system.

Conventional FACTS based stabilizers are decentralized controllers that adopt local measurements and operate in closed loop. To improve overall system dynamic performance, a coordinating application of FACTS based stabilizer is essential. Although, numerous researches have indicated the effectiveness and superiority of fuzzy logic controllers in comparison with the conventional linear controllers in power system application but researchers have not adequately investigated coordination of multi fuzzy controllers in multi‐machine power systems to provide optimal performance. Genetic algorithm is used to determine optimum values of controllers' parameters.

The search space of the optimisation procedure is decreased to a smaller one, design and computation time can be reduced significantly and the design process becomes more systematic.

A systematic method is introduced to coordinate multi fuzzy FACTS based stabilizers in multi‐machine power systems.

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.

The purpose of this paper is to study the application of entropy based optimized fuzzy logic control for a real-time non-linear system. Optimization of the fuzzy membership function (MF) is one of the most explored areas for performance improvement of the fuzzy logic controllers (FLC). Conversely, majority of previous works are motivated on choosing an optimized shape for the MF, while on the other hand the support of fuzzy set is not accounted.

The proposed investigation provides the optimal support for predefined MFs by using genetic algorithms-based optimization of fuzzy entropy-based objective function.

The experimental results obtained indicate an improvement in the performance of the controller which includes improvement in error indices, transient and steady-state parameters. The applicability of proposed algorithm has been verified through real-time control of the twin rotor multiple-input, multiple-output system (TRMS).

The proposed algorithm has been used for the optimization of triangular sets, and can also be used for the optimization of other fussy sets, such as Gaussian, s-function, etc.

The proposed optimization can be combined with other algorithms which optimize the mathematical function (shape), and a potent optimization tool for designing of the FLC can be formulated.

This paper presents the application of a new optimized FLC which is tested for control of pitch and yaw angles in a TRMS. The performance of the proposed optimized FLC shows significant improvement when compared with standard references.

The simultaneous scheduling of resource‐constrained maintenance and operations is addressed in this work. The purpose of the paper is to capture the uncertainty in the development of a model that schedules both preventive maintenance and operational activities. Fuzzy logic is employed to transform the human expertise into IF‐THEN rules.

The approach has the advantage of revealing semantic uncertainty with the associated non‐specifying measures. The methodology applied tracks the error values in terms of results in linguistic variable.

The results obtained indicate the feasibility of tracking the uncertain measures in the model discussed. Thus, the study may be applicable to both production system and transportation organizations that are engaged in both maintenance and operational activities.

The research has serious implication in terms of the ability to monitor the imprecision that were introduced in the previous models. This obviously provides a more reliable framework for researchers and practitioners interested in maintenance scheduling activities.

The paper is new in that it demonstrates the application of fuzzy logic in a form that was never documented.

A rotary kiln is a pyro processing device that is used to raise the temperature of materials in cement factories. Temperature monitoring is an essential process in the rotary kiln to yield high quality clinker. Temperature measurement is a challenging task in clinkering process and it is difficult to apply automation techniques. As the pyrometer gives unreliable readings, it is necessary to apply various image processing techniques on the camera images to measure the temperature inside the kiln at different zones.

In this paper, a fuzzy logic rule-based analysis is proposed to measure temperature using a burning flame image in which it considers red, green, blue (RGB) magnitude planes. The proposed method uses Mamdani fuzzy inference system for decision-making. The system takes RGB magnitude as an input fuzzified variable and generates temperature as fuzzified output.

This paper focuses on the temperature measurement obtained from the images of the camera system. The commands to the valves and actuators are controlled using the center of gravity of the control regime. The fuzzy logic controller detects the temperature of flame zones using color features of burning flame images.

Precise temperature mapping of flame images helps to control the temperature inside the rotating kiln to produce high quality clinker. The process can be viewed remotely and controlled using various control loops from anywhere.

This paper aims to propose an improved direct torque control (DTC) for the induction motor’s performance enhancement using dual nonlinear techniques. The exact feedback linearization is implemented to create a linear decoupled control. Besides, the fuzzy logic control approach has been inserted to generate the auxiliary control input for the feedback linearization controller.

To improve the DTC for induction motor drive, this work suggests the incorporation of two nonlinear approaches. As the classical feedback linearization suffers while the presence of uncertainties and modeling inaccuracy, it is recommended to be associated to another robust control approach to compensate the uncertainties of the model and make a robust control versus the variations of the machine parameters. Therefore, fuzzy logic controllers will be integrated as auxiliary inputs to the feedback linearization control law.

The simulation and the experimental validation of the proposed control algorithm show that the association of dual techniques can effectively achieve high dynamic behavior and improve the robustness against parameters variation and external disturbances. Moreover, the space vector modulation is used to preserve a fixed switching frequency, reduce ripples and low switching losses.

The theoretical, simulation and experimental studies prove that the proposed control algorithm can be used on different AC machines for variable speed drive applications such as oil drilling, traction systems and wind energy conversion systems.

The proposed DTC strategy has been developed theoretically and realized through simulation and experimental implementation. Different operation conditions have been conducted to check the ability and robustness of the control strategy, such as steady state, speed reversal maneuver, low-speed operation and parameters variation test with load application.