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Maryam Bayad. PH.D
Q.Karimi Doostsn.PH.D
Zakarya Bezdodeh
 
Abstract
This article means to survey the focus of attention and the focus of narration in the narrative theories within a novel entitled as ”Turn of the Screw”. Within the narrative approaches, there is no tendency to make discriminations between the two aspects of point of view in narration. The distinction between the two concepts of narration and focus of observation were of notice before Gerard Genette; the famous French narrative theorist. Yet Gerard Genette made a systematic study on this distinction. Genette used these two different terms for point of view, in which the first one relates to the act in which verbal transfer of story takes place by the storyteller, ( narrator in literature) while the second one refers to the focus through which the narration is observed from these aspects; setting ( place –time) , psychologically and ideologically. Genette believes that in every narrative work two distinct aspects of point of view might be manifested through one single person or be transferred simultaneously through a few narrators and observers. In a narrative work such as “Turn of the Screw” by Henry James, which is as a matter of fact a prominent work in narrative literature, one can observe different aspects of point of view, in addition to focus of attention there. Therefore this research intends to study the two distinct aspects of point of view as mentioned and further on point to their significance within the best world literary works.
 
 

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This paper proposes a new method of gain scheduling control design for a nonlinear system which is described as linear parameter varying form. A performance measure based on Linear Matrix Inequality is introduced. To consider stability and performance measures in design process, the H∞ loop-shaping method is used to design the local controllers, which can be described as state feedback observer based structure. By introducing the stability and performance covering condition for the linear parameter varying system, a new interpolation law is proposed, and it is proofed that the resultant controller can preserve the performance measure for the observer based structure for all values of the scheduling parameter. Also the closed loop stability is guaranteed. The method is successfully applied on the control of a well-known benchmark system, namely, the autopilot for a pitch-axis model of an air vehicle. The performance and effectiveness is evaluated against disturbances and parameter uncertainties using computer simulation.

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This paper addresses adaptive observer design problem for joint estimation of the states and unknown parameters for a class of nonlinear systems which satisfying one-sided Lipschitz and quadratic inner bounded conditions. It’s shown that the stability of the proposed observer is related to finding solutions to a quadratic inequality consists of state and parameter errors. A coordinate transformation is used to reformulate this inequality as a linear matrix inequality (LMI). Sufficient conditions that ensure the existence of adaptive observer are expressed in forms of LMIs, which are easily tractable via standard software algorithms. If the proposed conditions are satisfied, then the state estimation errors are guaranteed to converge to zero asymptotically while, the convergence of the parameters is guaranteed when a persistence of excitation condition is held. The effectiveness of the proposed method is shown by simulation for the joint estimation of states and parameters of a numerical system.

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 Non-fragile observer design is the main problem of this paper. Using continuous frequency distribution, the stability conditions based on integer order Lyapunov theorem are derived for Lipschitz class of nonlinear fractional order systems. The proposed observer is stable beside the existence of both gain perturbation and input disturbance. For the first time, in this paper a systematic method is suggested based on linear matrix inequality to find an optimal observer gain to minimize both the effects of disturbance on the synchronization error and norm of the observer gain. A comparison has done between this observer and previous research on resilient observer design for nonlinear fractional order systems based on fractional order Lyapunov method. The comparison shows a much broader range of feasible response for the proposed method of this paper besides simpler computing. After presenting thediscussion, chaos synchronization is simulated to show the effectiveness of the proposed method in the end.

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In this paper, an innovative adaptive output feedback control scheme is proposed for general multi-input multi-output (MIMO) plants with unknown parameters in a regulation task; such that the outputs of the plant converge to zero as well as the control gains remain uniformly bounded. First an adaptive observer is designed to estimate the state variables and system parameters by using the inputs and outputs of the plant. Then a linear combination of the estimated states by adaptive control gains is used to design a robust adaptive controller. Some theorems are given to show the convergence of the modeling errors and the control gains. The proposed controller is used to control a two degree of freedom robot manipulator such that the robot moves from any initial configuration to zero position. Simulation results exhibit the effectiveness of the proposed scheme to control the robot manipulator with different initial conditions and parameter perturbations.    

