Table 8.2-3: Comparison of total actuator travel distance/cycle between chilled water valve on AHUA (AFLC-GA) and AHUB (PIDL) for variation in SATSPT test. Comparison test results for [r]

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The theoretical design of a **fuzzy** iterative learning **controller** for sampled-time linear time invariant systems is presented in this project. The project also investigates of digital circuit implementation of the proposed **controller** with application to repetitive position tracking control of DC servo motors. The stability and convergence of the learning **system** are analyzed under uncertainties of initial state errors, input disturbance and output measurement error. This project also stress that the learning error will converge to a residual set whose level of magnitude will depend on the size of the uncertainties. The learning error will asymptotically converge to zero if all uncertainties disappear. To improve the learning performance, a concept of **fuzzy** learning gain is introduced which is designed based on the tracking error in the current and past iteration.

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Assistant Professor, Department of Electrical and Electronics Engineering, SNS College Engineering, TamilNadu, India 5 Abstract: The research paper involves based on Function Torque Adapted Gain **Fuzzy** Inference **System** (FTAGFIS) **adaptive** speed **controller** for the Permanent Magnet Synchronous Motor (PMSM). The proposed research area **controller** was developed using **Adaptive** Neuro **Fuzzy** Inference **System** (ANFIS). The expanded version of the FIS not only suggests the effectiveness of using the ANFIS to develop an **adaptive** speed **controller** for drives but also the effectiveness of the methodology that was employed to train the FIS. The results are under **development** using **Adaptive** control algorithm. Previous paper presents tracking and recovery performance from motor parameter variations, load torque and speed disturbances, which indicates the ability of the PMSM to self- adapt itself to different operating conditions here our paper involves the efficiency and reduced mechanical parameters and disturbances. .

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Today’s aircraft designs rely heavily on automaticcontrol **system** to monitor and control many of aircraft’s subsystems. The **development** of automatic control **system** has played an important role in the growth of civil and military aviation.The architecture of the flight control **system**, essential for all flight operations, has significantly changed throughout the years. Soon after the first flights, articulated surfaces were introduced for basic control, operated by the pilot through a **system** of cables and pulleys. This technique survived for decades and is now still used for small airplanes. The introduction of larger airplanes and the increase of flight envelopes made the muscular effort of the pilot, in many conditions, not sufficient to control the aerodynamic moments consequent to the surface deflection. The first solution to this problem was the introduction of aerodynamic balances and tabs, but further grow of the aircraft sizes and flight enveolpes brought to the need of powered systems to control the articulated aerodynamic surfaces. Modern aircraft include a variety of automatic control **system** that aids the flight crew in navigation, flight management and augmenting the stability characteristic of the airplane.Theautopilot is an element within the flight control **system**. Designing an autopilot requires control **system** theory background and knowledge of stability derivatives at different altitudes and Mach numbers for a given airplane [3..The number and type of aerodynamic surfaces to be controlled changes with aircraft category. Aircraft have a number of different control surfaces: the

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Because the peripheral control and devices are enough, there is the more extensive application in the industry control **system**. Therefore, the DC motor control is larger than other kinds of motors then are no matter in the theoretic study in the research and **development** application. The measurement and control can be achieved by PC-based. However, the technique of Instrument design also moves forward the times of “virtual instrument”, not only the designing time is shorten, but also the designing space is more elastic extension. This project presents to guide the motor speed control field with the various advanced computer technology and the **development** platform of software/hardware. Let the dynamic state response of motor have a better efficiency.

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A linear model is first computed from the nonlinear Simulink model of the FLC before the linear response plotted. During simulation, the software linearizes the portion of the model between specified linearization inputs and outputs, and plots the response of the linear **system**. The Simulink model can be continuous- or discrete-time or multi- rate and can have time delays depending on whether the design is aimed to be implemented in real-time hardware. Refer to Table 4 for a complete list of the response performance indices of the FLC.

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The concept of **Fuzzy** **Logic** was conceived by Lotfi Zadeh, a professor at the University of California at Berkley, and presented not as a control methodology, but as a way of processing data by allowing partial set membership rather than crisp set membership or non-membership. This approach to set theory was not applied to control systems until the 70's due to insufficient small-computer capability prior to that time. Professor Zadeh reasoned that people do not require precise, numerical information input, and yet they are capable of highly **adaptive** control. If feedback controllers could be programmed to accept noisy, imprecise input, they would be much more effective and perhaps easier to implement [2].

