The controlled **suspension** system is now widely applied such as semi-**active** **suspension** [6, 7], and **active** **suspension** [8, 9]. Chen [6] proposed a new method on design and stability analysis of semi-**active** **suspension** **fuzzy** **control** system. **Based** on the Lyapunov stability theory, each **fuzzy** subspace's stability of close loop system is analyzed. And then stable condition of whole **fuzzy** **control** system is obtained. The result of experiment and simulation shows that **fuzzy** **control** system of semi-**active** **suspension** is effective and stable and improves the **ride** **performance**. Pang [7] studied the problem of stability analysis and **fuzzy**-smith compensation **control** for semi-**active** **suspension** systems with time delay. **Based** on the Lyapunov stability analysis, the necessary and sufficient condition of the critical time delay for the semi-**active** **suspension** is derived, and the numerical computation method of solving the asymptotic stability area for the **suspension** system is given. Finally, the results show the **ride** comfort is improved. Although many scholars explored various semi-**active** vehicle suspensions to improve **ride** **performance**, the **active** **suspension** is widely utilized for luxurious vehicle since it can effectively improve the **ride** comfort as well as stability. However, a challenging problem for **active** **suspension** design is determined by a **control** strategy to improve the system **performance**. During the past two decades, many **control** strategies have been proposed by many researchers. A half car model with **active** **suspension** was presented by [10]. They utilized a dual loop PID controller strategy to improve dynamic **performance**. A nonlinear optimal **control** law is developed, and applied on a half-car model for the **control** of **active** **suspension** systems to improve the tradeoff between **ride** quality and **suspension** travel compared to the passive **suspension** system [11]. An effective and robust proportional integral sliding mode **control** strategy is proposed in the **active** **suspension** system [12]. **Control** strategies are investigated for **active** **suspension** systems with **control** concepts of look-ahead and wheelbase preview. The corresponding controllers were designed and the **suspension** system **performance** was improved [13]. A robust **fuzzy** sliding-mode controller for an **active** **suspension** system was applied in a half-car model. The feasibility was verified [14]. The uncoupled half car model is constructed such as Karnopps’ work [15]. He presented a passive and **active** **control** of road heave and pitch motions for approximately uncoupled motions. Wong’s work [16] considered a two DOF vehicle model to study the heave and pitch motions neglecting the vehicle **suspension** dynamics.

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In more detailed studies, [10] developed a **fuzzy** **logic** **control** for an **active** **suspension** system applied to a half vehicle model. The velocity and the acceleration of the front and rear wheels, and undercarriage velocity above the front and rear wheels are considered as controller input signals. The results showed that an achievement in **ride** and handling is obtained relative to a traditional passive **suspension** system. Using linear **control** together with **fuzzy** **logic** **control** in an **active** **suspension** system was investigated by [11]. In this paper the **active** **control** is the sum of two kinds of **control**. The former is obtained by vertical acceleration of the vehicle body as the principal source of **control**, and the latter is obtained by using **fuzzy** **logic** **control** as a complementary **control**. The simulation results indicate that, the proposed **active** **suspension** system is very effective in the vibration isolation of the vehicle body. [12] Prepared a theoretical investigation for an **active** **suspension** system applied to a full passenger car model. An **active** **suspension** system controller **based** on **fuzzy** **logic** **control** was designed. The sprung mass velocity and unsprung mass velocity were considered as controller inputs. The results indicated that **fuzzy** **logic** **control** of an **active** **suspension** system provides a significant **improvement** in comparison with passive **suspension** systems and LQR **active** **suspension** systems. Since investigating the interaction between the **active** **suspension** and the anti-lock braking systems is also important, [13] suggested a **fuzzy** **logic** controller for an **active** **suspension** system applied to a half vehicle model. The **active** **suspension** system, tyre-road interface and anti-lock braking system were included in the model. The body acceleration and **suspension** working space were used as controller inputs. From the simulation results, they concluded that **fuzzy** **logic** **control** with **active** **suspension** system improved the vehicle **ride** **performance** when compared with passive systems. Furthermore, as studying the interaction between the **suspension** and braking systems for heavy vehicles, [14] suggested **fuzzy** **logic** controllers for three axles long chassis truck **active** **suspension** and cab **suspension** systems together with anti-lock braking system and integrated **control** system. The results showed that **fuzzy** **logic** **control** with **active** truck **suspension** and cab **suspension** improved the truck **ride** comfort; also using it with anti-lock braking system enhanced the braking **performance** of the truck at different driving condition. Furthermore, the proposed integrated **control** improved the **ride** comfort of the truck during braking process.