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Friction is an inevitable issue in most of mechanical servo systems. Friction has a counter-effect on the dynamic performance of servo mechanisms and needs to be considered in the design process. Particularly, in high-performance motion systems, friction can severely deteriorate performance and can cause tracking errors, longer settling time and limit cycles. In this paper, a new method is presented based on the adaptive fuzzy sliding mode control strategy for position control of mechanical systems. The first order dynamic LuGre model is used for the design of friction observer. Unlike previous papers, the control input and friction are applied to the system with non-equal gains. An adaptive law is employed for the estimation of the ratio between the gains of input and friction terms. Various uncertainties on parameters of friction also are considered and an appropriate control strategy is designed to tackle these uncertainties.

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This paper deals with the design of an unknown input observer (UIO) with the assumption that the well-known observer matching condition is not satisfied. The proposed method can be used for fault detection problems with the use of residual vector. The basis of method is to compensate the unmatched uncertainties with the use of a set of auxiliary outputs. The introduced auxiliary outputs are obtained from successive integration of the system measurements and known inputs. Then, an unknown input observer is proposed which estimates exponentially the outputs. Therefore, the residual vector, generated from the estimated outputs and the actual outputs, will be obtained which insensitive to the unmatched disturbances. At the same time, the sensitivity of the proposed residual vector to the fault in sensors is investigated. The generated residual vector will be more robust against the presence of noise in the measurements. It is shown through numerical simulations that the proposed residual vector is sensitive to the presence of fault in sensors while it is insensitive to the presence of the unknown input. In addition, a comparison with a derivative based method is presented.

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Nonlinear factors such as air compressibility, leakage and friction make the control of pneumatic systems complex. Model-based robust control strategies are appropriate candidates for pneumatic systems, however in such controllers the measurement of state variables of the system are needed. In a pneumatic system the state variables are position and velocity of the actuator, and pressure in both sides of the cylinder. Pressure measurement is usually obtained by means of costly and low response sensors. A better way to deal with the measurement problem is to use observers to reconstruct the missing velocity and pressure signals. However the problem in a pneumatic system is that the system is not observable and pressure signals could not be observed by means of position signals only. To deal with this problem, in this paper, the pneumatic actuator is modeled as two separate chambers and the resulting subsystems are observable independently. High gain observers are designed for mentioned subsystems and for each chamber the pressure of the other chamber is considered as a disturbance. The input signal for each observer is the actuator position signal only. Finally a sliding-mode control strategy is designed for position tracking and experimental results verify that both controller and observer objectives are satisfied.

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This paper presents a new load frequency control (LFC) design in a multi area power system by using local observers. Firstly, sliding mode observers with unknown inputs are designed for each area to estimate the state variables locally. In this stage interconnections and load variations are assumed as unknown inputs. Then, local state feedback and output integral are used to attenuate the effect of load variations in each area. Analysis and simulation results for a three-area interconnected power system show improvements on closed loop performance in comparisons with other existing methods.

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This paper talked about spacecraft formation flying control. Leader-flower structure is used in formation flying. A non-linear PID controller is designed based on predictive control. The formation relative equation is obtained from nonlinear Hill equation. First, the frequency control is achieved with the using of predictive control algorithm. In control frequency, disturbances have been replaced from disturbance observer. Equations are rewritten in the form of PID gains. Stability of the closed-loop system is proven by closed-loop error dynamics. Nonlinear PID controller performance in the pursuit of desired arrangement has been tested in simulations. Also effects of various factors on the quality of controller results are studied. It is shown that choosing predictive horizon time and disturbance observer gains have the most effect on system response. It is shown that if predictive time increase the settling time increase and the control effort decrease. if disturbance observer gain increase from a limit, it has no effect on settling time but control effort increase. As shown in simulation, the tracking response show the controller method ability. the simulation show the ability of this nonlinear control method in tracking.

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This paper is devoted to sensor fault detection in linear systems with observer-based approach. It is assumed that the system has linear dynamics with the presence of uncertainties. The uncertainties are modeled as unknown input (disturbance), while it is assumed that the well-known observer matching condition is not necessarily satisfied. To decouple the unknown-input effects, and distinguish their effects from the fault effects, an equivalent dynamic system is proposed which is independent from the unknown input. The equivalent system is constructed by the use of a unique integral filter. The introduced integral-filter, which is called buffer-based integral filter in this paper, has frequency response similar to the low-pass filter. Hence, the capability of noise filtration will also be provided. The construction of the equivalent dynamic system is achieved from the use of multiple successive buffer-based integrators and the number of successive filters is related to relative degree between the unknown input and the sensor output. Then, an unknown input observer is proposed for the equivalent system, and therefore, a disturbance-decoupled and fault-sensitive with exponential-convergent toward-zero residual vector will be generated. Although, the generated residual vector can be used for sensor and actuator fault diagnosis problems; however, the focus of this paper will be on the sensor fault detection. Finally, the applicability of the proposed method will be investigated via simulation of a simple inverted-pendulum on a horizontal-moving cart.

