Due to **nonlinear** characteristics of **boost** **converter**, some researches have employed **nonlinear** controller such as **sliding** **mode** **control**. The robustness against parameter uncertainty and disturbance are the main reason why **sliding** **mode** **control** is utilized to **control** **nonlinear** **system**, including **boost** **converter**. Many **sliding** **mode** **control** methods [9-13] had been applied to **boost** **converter**. However, in practical, this **control** method requires to be fully known some variables, such as input voltage, inductor current, output voltage, and resistance load. As consequences, many sensors are needed to be installed to acquire those variables as input **control**. Implementing those methods causes increasing cost production and adding more space and weight in real **system**. Therefore, to reduce the number of sensors, **nonlinear** disturbance **observer** [13-15] is designed to estimate some variables, such as inductor current, output voltage, resistance load, and input voltage generated from solar array. The **nonlinear** disturbance **observer** accurately generates the estimated value of resistance load and input voltage such that when the variations of those variables exist, the proposed controller is still able to overcome those disturbances. In **sliding** **mode** **control** design, steady state error regulation needs to be considered. However, in [13], it is employed standard **sliding** **surface** and only use equivalent **control** signal to regulate **boost** **converter**. This can cause the output voltage response cannot track the varying desired output voltage and leads to steady state error. To enhance **system** performance, **adaptive** **sliding** **mode** **control** is applied to the **boost** **converter** for overcoming parameter uncertainty and disturbance [16-17]. Steady state error can be eliminated by constructing **PI** **sliding** **surface**, while ensuring **sliding** **mode** in finite time is employed reaching law dynamics and incorporates it to natural **control** signal. Therefore, **nonlinear** **observer** **based** **adaptive** **sliding** **mode** **control** with **PI** **sliding** **surface** is proposed for **boost** **converter**. The main contribution of this paper is to improve the **system** performance of voltage regulation **boost** **converter** using the combination of **nonlinear** **observer** and **adaptive** **sliding** **mode** **control** by modifying the conventional **sliding** **surface** into **PI** structure **sliding** **surface**. In addition, the stability of proposed method is proven by using direct Lyapunov method.

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Abstract— This paper presents a photovoltaic (**PV**) **system** with an **adaptive** **sliding** **mode** **control** **based** maximum power point tracking (MPPT) algorithm. The goal of this work is to maximize power extraction from the photovoltaic generator. This aim is achieved by using an **adaptive** **sliding** **mode** con- troller (ASMC) that drives a **boost** **converter** connected between the **PV** generator and the load. The robustness and stability of the proposed controller are investigated against weather changes. Simulation results with real data, under MATLAB- SIMULINK, are given to demonstrate the effectiveness of the proposed approach.

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The average generalized **PI** output feedback regulator as a steer for defining the switched implementation of the average **sliding** **mode** features through a sigma-delta modulation strategy has been addressed [11]. The con- trol loop of a parallel connection of two non-identical paralleled positive output elementary super lift Luo con- verters using the SMC theory for current distribution **control** in continuous conduction **mode** [12]. A droop me- thod has been proposed for the **converter** parallel operation, which adaptively controls the reference voltage of each module. The scheme improves the output voltage regulation and the current sharing of the conventional droop method [13]. A robust controller for parallel dc-dc buck converters has been coined by combining the concepts of integral-variable-structure and multiple-**sliding**-**surface** **control** [6]. Grid connected solar **PV** **system** with SEPIC **converter** compared with parallel **boost** **converter** **based** MPPT [14]. **Nonlinear** back-stepping adap- tive controller has been proposed for the design of parallel DC-DC buck converters with uncertainties of load and power disturbance. The relationship between the **control** elements and circuit parameters has been deter- mined by simulation analysis. The relationship between current sharing difference and circulating current for two parallel connected dc-dc converters has been investigated [15]. Although there may exist a trade-off be- tween current sharing difference and voltage regulation, the proposed droop index algorithm gives better per- formance and low voltage regulation. The detailed analysis and design procedure are explained for two dc-dc **boost** converters connected in parallel. The effectiveness of proposed method is verified using MATLAB simu- lation.

