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An approach for assignment of the optimal location and tap changer adjustment related to Flux-Lock type Superconducting Fault Current Limiter with Tap Changer is used in this paper by debating the reduction of fault current flowing from each device and enhancement of reliability varying with customer type in a distribution network connected with Distribution Generation (DG). Three objective functions based on reliability index, reduction of fault current and number of installed MSFCL is systematized and a Nonlinear Time-Varying Evolution (NTVE) based Multi-Objective Particle Swarm Optimization (MOPSO) style is then formed in searching for best location and tuning of tap changer of MSFCL to meet the fitness requirements. A decision-making procedure based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used for finding best compromise solution from the set of Pareto-solutions obtained through MOPSO-NTVE. In a distribution network as Bus 4 of Roy Billinton Test System (RBTS), comparative analysis of the results obtained from application of the Resistive SFCL (RSFCL) and MSFCL is presented. The results show that optimal placement of MSFCL than RSFCL can improve reliability index and fault current reduction index with less number.

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In this paper, the guidance law designing problem in the presence of the control loop dynamics using sliding mode control has been studied. For this purpose in the design process, stable control loop dynamic considered that usually not considered by the designers. In practice there is a lag for control loop that may lead to instability in the guidance loop. In this paper the control loop dynamic that is stabilized with an autopilot, approximated as first order lag and then is considered with kinematic equation of motion in designing procedure. To solve the problem because of the nonlinearity in equations and target maneuvers as uncertainty, the sliding mode control scheme is used. So just having the bounds of the uncertainty we can design guidance law and the measure or estimate of uncertainty is not required. The sliding variable is defined with respect to parallel navigation idea using relative lateral velocity between the interceptor and the target. Then a controller is designed for reaching the sliding variable to sliding surface. Therefore the line of sight rate will be zero and collision is inevitable. Also for removing chattering, the continues approximation method is used.

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In this paper, a new combination of fractional order calculus and finite time sliding mode control, used to design an aircraft autopilot. This combination aims to reduce the chattering phenomena and have a smoother control signal than conventional sliding mode. Fractional order control uses fractional integrator and derivative to improved integer order control methods. The sliding surface and sliding mode control law is proposed to reduce the chattering phenomena and also, closed-loop stability is guaranteed too. Using this algorithm, a robust autopilot against aerodynamic coefficients uncertainty is designed for an aircraft and proposed control law is utilized to stabilize the close loop system by Lyapunov stability theorem. The proposed autopilot is applied to the aircraft model and simulation results illustrate the reduction of chattering phenomena.

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In this paper, a finite-time stabilized guidance law is addressed in presence of some measurement noises. The measurement noise would effect on the guidance system stability and or performances. Hence, in presence of measurement noise, the guidance law must be modified such a way that the noise effect on the guidance system response would be reduced. By using the stochastic stability theory, a modified guidance law, depended on the measurement noises variance, will be proposed such that the line of sight angle rate is stabilized in a finite time. After such a finite-time, no force would be applied to the vehicle actuators. Then the line of sight angle would be a constant one. The proposed method would be used in a two-dimensional numerical example. The effectiveness of the suggested method is shown in the simulation results.

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In this paper, a robust model predictive control (MPC) algorithm is designed for nonlinear uncertain systems in presence of the control input constraint. To achieve this goal, first, the additive and polytopic uncertainties are formulated in the nonlinear uncertain system. Then, the control policy is chosen as a state feedback control law in order to minimize a given cost function at each known sample-time. Finally, the robust MPC problem is transformed into another optimization problem subject to some linear matrix inequality (LMI) constraints. The controller gains are determined via the online solution of the proposed minimization problem in real-time. The suggested method is simulated for a second order nonlinear uncertain system. The closed-loop performance is compared to other control techniques. The simulation results show the effectiveness of the proposed algorithm compared to some existing control methods.
 

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Electromagnetic suspension technology has been developed in recent years due to advantages such as no contact and reduced friction. Of course, ensuring efficiency in these systems requires precise control of the position of the suspended object. Therefore, electromagnetic suspension is considered as a process by control engineers. The dynamics of electromagnetic suspension systems is nonlinear and also include model and parametric uncertainties such as the weight of the suspended object. In this paper, a finite time nonlinear hybrid method is used to stabilize the electromagnetic suspension system. Proof of finite time stability of the proposed method is performed using Lyapunov theory and a relation for calculating the convergence time depends on the controller gains is presented. To ensure the finite time convergence of the system state and output variables, the backstepping algorithm is used and in each step, the finite-time convergence theory is used. The controller designed in this paper is compared with the backsteping method and the superiority of the proposed method in various simulations is shown.

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Usually, constrained lateral acceleration would have undesirable effects on the stability and performance of a guidance system. The composite nonlinear feedback (CNF) can be effectively used to improve the transient response of the closed-loop system in the presence of the constrained input. In this way, guidance law consists of an extra nonlinear term besides the conventional linear one. As a result, such a term adjusts the qualitative characteristics of the transient response. Meanwhile, the nonlinear term is a function of the rate of line-of-sight (LOS) angle which is not activated at origin and infinity. Thus it would be effective only in a specified region. In this paper, proportional navigation is employed for the linear term of the CNF-based guidance law. Therefore, a guidance algorithm is developed for tracking problems using the CNF idea. Applying the proposed guidance method, the closed-loop stability is analytically proved via the well-known Lyapunov stability theory. The suggested approach is simulated in a numerical example. Then the results are compared with an existing technique. As expected, guaranteeing closed-loop stability, in contrast to a similar method, the addressing scheme considerably improves the performance and transient response of the guidance system in the presence of lateral acceleration limitations.
 

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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.