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A number of soft computing techniques exist for maximum power tracking. This includes Neural network, **adaptive** **fuzzy** etc. In neural network (NN) technique, a trained neural network outputs the reference voltage for MPP. The reference voltage can be attained using either a **fuzzy** or a PI **controller**. This can be done with less computational efforts and the knowledge of internal **system** parameters may not be required. Neural network has to be periodically trained since the characteristics of a PV array can change with time. Section II provides details about photovoltaic **system** and MPPT strategy. Proposed **fuzzy** **logic** **controller** is explained in Section III and the verified simulation results are presented in Section IV which is followed by conclusion in Section V.

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The results show a considerable vibration reduction using the MR damper as a passive energy dissipation device in a passive ON con¯guration. The increase of the operating current in the MR damper has a signi¯cant e®ect in the damping force and the energy dissipation capacity of the device. Figure 13 displays the structural re- sponse of each °oor obtained with the proposed **fuzzy** based control **system** along with the uncontrolled response of the third °oor during the numerical simulation. As can be seen, the proposed semi-active control **system** achieves a good performance in reducing the structural responses using only °oor velocities as the reference (input) signals to compute the control action. In fact, the main advantage of this **fuzzy** **logic** based control **system** is that only the ¯rst and third °oor velocities of the structure are required to determine the desired control signal. This means that the damping

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Regarding the membership type used in **Fuzzy** **Logic**, other choices than the triangular membership are available such as the gaussian membership, trape- zoid membership or sigmoid membership. As explained above, researcher usu- ally applies the triangular membership as it is seems to be easier than other membership. Even if this is the factor, there were also some other ﬁndings that discovered very interesting results as shown by V.O.S Olunloyo et al.[10]. Based on their ﬁndings, the triangular membership can exhibit linguistic error as it may not deﬁnes properly the real **system** conditions. As a result, the gaussian membership can be the best membership to describe any practical **system** for most engineering application. Moreover, gaussian membership may surpassed triangular membership function if better tracking performance is being priori- tized[11].

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In recent years, **fuzzy** **logic** based control schemes have become a topic of great interest. It is one of the active areas of research in the applications of **fuzzy** set theory, because conventional controllers cannot be used due to lack of knowledge regarding the input- output models [1,3]. These methods have been used successfully in many real-world applications [2,8]. **Fuzzy** **logic** based controllers are generally applicable to systems that do not accurate mathematical models and require only qualitative guidance through experienced operators for their implementation [4]. **Fuzzy** **logic** is conceived as a tool for dealing with uncertainty but has proven to be an excellent choice for many control **system** applications. The fast developments in the technology relating to hardware systems required for implementing **fuzzy** **logic** based controllers like **fuzzy** memory devices, **fuzzy** computers etc. have made a way for effective utilization of **fuzzy** **logic** even in complex and ill defined

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Abstract - The paper proposes the realization of a **Fuzzy** **Logic** Temperature **Controller**. In this paper an analysis of **Fuzzy** **Logic** **Controller** is made and a temperature **controller** using MATLAB is developed. Here we used **Fuzzy** **Logic** Toolbox which is very useful software for **development** and testing of **Fuzzy** **Logic** **system**. It can be very quickly implemented and its visual impact is very encouraging. In this **controller** the Rule Base, membership functions and inference engine are developed either using digital systems such as memory and **logic** circuit or it can be developed using analog CMOS circuits. Analog **Fuzzy** systems are popular because of their continuous-time-processing and high frequency and low power implementation.

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From old days the transportation systems have been one of the most signiﬁcant aspects of our life. Today’s rapid increasing of traﬃcs in roads, the most frequent driving scenario [1], demands enormous increasing of intelligent transportation systems (ITSs). One of the applications of ITS is to provide the assistance to the control of some of the vehicle elements, like the throttle pedal and consequently, the speed-control assistance [2]. **Adaptive** cruise control (ACC) **system** is a new generation of conventional cruise control systems. But, unlike the conventional cruise control structures, this new **system** can automatically adjust the speed in order to maintain a proper headway distance (gap) between vehicles in the same lane [3,4]. This is achieved by using a radar headway sensor such as lidar [5], a digital signal processor, and an intelligent speed control **system** [6]. If the front vehicle slows down, or another obstacle is detected, the ACC sends a signal to the engine and the braking **system** decelerates the vehicle. On the other hand, when the front vehicle speeds up, or no obstacle is detected, the ACC will re-accelerate the vehicle back to the default speed [7,8]. Because of the ability of **fuzzy** **logic** **controller** (FLC) in mimicking human behaviour, it can be used as a good bridge to achieve this goal.

