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Generally nonlinear modelling of aerospace system has uncertainty in model parameters and also in real situation different disturbances are applied to system. In spite of these uncertainties and disturbances, autopilot control system should be guarantee stability and desired performance of system. The conditions such as fast response, low tracking error, system robustness must be considered in autopilot design. In this paper, a new method is suggested to reduce the tracking error and increase system robustness. The proposed method is based on Backstepping approach. To reduce the tracking error, resulted from the simplification of the missile model, a nonlinear disturbance observer is used to estimate the uncertainty and also update the reference signal. In addition nonlinear disturbance observer is used to eliminate output disturbance. The advantage of the proposed method is its complete flexibility and also it can be employ for linear and nonlinear systems

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The very low cost of renewable energy resources and the increase of the greenhouse gas emissions and fuel cost have led to a simultaneous increase in utilizing the renewable energy resources (RESs) and electric vehicles (EVs). In this paper, a mixed-integer linear programming (MILP) model is proposed for the stochastic unit commitment problem with the aim of minimizing the operation cost and the emission in the presence of EVs and RESs. EVs with the capability of vehicle-to-grid (V2G) can operate as energy storage units in the smart grid and, if necessary, be connected to the network as generation resources. In this paper, an aggregator is responsible for coordinating the charging and discharging of EVs. The RESs uncertainties has complicated the management of electric vehicles and the unit commitment problem. Therefore, in this paper, Monte Carlo simulation method is used for modeling the uncertainties of the wind and solar power and the load demand. The simulation results show that the simultaneous utilization of the proposed MILP model and the probability distance method for reducing the number of scenarios, can minimize the operation cost of thermal units and pollutant emissions while reduces the solution time, significantly

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The paper presents an optimal and coordinated power oscillation damper based on a wind turbine and power system stabilizer (PSS) to maintain the power system stability and damp inter-area oscillations. The optimal and coordinated design of the PSS located at the generator site and the damper which was installed in the control section of the doubly-fed induction generator (DFIG) is defined as an optimization problem and simulations have been performed in MATLAB software environment. To determine optimal coefficients of the PSS and damper, the metaheuristic salp swarm optimization (SSA) algorithm was employed with an objective function that aimed to minimize the error caused by frequency deviations of two areas. Due to the use of wide-area measurement systems (WAMS) in the proposed damper to enhance controllability and observability of most of the oscillation modes, time delays resultant from the WAMS was also taken into account. Additionally, uncertainties of wind intermittency and time delay of WAMS were calculated probabilistically. The suggested method was applied to a six-machine two-area power system with a wind farm. The obtained simulation results highlighted and validated the superior performance and stability of the power system as a result of using the proposed method.

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In this paper, the problem of stabilization and synchronization of Lorenz and Chua chaotic in the presence of uncertainty using fractional order sliding mode control strategy based on nonlinear adaptation law has been investigated. Lorenz and Chua systems denote third order dynamics models which are chaotic for certain parameters. The proposed control law is composed of two prats sliding mode control and adaptive control law. Firstly, by supposing that instantaneous information of nonlinear part of chaotic system is not available, a linear regressor equation including an unknown section has been used. Using Lyapunov stability theorem and based on fractional calculus, adaptation law is developed to instantaneous estimation of unknown part. Moreover, by defining based on error signals and realizing exponential reaching law for insuring closed-loop stability, the sliding mode control law including equivalent and switching control has been derived. Eventually, the final control law has been derived by synthesizing sliding mode control and adaptive laws. The important aspect of the proposed approach is ability to encounter unstructured uncertainties and nonlinear effects of chaotic systems dynamic and guiding the state variables into sliding surface for arbitrary initial conditions. The performance of the proposed algorithm has been evaluated by realizing the stabilization problem of chaotic Lorenz system and synchronization of chaotic Lorenz and Chua systems.
 

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In an islanded microgrid, power electronic converters are used to exchange power, and these converters have very low inertia, thus compromising the frequency stability of the microgrid. Virtual inertia control is used to improve the frequency stability of an islanded microgrid. The derivative control technique is usually used to implement virtual inertia control in the microgrid. Factors such as disturbance and uncertainty of parameters of the islanded microgrid compromise the performance of virtual inertia control and may cause system frequency instability. Therefore, the virtual inertia control structure, a complementary controller is needed that can weaken the effect of disturbance on the microgrid as much as possible and be resistant to the uncertainty of parameters of the microgrid. In this paper, a robust control method is used in a virtual inertia control structure that uses system output feedback. The proposed method is expressed based on linear matrix inequality and is proved based on the Lyapunov criterion. Among the advantages of the proposed method is the attenuation of disturbance, resistance to the uncertainty of parameters of the microgrid, and increasing the degree of freedom to control the system in this method. The results of the proposed method to improve the performance of virtual inertia control in several different scenarios by considering the uncertainty of parameters of the two-zone microgrid and disturbances on the microgrid are compared with several methods and the effectiveness of the proposed method in terms of improving frequency stability is shown.

 

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This paper presents a new method of the adaptive non-singular Second-order Terminal Sliding Mode (SOTSM) control for the fast and finite time stabilization of Cyber-Physical Systems (CPSs) in the simultaneous presence of parametric uncertainties, unwanted disturbances and actuator cyber-attacks. By utilizing the presented non-linear manifold and sliding surface, the reaching mode is deleted and the entire system’s robust performance is improved. The proposed online adaptive laws deal with parametric uncertainties, unwanted disturbances and cyber-attacks, so that there is no need to identify their upper bounds. The designed adaptive non-singular SOTSM control method guarantees the robust performance of the system in the mentioned conditions along with fast and smooth response, high accuracy and flexibility, without transient fluctuations and chattering, as well as proper convergence in finite time. The numerical simulation results show the effectiveness and success of the adaptive non-singular second-order terminal sliding mode control method in comparison with the results of adaptive integral sliding mode control, traditional sliding mode control and state feedback control.
 

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Thrust vector control is a special method to change the attitude and position of flying objects, which can only be applied in some missions. These systems require feedback control and lead to better maneuverability. In this paper, a finite time adaptive sliding model controller is presented for controlling the thrust vector of a flying object. The first-order sliding model method requires information about the upper bound of system uncertainties and also this method causes chattering in the control signal. The standard adaptive sliding model method has solved the problem of the need for the uncertainty bound and also reduces the chattering range. But this method does not guarantee finite time stability. In this article, the finite time type of adaptive sliding model is used to control the thrust vector. This method guarantees finite time stability without the need for upper bound information of system uncertainties, and in it, the convergence time of the tracking error and estimation depending on the initial conditions can be calculated. The performance of the proposed thrust vector control system has been investigated by computer simulation and its efficiency is shown in comparison with other methods.