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The switch **mode** DC-DC converters are some of the simplest circuit which converts power level of DC power effectively. It has wide application in modern computer, DC motor drive, power **system**, automotive, aircrafts etc. the commonly used **control** methods are pulse width modulation (PWM), voltage **mode** **control**, PWM current **mode** **control** with proportional (P), proportional integral (**PI**), and proportional integral derivative (PID) controller. But this **control** method cannot perform satisfactory under large load variation so non liner **control** technique is in picture. The dc-dc converters, which are non-linear and time variant **system**, and do not lend themselves to the application of linear **control** theory, can be controlled by means of **sliding**-**mode** (SM) **control**, Which is derived from the variable structure **control** **system** theory (VSCS). Variable structure systems are systems the physical structures of which are changed during time with respect to the structure **control** law. The instances at which the changing of the structure occurs are determined by the current state of the **system**. Due to the presence of switching action, switched-**mode** power supplies (SMPS) are generally variable structured systems. Therefore, SM controllers are used for controlling dc-dc converters. Though SM **control** compiles of various advantages, SM controlled converters suffers from switching frequency variation when the input voltage and output load are varied. Hence there are many **control** methods which have been developed for fixed switching frequency SM **control** such as fixed frequency PWM **based** **sliding** **mode** controllers, **adaptive** SM controller, digital fuzzy logic SM controller, etc. In case of **adaptive** **control**, **adaptive** hysteresis band is varied with parameter changes to **control** and fixate the switching frequency. But, these methods require more components and are unattractive for low cost voltage conversion applications.

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performance. This cascade **control** is employed at the low hierarchy level in order to **control** the **boost** **converter**. This level also uses a feedforward **control** which eliminates all the demerits of feedback **control**. As a result, the speed and performance of the overall **system** improves, if feedforward **control** together with feedback action is employed. Differential flatness is used at the high hierarchy level which ensures that the **system** is controllable or not. If the **system** is controllable, then the **system** is said to be stable. DC motor is placed in the high hierarchy level. The low hierarchy level ensures that the **converter** output voltage is equal to the one required to drive the DC motor whereas the high hierarchy level is designed to complete the velocity tracking task developed for the DC motor.

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In this paper, a **nonlinear** **sliding** **surface** has been proposed. It is shown that the error states converge to an arbitrarily small region centred at the origin within a finite time and thereafter asymptotically converge to the equilibrium point. A **nonlinear** **sliding** **mode** **control** law has been developed using an improved version of the exponential reaching law. The method is applied for **control** of a robot manipulator **system**. The residual set defined by the **sliding** **mode** has been determined. The simulation results show that the proposed method has more precise tracking, faster convergence, and stronger robustness against **system** uncertainty and external disturbances than an existing approach. Due to the smooth **control** signal, the proposed approach reduces the chattering effectively and has a wider range of applications. Future work will focus on application of the results experimentally and in industry.

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The main aim of this project is to revise and define the automatic steering con- trol of passenger cars for general lane-following maneuver. A 2-DOF controller **based** on H loop-shaping methodology is used by lateral vehicle **control** **system** is success- fully designed [5]. The 2-DOF controller supplies good lane-keeping and lane-change abilities on both curved and straight road segments. Moreover, it provides a com- putationally efficient algorithm and does not require explicit knowledge of the vehicle uncertainty. But, the test results show that the higher the vehicle’s speed, the less stable the vehicle **system**.

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The robustness to the uncertainties becomes an important aspect in designing any **control** **system**. **Sliding** **mode** **control** (SMC), originally studied by Utkin [2], is a robust and simple procedure for the **control** of linear and **nonlinear** processes **based** on prin- ciples of variable structure **control** (VSC). It is proved to be an appealing technique for controlling **nonlinear** systems with un- certainties. Figure 1 shows the graphical representation of SMC using phase-plane, which is made up of the error (e(t)) and its derivative ( e(t)). It can be seen that starting from any initial con- ˙ dition, the state trajectory reaches the **surface** in a finite time (reaching **mode**), and then slides along the **surface** towards the target (**sliding** **mode**).

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The fuzzy logic controller integrates the fuzzy approximation theory and the SMC have been developed. Moreover, **adaptive** fuzzy **sliding** **mode** **control** (AFSMC) schemes have been proposed [6–11]. The **adaptive** fuzzy controller incorporated with a SMC is developed. In other words, the **adaptive** fuzzy logic systems are utilized to approximate the unknown **system** functions in designing the SMC of **nonlinear** **system**. In order to improve the performance of AFSMC, an **adaptive** fuzzy logic **control** combining linearization feedback and SMC is considered in this paper. It is proved that the closed-loop **system** is globally stable in the Lyapunov sense, if all the signals are bounded and the **system** output can track the desired reference output asymptotically with uncertainties.