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This paper propose the **Fuzzy** **Logic** **Controller** (FLC) to control the speed of DC motor using LabVIEW and display the speed of the motor in real-time in order to obtain the **system** response of FLC. The real-time monitoring of DC motor not only can substitute the traditional instrument but also can be used to observe the machine operating normally or not. This programming **system** is based on a structure of the PC, and combines the DC motor supervision needed instrument which then replaces other hardware equipment with the cheaper and more efficient method to facilitate operators with the graphical friendly interface.

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The impact of soft-computing in modern day engineering and technology cannot be overemphasized. **Fuzzy** **logic** approach as proposed by Lofti Asker Zadeh, popularized by the Japanese, has found its way into the control of many domestic and industrial appliances/machines. Unlike the popular PID controllers and the pulse width modulation based controllers, the performance of computer fan is investigated using the **fuzzy** **logic** approach with two inputs parameters, that is, the computer loads and the temperature and one output parameter which is the speed at which the computer fan operates. For the **fuzzy** inference **system**, four membership functions are selected for the inputs as well as the output. Relevant rules are set to determine the operating conditions and boundaries for the **controller**. In order to make the **controller** **adaptive**, neurofuzzy **logic** appproach is used with parameters set as the case with **fuzzy** **logic**. Training of the **controller** is carried out and the performance of each **controller** is presented in three dimensional view and two dimensional surface view with neurofuzzy based **controller**, in performance, having an edge over the **fuzzy** **logic** based **controller**.

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The IT2-FLC contains four components fuzziﬁer, inference engine, rule base, and output processing that is inter-connected as shown in Fig. 5 [28] . The IT2-FLC works as follows [15] : the crisp input is ﬁrst fuzziﬁed into input IT2-FSs. The input IT2- FSs then activate the inference engine and the rule base to pro- duce output IT2-FSs. The IT2-FLC rules will remain the same as in T1-FLC, but the antecedents and/or the consequent will be represented by IT2-FSs. The IT2 **fuzzy** outputs of the infer- ence engine are then processed by the type reducer, which com- bines the output sets and performs a centroid calculation that leads to T1-FSs called the type-reduced sets. After the type reduction process, the type-reduced sets are defuzziﬁed (by taking the average of the type-reduced set) to obtain crisp outputs.

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PID control requires the model of the **system** for the determination of the parameters of PID **controller** using control theory and finally the **development** of an algorithm for the **controller** [2]. However, in case of **fuzzy** **logic**, the **system** behavior is characterized using human knowledge which directly deals with the design of control algorithm on the basis of **fuzzy** rules. These rules are described in terms of the relationship of inputs to their corresponding outputs, and precisely determine the **controller** parameters. Any adjustment or debugging only requires modification in these **fuzzy** rules instead of redesigning the **controller** [4,5,8]. Hence control technique based on **fuzzy** **logic** not only simplifies the design, but also reduces the monotonous task of solving complex mathematical equations of nonlinear systems.

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Observers were impressed with this demonstration, as well as later experiments by Yamakawa in which he mounted a wine glass containing water or even a live mouse to the top of the pendulum. The **system** maintained stability in both cases. Yamakawa eventually went on to organize his own **fuzzy**-systems research lab to help exploit his patents in the field. [3]

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The paper by [8]studied the use of belt for high precision applications that became appropriate because of the rapid **development** in motor and drive technology as well as the implementation of timing belts in servo systems. Modeling of a linear belt-drive **system** and designing its position control were examined in the research work. Friction phenomena and position dependent elasticity of the belt were analyzed. Computer simulated results showed that the developed model was adequate. The PID control for accurate tracking control and accurate position control was designed and applied to the real test setup. Both the simulation and the experimental results demonstrated that specifications. The designed **controller** met the specified performance specifications.

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In this paper, a model that behaves exactly like Cement kiln is identified using **System** Identification toolbox in MATLAB with kiln speed (KS), Coal feed (CF), Kiln feed (KF) and Preheater fan speed (PHFS) as inputs and Burning Zone Temperature (BZT), Torque (TOR) and Kiln inlet temperature. A **Fuzzy** **Logic** **Controller** is then designed to work on this **system** for desired set points. The **system** is then made **adaptive** to work on a range of set points.