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This paper presents a new scheme based on state estimation to diagnosis an actuator or plant fault in a class of nonlinear systems that represent the nonlinear dynamic model of gas turbine engine. An optimal nonlinear observer is designed for the nonlinear system. By utilizing Lyapunov's direct method, the observer is proved to be optimal with respect to a performance function, including the magnitude of the observer gain and the convergence time. The observer gain is obtained by using approximation of Hamilton -Jacobi -Bellman (HJB) equation. The approximation is determined via an online trained neural network (NN). Using the proposed observer, the system states and the fault signal can be estimated and diagnosed, respectively. The proposed approach is implemented for state estimation and fault detection of a gas turbine model subject to compressor mass flow fault. The simulation results illustrate that the proposed fault detection scheme is a promising tool for the gas turbine diagnostics.

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In this paper, the performance of a single-axis attitude control with pulse-width pulse-frequency (PWPF) modulation is enhanced using a modified proportional-integral-derivative (PID) controller for a rigid satellite with on-off thruster actuators. For this purpose, the well-known observer-based PID approach is utilized. The on-off thruster actuator is modeled with a constant delay followed by a second-order binomial transfer function. The modulator update frequency is limited to 40 Hz as an input to the on-off thruster actuators. In this study, the design criteria of pointing accuracy, overshoot of the attitude response, fuel consumption, and the number of thruster firings are considered for a step external disturbance (with different values). The parameters of the observer-based PID controller are tuned using parametric search method. Simulation results show that the fuel consumption and settling time of the observer-based approach are considerably decreased with respect to those of PID controller with PWPF modulator. Moreover, the overshoot of the observer-based approach is omitted. Finally, the robustness of the observer-based modified PID controller is investigated in presence of uncertainties in satellite moment of inertia and thrust level of on-off actuators.

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Full feedback data is mostly essential in control design. The measurement of the variation of flexible joint robot (FJR) actuators is not easy as the measurement of the changes of FJR links’ angles. The measurement of the states is also affected by noise, and the disturbance in the workspace of the robot is not ignorable. Hence a state observer or a nonlinear estimator is necessary to improve the performance of the dynamic system. The state-dependent Riccati equation (SDRE) is one of the most promising nonlinear optimal control methods for estimating variables of systems. Systematic procedure, simple structure, and incorporating wide range of systems (under observability condition) are some advantages of SDRE method. The majority of nonlinear techniques linearize the model, but the SDRE directly uses the nonlinear state space; it is one of the reasons for its precision and flexibility in design with respect to other methods. The goal of this work is to merge the SDRE controller and estimator simultaneously to reduce the state error of the system in presence of external disturbance and measurement noise. So, first, the controller and the observer formulation has been stated. Then, the procedure has been applied to design and to simulate a 3 DOF robot arm with flexible joints. Next, the process has been tested experimentally using Scout robot and the simulation results have been verified. Finally, the proposed method of this paper has been compared with the optimal sliding mode.

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Piezoelectric actuators (PA) are widely used in electromechanical system thank to interesting properties such as: high resolution, fast response, wide bandwidth, mechanical simplicity, high stiffness. Despite these unique desirable properties, they suffer from nonlinear behaviors which adversely affect the positioning accuracy. Among them, hysteresis between applied voltage , actuator position is the most important nonlinearity which can lead to significant error if not compensated. In this study, a sliding mode controller associated with an unknown input observer, which uses the position feedback provided by a selfsensing circuit, is suggested to use in micro positioning applications. The selfsensing technique is based on the linear relation between position , charge, which is measured by an active charge measurement circuit. The advantages of proposed scheme could be summarized as follows. It is a sensorless method which does not need an external position sensor. It does not need any operators to model hysteresis or its inverse. It has improved performance in comparison to traditional controllers like proportional integral (PI) controller. Obtained experimental results demonstrate the effectiveness of proposed method to use in micro-positioning applications.