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systems with both packet dropouts and channel noises in the networked scenario. Literature [11] developed a distributed state estimation method **based** on MHE for a class of two- time-scale **nonlinear** systems. These works have contributed to the teleoperation **system** **based** on network communication which enhanced robustness of the networked **control** **system**. Advanced controllers, including **sliding** **mode** con- trollers (SMCs) and **adaptive** controllers, have recently been developed for teleoperation systems in order to obtain accu- rate trajectory tracking and faithful force feedback. One **sliding** **mode** approach, the three-**mode** **control** scheme, can implehent a position–position, force–force, or force–position scheme, and the results show good trajectory tracking per- forhance [12]. However, it does not consider time delay. To solve the problem of the adverse effects of parametric uncertainties, an **adaptive** **sliding** **mode** **control** scheme was proposed by Motamedi et al. [13]. The algorithm has been verified on a teleoperation **system** with a single degree of free- dom (DOF). Yang and Hua [14] proposed a novel nonsingular fast integral terminal **sliding** **mode** (NFITSM) for a teleoper- ation **system**, and practical experiments on one-DOF motion tracking have now been completed. A nonsingular terminal **sliding** **mode** and **adaptive** finite-time **control** method was proposed by Zhang et al. [15], and simulation results have verified the effectiveness of this method. These methods are useful attempts to design feedback controllers that improve the performance and stability of a teleoperation **system**.

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This paper proposes the implementation of a fuzzy **based** robust **sliding** model **control** design to obtain voltage regulation in a **boost** **converter** with high dc gain. The proposed controller has an inner loop **based** on **sliding**- **mode** **control** whose **sliding** **surface** is defined for the input inductor current. The current reference value of the **sliding** **surface** is modified by a fuzzy logic controller in an outer loop that operates over the output voltage error.Robustness is analyzed in depth taking into account the parameter variation related with the operation of the **converter** in different equilibrium points. Simulations and experimental results are presented to validate the approach for a 20–100-W **boost** **converter** stepping-up a low dc voltage (15–25-V dc) to a 400-V dc level.

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ABSTRACT:The main objective of this paper is to design a SM Controller for a buck **converter** to convert a dc input voltage to the required lower dc output voltage level for lower power application to solve the problem of voltage regulation and high power loss of the linear voltage regulator circuit. The **converter** uses a switching scheme which operates the switch MOSFET in cutoff and saturation region to reduce power loss across MOSFET. Then, the output voltage is controlled using SM **Control** technique to get the desired output voltage level. The design is **based** on low power application such as laptop charger, mobile charger etc. The circuit is simulated using MATLAB/SIMULINK software to obtain desired response.The SM **control** is a type of **nonlinear** **control** introduced initially as a means for controlling variable structure systems. The main advantage of the SM **control** over other types of **nonlinear** **control** methods is its ease of implementation. This makes it well suited for common DC–DC power regulation purposes. The SM **control** is naturally well suited for the **control** of variable structure systems. Characterized by switching, power converters are inherently variable structure systems. It is therefore; appropriate to apply SM **control** on power converters.

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NVIRONMENTAL consequences of using fossil fuels and also depletion of these reserves in recent years have caused researches to be focused on renewable energies and interface converters. DC-DC converters are mostly used in these applications. In the last decade, lots of DC-DC converters have been proposed for various applications such as extracting maximum power from photovoltaic (**PV**) and fuel-cell (F.C) systems, portable devices, hybrid electric vehicles (HEV) and etc. [1-5]. Among DC-DC converters with different applications, BDCs have become necessary for HEV applications since energy storage systems are required for cold starting and battery recharging [4]. BDCs are used in HEVs [6-9], uninterrupted power supplies (UPS) [10-12], F.Cs [12-16], PVs [17,18], battery chargers [19-21] and many other industrial applications. BDCs are also used in DC micro-grid. Regulation of DC bus voltage and uninterrupted power

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The expression for the average voltage applied across the winding is given by (25). The dc signal output of F/Vconverter is given as one of the input to analog to digital (A/D) **converter** of the DSP processor to determine the actual speed of the motor. The reference speed is set through a potentiometer and voltage follower and it is given as another input to the A/D **converter** to determine the reference speed. There is also another provision to set the reference speed from watch window of code composer studio software. The function of the DSP processor is to compute the error and change in error, store these values, compute the Fuzzy controller output, determine the new duty-cycle for the switching devices and perform electronic commutation. The high speed digital buffer IC 74HCT244 is used to interface the DSP with the IGBT hybrid power module ICand the hall sensor circuit. The flowchart for the fuzzy controller program implemented in DSP is shown in Fig 6.

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Abstract—This paper studies the application of the **sliding** **mode** **control** method to reduce the vibration of flexible structure with piezoelectric actuators and strain gage transducer in practical complex environment. The state- space dynamic model of the **system** was derived by using finite element method and experimental modal test. The structure is subjected to arbitrary, unmeasurable disturbance forces. Taking into account the uncertain random disturbance and measurement noise, Kalman filter is chosen as the state estimator to obtain the modal coordinates and modal velocities for the modal space **control**. A **sliding** **mode** controller is adopted due to its distinguished robustness property of insensitiveness to parameter uncertainties and external disturbances. The **sliding** **surface** is determined by using optimization method, and the **sliding** controller is designed by applying Lyapunov direct method. That is, along the switching **surface**, the cost function of the states is minimized. A real-time **control** **system** was built using dSPACE DS1103 platform, and vibration **control** tests were performed to experimentally verify the performances of the proposed controller. The results of experiment show the controller can effectively attenuate elastic vibration of the structure.