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Abstract- In this analysis Shunt **Active** Power Filter (APF) is proposed for the compensation of harmonic currents and reactive power. For this purpose, a **fuzzy** **logic** controller is developed to adjust the energy storage of the dc voltage. The reference current computation of the shunt APF is **based** on the instantaneous reactive power (p-q) theory. MATLAB/SIMULINK power system toolbox is used to simulate the proposed system. With this controller we are able to compensate the harmonic current and reactive power within limits by making an **improvement** in conventional triangularization current **control** loop by eliminating current controller from feedback **control** system.

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outer voltage **control** loop to produce the current references. This **control** strategy leads to very quick dynamics but brings all disadvantages inherent to fluctuating frequency. Examples of digital **control** techniques utilizing intern model principles are the adaptive and dead beat **control**. One of the main **control** challenges faced in all these approaches is the production of current references. Among the theories to calculate the compensation reference current are the theory, the synchronic reference frame, the discrete Fourier transform, adaptive neutral networks and Lyapunov function **based** **control**. These **control** approaches while having an acceptable **performance** for accomplishing unity power factor relatively difficult to implement. A novel APF **control** indirect techniques are recommended here as alternative to the cited techniques. These new indirect methods are depends on the concept of virtual impedance emulation through inverters to obtain unity power factor at input side. A major feature is that either mains side current or voltage sensor is utilized, leading to senseless approaches. The APF implicit current reference does not contain higher order harmonic components to ensure unity power factor at the mains side, and hence the **control** technique is highly reduced, leading to the **control** methods classified in this paper.

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Recently, soft computing and intelligent algorithms such as **fuzzy** **logic** **based** controllers (FLCs) have been proposed for semi-**active** vehicle **suspension** systems. This advanced mod- eling technique represents a powerful tool to deal with non-linear systems. FLC controllers use human-like reasoning through the use of **fuzzy** sets and linguistic variables related by a set of IF-THEN **fuzzy** rules. The design of a FLC requires the definition of membership functions (MFs) and the selection of appropriate **fuzzy** inference rules that relate the controller inputs and outputs. In this case, triangular MFs are used to represent both input and output variables as shown in Figures 4 and 5. The FLC inputs are the relative displacement and velocity across the MR damper, which represent state variables obtained via sensor measurements. The con- troller output is the **control** signal used to operate the MR damper.

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For limiting the aerodynamic power captured by the wind turbine at the high-wind speed regions, several pitch angle **control** methods have been suggested. The proportional–integral (PI) or proportional—integral– derivative (PID) **based**-pitch angle controllers have been often used for the power regulation [1], [12]–[15]. The disadvantage of this method is that the **control** **performance** is deteriorated when the operating points are changed since the controller design is **based** on the turbine model which is linearized at the operating points by a small signal analysis. Another scheme using the H∞ controller with a linear matrix inequality approach was proposed [16], which gives a good **performance** of the turbine output power as well as the robustness to the variations of the wind speed and the turbine parameters. However, it is rather complex since the parameters of the model and the controller need to be redesigned due to the changes of the weighting functions by the constraints. The power fluctuations caused by wind speed variation, wind shear, tower shadow, yaw errors, etc., lead to the voltage fluctuations in the network, which may produce flicker [3]. Apart from the wind power source conditions, the power system characteristics also have impact on flicker emission of grid-connected wind turbines, such as short-circuit capacity and grid impedance angle [4], [5]. The flicker emission with different types of wind turbines is quite different. Though variable-speed wind turbines have better **performance** with regard to the flicker emission than fixed-speed wind turbines, with the large increase of wind power penetration level, the flicker study on variable speed wind turbines becomes necessary and imperative. A number of solutions have been presented to mitigate the flicker emission of grid-connected wind turbines. The most commonly adopted technique is the reactive power compensation [6]. However, the flicker mitigation technique shows its limits in some distribution networks where the grid impedance angle is low [7]. When the wind speed is high and the grid impedance angle is 10 ◦ , the reactive power needed for flicker mitigation is 3.26 per unit [8]. It is difficult for a grid-side converter (GSC) to

