Top PDF Improvement of ride performance with an active suspension based on fuzzy logic control

Improvement of ride performance with an active suspension based on fuzzy logic control

Improvement of ride performance with an active suspension based on fuzzy logic control

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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Fuzzy-Skyhook Control for Active Suspension Systems Applied to a Full Vehicle Model

Fuzzy-Skyhook Control for Active Suspension Systems Applied to a Full Vehicle Model

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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Fuzzy Logic Controller based Shunt Active       Filter

Fuzzy Logic Controller based Shunt Active Filter

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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AN ADVANCED FUZZY LOGIC CONTROL BASED ACTIVE POWER FILTER WITH RESONANT CONTROLLER

AN ADVANCED FUZZY LOGIC CONTROL BASED ACTIVE POWER FILTER WITH RESONANT CONTROLLER

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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Development of an optimal and fuzzy semi-active control  system for vehicle suspension

Development of an optimal and fuzzy semi-active control system for vehicle suspension

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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FUZZY LOGIC BASED NOVEL PITCH CONTROL  FOR STABILITY IMPROVEMENT IN DFIG BASED WIND ENERGY SYSTEMS

FUZZY LOGIC BASED NOVEL PITCH CONTROL FOR STABILITY IMPROVEMENT IN DFIG BASED WIND ENERGY SYSTEMS

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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Fuzzy-Logic-Based Adaptive Proportional-Integral Sliding Mode Control for Active Suspension Vehicle Systems: Kalman Filtering Approach

Fuzzy-Logic-Based Adaptive Proportional-Integral Sliding Mode Control for Active Suspension Vehicle Systems: Kalman Filtering Approach

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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ACTIVE BODY CONTROL SUSPENSION

ACTIVE BODY CONTROL SUSPENSION

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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One Cycle Control Based Active Power Filter for Power Quality Improvement Using Fuzzy Logic Control
R Srinivasa Rao & Ch Ravi Kumar

One Cycle Control Based Active Power Filter for Power Quality Improvement Using Fuzzy Logic Control R Srinivasa Rao & Ch Ravi Kumar

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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Fuzzy logic control 
		based three phase shunt active filter for power quality improvement in 
		distribution system

Fuzzy logic control based three phase shunt active filter for power quality improvement in distribution system

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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Fuzzy logic based power quality improvement using shunt active filter

Fuzzy logic based power quality improvement using shunt active filter

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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Performance improvement of a wind energy system using fuzzy logic based pitch angle control

Performance improvement of a wind energy system using fuzzy logic based pitch angle control

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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Power Quality Improvement with a Shunt Active Power Filters using Fuzzy Logic

Power Quality Improvement with a Shunt Active Power Filters using Fuzzy Logic

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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Performance Improvement of External Inductor based Voltage Controlled DSTATCOM using Fuzzy Logic Controller

Performance Improvement of External Inductor based Voltage Controlled DSTATCOM using Fuzzy Logic Controller

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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SEMI-ACTIVE SECONDARY SUSPENSION CONTROL USING FUZZY SKYHOOK FOR IMPROVING RAILWAY VEHICLE DYNAMICS PERFORMANCE IN LATERAL DIRECTION

SEMI-ACTIVE SECONDARY SUSPENSION CONTROL USING FUZZY SKYHOOK FOR IMPROVING RAILWAY VEHICLE DYNAMICS PERFORMANCE IN LATERAL DIRECTION

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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Vehicle Active Suspension System performance using
Different Control Strategies

Vehicle Active Suspension System performance using Different Control Strategies

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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Fuzzy Logic Controller for Low Voltage Ride through Capability Improvement of Grid Connected Photovoltaic Power Plants

Fuzzy Logic Controller for Low Voltage Ride through Capability Improvement of Grid Connected Photovoltaic Power Plants

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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Evaluating the Performance of Fuzzy Logic and
Classic(PID) Active Controllers in Control of
Surge Phenomenon in Centrifugal Compressors

Evaluating the Performance of Fuzzy Logic and Classic(PID) Active Controllers in Control of Surge Phenomenon in Centrifugal Compressors

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 Improvement using Bridgeless Resonant Pseudoboost Rectifier Based on Fuzzy Logic Control

Power Factor Improvement using Bridgeless Resonant Pseudoboost Rectifier Based on Fuzzy Logic Control

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.

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Fuzzy logic control based dynamic voltage restorer for 
		power quality improvement in distribution system

Fuzzy logic control based dynamic voltage restorer for power quality improvement in distribution system

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.

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