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Abstract — This paper presents a time-varying **sliding** **mode** **adaptive** controller in order to handle the stick-slip oscillation of **nonlinear** rotary drilling **system**. The time-varying **sliding** **mode** controller with strong robust has two time-varying **sliding** surfaces, one of them induced time-varying integral **sliding** **mode** **control** can **control** the transient stage of the rotary drilling **system** and ensure the **system** remains the **sliding** condition whatever in usual or existing the parameter changes and disturbances to arrive at a controller capable of global stability. The herein developed controller is, a time-varying **sliding** **mode** **adaptive** controller has tracking performance and identification of drilling parameters. Lyapunov principles have been carried out to verify the stability and robustness of **system**. The simulation results show that the controller has faster dynamic responses and suppress stick-slip in oil well drill string, can achieve global stability of rotary drilling **system**.

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There has been a widespread interest in using advanced **control** techniques to improve the performance of vehicle suspension **system**. Performance of the suspension **system** has been greatly increased due to increasing vehicle capabilities. Several performance characteristics have to be considered in order to achieve a good suspension **system**. These characteristics deal with regulation of body movement, regulation of suspension movement and force distribution. Ideally the suspension should isolate the body from road disturbances and inertial disturbances associated with cornering and braking or acceleration [1]. During the design of a suspension **system**, a number of conflicting requirements have to be met [2]. The suspension must be able to minimize the vertical force transmitted to the passengers for passengers comfort. These objectives can be achieved by minimizing the vertical car body acceleration. Also, optimal contact between wheel and road **surface** is needed in various driving conditions in order to maximize safety [3]. An early design for automobile suspension systems was focused on unconstrained optimizations for passive suspension **system** which indicate the desirability of low suspension stiffness, reduced unsprung mass, and an optimum damping ratio for the best controllability [4]. Thus the passive suspension **system**, which

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Numerous techniques have been proposed to eliminate this phenomenon in SMC, such as saturating approximation, integral **sliding** **control** and boundary layer technique. To tackle these difficulties, fuzzy logic controllers (FLC) are often used to deal with the discontinuous sign function in the reaching phase of SMC [10]. As well known, fuzzy logic **control** (FLC) is a knowledge-**based** **control** approach which can mimic human experience in controlling complex systems and has excellent capability to deal with **nonlinear** plants [21,22]. This method is a good choice for inferring the **control** gains of VSSM controller in wind turbines through fuzzy-rules-**based** inference. Meanwhile, the modeling error and the uncertain disturbance of wind power **system** can be estimated to obtain the appropriate switch gain through a fuzzy inference **system** with single input and single output. Many new algorithms have been proposed **based** on the integration of the fuzzy logic and the SMC [11]. These approaches are similar in the aspect that they directly approximate the **sliding** **mode** **control** law by fuzzy approximations. The main advantage of this **control** scheme is its ability to eliminate the chattering using a fuzzy **sliding** **surface** in the reaching condition of the SMC [12]. Recently, **adaptive** fuzzy SMC methods are also used for this purpose, which is shown to be quite effective [13]. In contrast to a conventional feedback **control** algorithm, there is a fuzzy **control** algorithm consists of a set of heuristic decision rules that can be represented as a non-mathematical **control** algorithm. This algorithm proves to be very effective especially when the precise model of the **system** under **control** is not available or expensive to prepare. This combination (i.e., F-SMC) provides the mechanism to design robust controllers for **nonlinear** systems with uncertainty.

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In spite of being a potential candidate, many factors associated with fuel cell application still prevent its commercialization. The high capital cost of fuel cell **based** electricity generation **system** due to high cost of fuel, catalyst, components etc, complex BOP (Balance of Plant) requirements, custom designed power conditioning unit, high operating and maintenance cost, sophisticated and advanced controller requirements to **control** the non-linearity of cell has limited its growth and development.

3.4.1 Implementation of Particle Swarm Optimization Algorithm 64 3.4.2 Parameters Selection for Particle Swarm Optimization Algorithm 67 3.4.3 Inertia Weight for Particle Swarm Optimization Algorithm 69 3.4.4 Constriction Factor for Particle Swarm Optimization Algorithm 70 3.5 Integration of the Particle Swarm Optimization Algorithm to the **Sliding**

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