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Sliding mode controller (SMC) has been attract- ing lots of attention during recent years [44, 45, 46, 21, 34]. In order to design an SMC, mainly two steps are considered [8, 22, 10]. The first step is to design a sliding surface such that the desired **performance** has been guaranteed in a finite time. The second step is to design a controller to force the state variables to reach the sliding surface in a finite time and maintain the state variables on the sliding surface. Some of the main advantages of the SMC can be enumerated as disturbance rejection, insensitivity to parameters un- certainties, fast response, and finite time **performance** [25, 36, 35]. However, the SMC provides a discontin- uous **control** input signal which is not applicable in practice [20]. One of the fruitful ways to overcome

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A significant **improvement** are often achieved by mistreatment of an energetic mechanical system, that provided a better power from Associate in Nursing external supply to get **suspension** forces to realize the required **performance**. The force could also be a perform of many variables which may be measured or remotely detected by numerous sensors, that the flexibility are often greatly enhanced.With speedy advances in electronic technologies, the event of style techniques for the synthesis of **active** vehicle **suspension** systems has been an energetic space of analysis over the last 20 years to realize a stronger compromise throughout numerous driving conditions.Automotive Corporation’s square measure competitive to create a lot of developed cars, whereas comfort of passengers is a crucial demand and everybody expects from industries to boost it day by day. Therefore, so as to supply a sleek **ride** and satisfy passengers comfort, planning a contemporary mechanical system is necessary. a decent and economical mechanical system should speedily absorb road shocks then come to its traditional position, slowly. However, in an exceedingly passive mechanical system with a soft spring, movements are high, whereas mistreatment arduous springs causes arduous moves because of road roughness. Therefore, it’s troublesome to realize smart **performance** with a passive mechanical system.In order to meet the target of planning an energetic mechanical system i.e. to extend the **ride** comfort and road handling, there square measure 3 parameters to be determined within the simulations. The 3 parameters square measure the wheel deflection, dynamic tire load and automobile body acceleration. For definition of the allowable limits of automobile body acceleration, there's a frequency domain wherever citizenry square measure most sensitive to vibration

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A Distribution line consists of different loads which are linear as well as non linear. The non linear loads generate a harmonic and reactive current, which leads to poor power factor, low energy efficiency, and harmful disturbance to other appliances. **Active** Power Filters (APFs) which are custom power devices are used to reduce the harmonics improves the power factor and can achieve better voltage regulation. A shunt APF is connected in parallel with the nonlinear loads and functions as current sources to cancel the harmonic/reactive components in the line current by injecting a reverse current with equal magnitude and reverse in phase so that the current flow into and from the grid is sinusoidal and in phase with the grid voltage. A one cycle **control** technique is used to **control** the APF which is operated in the dual boost converter mode. A **fuzzy** **logic** controller is used to regulate the converter. The experiment is done using matlab simulation tool.

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The frequency domain methods require large memory, computation power and the results provided during the transient condition may be imprecise [4].On the other hand, the time domain methods require less calculation and are widely followed for computing the reference current. The two mostly used time domain methods are synchronous reference (d-q-0) theory and instantaneous real-reactive power (p-q) theory. Synchronous reference (d-q-0) theory is followed in this work. There are several current **control** strategies proposed in the literature [5]-[7], [9]-[10], namely, PI **control**, Average Current Mode **Control** (ACMC), Sliding Mode **Control** (SMC) and hysteresis **control**. Among the various current **control** techniques, hysteresis **control** is the most popular one for **active** power filter applications. Hysteresis current **control** [6] is a method of controlling a voltage source inverter so that the output current is generated which follows a reference current waveform in this paper.