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In this paper, designing optimal linear controller for non-holonomic wheeled mobile robots based on Linear Quadratic Gaussian (LQG) controller is considered. Parameters of the governing kinematics equation of motion are derived based on system identification techniques by using real experimental data. The autoregressive moving average-exogenous input (ARMAX) models are taken into account. The least square (LS) algorithm is utilized to estimate the parameters of the model. Thereafter, optimal model order and the performance of the model are determined using several statistical analyses. Also, the recursive LS (RLS) with forgetting factor is employed to demonstrate the convergence of the model parameters. Verification of discrete linear model implies the possibility of using the linear controllers. Therefore, the optimal LQG controller for wheeled mobile robots is designed to track the reference trajectory. The Kalman observer is used to estimate un-measurable states of the robot. Furthermore, the optimal linear control together with system identification techniques yields simpler controller than nonlinear controllers. Designed controller and verified model are simulated using the MATLAB-Simulink software. Results show the effectiveness of the controller in tracking the desired reference trajectory.

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An algebraic method based on unknown input observer for fault estimation in linear time invariant system with unknown input is implementable if matching condition is satisfied. Matching condition limits practical application of these methods. In this article, a method is proposed for fault estimation which need not to satisfy matching condition. Unlike classical methods, the provided method doesn’t require for auxiliary output for fault estimation. In first step, the unknown input is divided in two parts: the matched and the unmatched unknown inputs. Assuming that there exist a dynamic model for the unmatched part, new augmented system is constructed. The augmented system has revealed as a new system with matched unknown input. Then, the effect of matched unknown input has perfectly removed from observer estimation using the traditional unknown input decoupling strategy. In next step, the full order observer is designed for the augmented system. A fast adaptive law is employed for the fault estimation. Lyapunov stability condition of state and fault estimation is derived by linear matrix inequality(LMI) criteria. The effectiveness of the proposed method is shown via numerical simulation on a flexible joint example.

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In this paper, a novel dynamical model is proposed for the multi-input multi-output electrically driven robot manipulators, by an observer-based robust adaptive fuzzy controller. The proposed control scheme utilizes current control effort, which is more efficient than the torque control approach. The proposed method is very simple, accurate and robust. Based on the adaptive fuzzy system an observer-based estimator is presented that uses feedback error function as the input of fuzzy system to approximate and adaptively compensate the unknown uncertainties and external disturbance of the system under control. Although the proposed controller scheme requires the uncertainties to be bounded, it does not require this bound to be known. An H_∞ robust controller is employed to an attenuate the residual error to the desired level and recompenses the both fuzzy approximation errors and observer errors. The proposed method guarantees the stability of the closed-loop system based on the Strictly Positive Real (SPR) condition and Lyapunov theory. The proposed control scheme is not limited only for controlling of robotics vehicles, it can be applied for a class of nonlinear MIMO systems. Finally, in simulation study, to demonstrate the usefulness and effectiveness of the proposed technique, a two-link robot manipulator system is employed.

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In recent years, to reduce positioning cost for civil and robotic applications, low-cost inertial sensors especially Micro Electro Mechanical System (MEMS) types have been produced. Positioning Error of an inertial navigation system comprising low-cost inertial sensors increases due to significant uncertainty of noises, bias and drift of MEMS sensors in short times. Therefore, combination with an auxiliary system such as the Global Positioning System (GPS) is proposed in order to reduce the errors trough integration estimator algorithms. This paper aims developing a new estimation algorithm for integrated attitude and heading reference system (AHRS) with GPS. Kalman Filter is commonly used for linear systems and its extended version has been used for nonlinear system. Generally, the Kalman type estimators fall in trouble when the system exhibits nonlinear behavior and to overcome these issues, the predictive estimator is considered in the paper. Design process of Model Predictive Observer (MPO) is proposed based on the duality between the problems of control and estimation in linear systems. To assess the performance of the proposed method compared with the extended Kalnman filter, practical tests of AHRS/GPS have been done on car and flight vehicles. The test results of the designed MPO during all tests show the significant superiority in comparison to the extended Kalman filter.

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This paper presents the problem of controlling multiple tasks in a prioritized scheme during accidental external physical interaction with redundant robot. This issue arises when robots are employed in social, unknown, dynamic environments for complex and critical missions. Exploiting robot redundancy to ensure safety and compliance during performing hierarchical tasks is considered in this work. A general approach to control the main task (position/orientation of the end-effector) with allocated priorities beside compliance behavior in the null space of the tasks is proposed. External interactions on the robot body are estimated with an appropriate observer without using any force/torque sensors which is further used to bring compliance in the redundant space. A novel task allocation method is proposed and the convergence of the task space error, interaction estimation error as well as null space velocities are guaranteed. Finally, the performance of the method is investigated through computer simulation and real experiments on KUKA robot arm.