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IJEDR1402009 International Journal of Engineering Development and Research (www.ijedr.org) 1374 PI-Controller: Fig. 5 shows the block diagram of the proposed PI **control** scheme for the **active** power filter. The DC side capacitor voltage is sensed and compared with a reference voltage. This error e = Vdc, ref −Vdc at the nth sampling instant is used as input for PI controller. The error signal is passed through Butterworth design **based** Low Pass Filter (LPF). The LPF filter has cut-off frequency at 50 Hz that can suppress the higher order components and allows only fundamental components. The PI controller is estimate the magnitude of peak reference current Imax and **control** the dc-side capacitor voltage of voltage source inverter. Its transfer function is represented as, H (s) = Kp+ KI/S

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The controller shown in Fig. 9 can be operated in two **control** modes (power and speed) in order to achieve above tasks effectively. The operating mode of the controller can be determined using selection switch. The controller works in power **control** mode with switch set to input 1 when the wind speed exceeds its rated speed to maintain the output power of the generator at rated value. However, for below rated wind speed controller not in operation where = 0 to extract the possible maximum wind power as governed by the Fig. 2. On the other hand, if the rotor speeds of induction generator increase to a threshold rotor speed ( TH r ) due to network disturbances, the controller input is set to input 2, as a result, it works in speed **control** mode where the transient stability enhancement is achieved with suitable pitch angle generation.

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In order to simplify the **control** scheme and to enhance the accuracy of the APF, an advanced **control** strategy is pi-vpi **control** is applied, as shown in Fig. 3. In Fig. 3, the pi-vpi **control** scheme is implemented by using only the supply current (iSa and iSb) without detecting the load current (iL,abc) and filter current (iF,abc). Thereby, the load current sensors and filter current sensors in the typical shunt APF shown in Fig. 2 can be eliminated. And also, the harmonic current detection is omitted. Due to the absence of harmonic detection, the pi-vpi **control** scheme can be implemented with only two loops: the outer voltage **control** and the inner current **control**. The outer loop aims to keep dc-link voltage of the APF constant through a PI controller, which helps the APF deal with load variations. The output of this **control** loop is the reference **active** current in the fundamental reference frame (i*Sd). Meanwhile, the reference reactive current (i*Sq) is simply set to be zero, which ensures the reactive power provided by the power supply to be zero. And, the reactive power caused by loads is supplied by the shunt APF. The inner loop is then used to regulate the supply current in the fundamental reference frame (iS,dq) by using the PI-VPI current controller. The output of this loop becomes the **control** signal (v*F,ab) applied to the four-switch APF which is implemented by the FSTPI. Since the current **control** is executed without the harmonic detector, the **control** **performance** of the APF only relies on the current controller. In the next section, the analysis and design of the proposed current controller will be presented.

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DSTATCOM is a component of Flexible AC Transmission Systems (FACTS) family that is associated in shunt with the power system [5]. By controlling the magnitude of the DSTATCOM voltage, the reactive power interactions between the DSTATCOM and the transmission line **control** the quantity of shunt compensation in the power system [6-7]. The DSTATCOM is a power electronics device depends on the law of injection or absorption of reactive current at the point of common coupling (PCC) to the power network. The benefit of the DSTATCOM is that the compensating current does not depend on the voltage level of the PCC and thus the compensating current is not minimized as the voltage drops. The supplementary motive for choosing a DSTATCOM as an alternative of an SVC are on the whole superior operational features, faster **performance**, lesser size, cost minimization and the capability to give both **active** and reactive power, thereby providing flexible voltage **control** for power quality enhancement [8].

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The response of railway vehicle model for a sinusoidal track irregularity with the amplitude of 7 cm and 3 rad/sec excitation frequency are presented in Figure- 10, Figure-11 and Figure-12. From the figures, it can be seen that **fuzzy** bogie-**based** skyhook is able to damp out unwanted vehicle motion effectively and shows better **performance** in all three **performance** criteria compared to **fuzzy** body-**based** skyhook and the passive system. This is due to the fact that **fuzzy** bogie-**based** skyhook is able to cancel out the effect of track irregularity before being transmitted to the car body. Table-4 shows the RMS values of simulation results on passive system, **fuzzy** body-**based** skyhook **control** and **fuzzy** bogie-**based** skyhook **control** for 3 rad/sec excitation frequency. Results, as shown in Table 4, strongly proved that **fuzzy** bogie-**based** skyhook improved all three **performance** criteria by 49.87 % for body lateral displacement, 35.93 % for unwanted body roll angle and 36.82 % for unwanted body yaw angle over passive system.

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A **fuzzy** **control** system is a system which emulates a human expert. Creating machines to emulate human expertise in **control** gives a new way to design controllers for complex plants whose mathematical models are hard to specify. The knowledge of the human operator would be put in the form of a set of **fuzzy** linguistic rules[19]. The **fuzzy** **logic** controller consists of three steps: fuzzification, **fuzzy** inference and defuzzification. Fuzzification is a Process of defining the **fuzzy** sets, and determination of the degree of membership of crisp inputs in appropriate **fuzzy** sets. **Fuzzy** inference is a process of evaluation of **fuzzy** rules to produce an output for each rule and aggregation or combination of the outputs of all rules. Defuzzification is a process of conversion of aggregate output **fuzzy** set to real-number (crisp) output values [15].

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In these algorithms, a compromise should be taken into complexity and the convergence speed. Numerous comparisons have been made among the proposed Fuzzycontroller and adaptive PI controller.In this paper, a novel utilization of the Fuzzycontrollerisarray for upgrading the LVRT capacity of grid-connected PV power plants. The DC- DC boost converter is utilized for a most extreme power point tracking operation **based** on the partial open circuit voltage strategy. The grid-side inverter is used to **control** the DC-link voltage and terminal voltage at the point of common coupling (PCC) through a vector **control** scheme. The **Fuzzy** controller is utilized to **control** the power electronic circuits due to its fast convergence. The PV power plant is connected with the IEEE 39-bus New England test system.the effectiveness of the proposed **control** strategy is compared with the obtained adaptive PI controller taking in to account subjecting the system to symmetrical and unsymmetrical faults.

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Applying these rules it would be possible to provide an accurate and fine adjustment. Invention of automatic adjusting methods has paved the way for integrating PID controllers with this function. Ziegler and Nichols have proposed a set of rules for adjusting controllers **based** on step response obtained from examination or value of K p which leads to boundary stability when proportional **control** is used. Ziegler-Nichols rules are employed when mathematical model of system in not known, however they can also be used in the design of systems mathematical model of which are known. These rules result in a set of values for K p , T i , and T d coefficients and consequently system stability. Ziegler-Nichols rules are vastly used for industrial **control** systems the dynamic behaviors of which are not known clearly. Usability of these rules has been validated for a long time. However, these rules can also be used for systems with known dynamic behavior[16, 17].

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Power factor correction (PFC) has now become a very important field in power electronics. .Power factor correction can be broadly classified into two - passive and **active**. In passive PFC, only passive elements are used to shape the input line current and the output voltage cannot be controlled. In the case of **active** PFC circuit, an **active** semiconductor device is used together with passive elements to shape the input current .In this the output voltage can be controlled.

In this paper Dynamic Voltage Restorer (DVR) is used to overcome the voltage sag and swell with the use of hybrid multilevel inverter is presented to improve the Power Quality. This is proved by MATLAB simulation results. The result indicates that the load voltage is improved within few seconds using DVR when faults or any disturbance occur in distribution system which shows the DVR’s excellent **performance** and the **control** system in order to protect sensitive equipment from PQ disturbances.