جستجوی مقالات مرتبط با کلیدواژه « robust control » در نشریات گروه « برق »

The issue of energy supply on the one hand and the environmental problems caused by the consumption of fossil fuels, on the other hand, have prompted researchers to turn to methods of reducing energy consumption as well as the use of renewable energy sources and hybrid vehicles. The time of several distributed generation units is optimal, but due to the uncertainties in phenomena such as solar radiation, to more accurately predict how the microgrid and its devices will work in the future to make appropriate decisions, a mechanism for processing and managing relevant data is. The answer to all these questions requires familiarity with a new concept called the "energy hub". Due to the wide scope of control of energy hub systems, in this article, while examining the definitions and concepts related to these systems, we have tried to design the control system so that the system is resistant to uncertainty and perturbation. The proposed method uses nonlinear predictive controllers. It is durable. Case study and simulation results in MATLAB software show well that the output variables of the modelled energy hub system have been able to be stable at a suitable time after input.

Due to its simplicity of implementation, the sliding mode control (SMC) method has been increasingly used in converters in the field of power electronics in recent years. The equivalent control approach can be leveraged to make the switching frequency stable in the sliding mode control of converters. In the SMC approach, most of the controller parameters are tuned by trial and error, and as a result, it is not possible to guarantee the stability of the controller in a wide range of operation. To cope with this challenge, a novel approach for designing the sliding mode controller and adjusting its parameters in a Z-source converter is proposed in this paper. To evaluate the proposed design, various important criteria including steady-state error and controller robustness against variations in the reference voltage, load, and input voltage have been investigated. The efficiency of the proposed method has been proved with the experiments through extensive MATLAB/Simulink simulations.

Aerodynamic load simulators generate the required time varying load to test the actuator’s performance in the laboratory. Electric Load Simulator (ELS) as one of variety of the dynamic load simulators should follows the rotation of the Under Test Actuator (UTA) and applies the desired torque to UTA’s rotor at the same time. In such a situation, a very large torque is imposed to the ELS from the UTA. Recently, the Permanent Magnet Synchronous Motor (PMSM) has been used for the ELS. In practice, the composition of PMSM and its drive is an unknown nonlinear plant. Model-based ELS design methodologies use a simplified model for PMSM and its drive. In this paper, a model-free method is used for ELS controller design. Two applicable and novel experiments are set up for identifying the ELS and disturbance models. Then, the identified models are used for a robust controller design based on quantitative feedback theory. The proposed method uses the ELS as a black-box and eliminates the large disturbance only based on the estimated model of the unknown nonlinear model. The control effort limitation is considered in the design stage and simultaneously a large bandwidth is achieved for the non-minimum phase system. The robust performance of the controller is evaluated on the ELS coupled to UTA.

In this paper, a new method for robust control is used. The whole robotic system, including the robot arm and motors in control, is considered. The main purpose of this article is to obtain the best results of the control law in order to achieve the minimum tracking error, which uses congestion optimization. Also, the designers of the control law are based on the nominal model . The real model uses intelligent systems. Control to resistance is evaluated by analysis and analysis.The stability of the system is demonstrated using Lyapunov's direct method, and the simulation results show the effectiveness of the proposed methods applied to a spherical robot driven by permanent magnet dc motors. Using the simulation results, the optimal values ​​of the parameters in the torque controllers have not converged to their true values ​​due to the large modelless dynamics, while they have converged to their true values ​​in the voltage control because it has only parametric uncertainty. . Also, the torque control law requires position vector, velocity vector and acceleration vector feedback.These feedback can not be easily obtained. In contrast, the law of voltage control requires feedback from the position vector, velocity vector, current vector, and time derivative. These feedback can be easily accessed.

Wireless power transmission technology with titles such as contactless power transmission, magnetic coupling power transfer, etc.are known and in fact, this method safely and reliably transmits power in such a way that does not require a mechanical connection between the source and the load. In this method, power transmission is done wirelessly using resonance induction coupling. By operating the transducer in the resonant mode, it will be possible to transfer a significant amount of power over an air distance of a few tens of centimeters, while the system efficiency is high and the voltage and current stress of the transducer are reasonable. In this paper, by presenting a method based on robust control and meta-heuristic algorithms, we improve the charging process of electric vehicles by considering uncertainty conditions. The simulation results show the better performance of the proposed controller compared to other controllers. Also, in this paper, the effect of connecting the charging station of electric vehicles to the distribution network is investigated by considering the optimal charging and discharging scheduling systems to maximize the economic profit of the vehicles and the charging station. In the proposed method, the best program for charging and discharging cars in order to maximize their profit is extracted based on genetic algorithm. According to the simulation results, optimal charging and discharging planning has reduced the value of losses to the total network energy to load the station in some trains, so that network targets such as losses and voltage deviation index are minimized and voltage stability index is maximized. In this study, minimization of losses, voltage deviation as well as maximization of voltage stability index have been investigated and the optimal location of the station has been obtained by considering these goals along with the profit of the station and vehicles. Finally, according to the results, with the planning of charging and discharging cars, in addition to providing the required charge, the profit of the station and cars has also increased.

In this paper, the formation tracking of multi-agent systems is discussed. The model considered for each agent is linear with uncertain parameters. The effect of external disturbances is also considered in the model. To achieve predetermined time-varying formation, the required control input is presented. By applying this input, the closed-loop system will take the desired formation. Establishing the appropriate conditions for the realization of time-varying formation, and using the Lyapunov theory and the H_inf index to reduce the disturbance effect, results in some linear matrix inequalities. The designed parameter is then computed by solving those linear matrix inequalities. Finally, a simulation example is presented to illustrate the effectiveness of the designed strategy.

Induction motor is one of the most widely used electric motors in the industry and even in everyday life. Various controllers are provided for this motor, one of which is the predictive control method. The main problem with this new method is the inaccuracy of the motor parameters and the lack of access to accurate system information. This inaccuracy can lead to errors in the controller operation. In this paper, the predictive control of induction motors is considered. To improve the performance of this controller, a super twisting sliding observer is used to reduce the effect of uncertainties in the structure of the induction motor. A model reference adaptive estimator has also been used to improve speed estimation in sensor-less control and online estimation of stator resistance. Finally, by adding a fuzzy controller, improvements in the performance of this estimator have been achieved. The simulation results indicate the optimal performance of this method in eliminating the uncertain effect and optimal control of the induction motor.

This paper presents a robust-H∞ control design for semi-active vehicle suspension system using linear matrix inequality (LMI). In vehicle control systems, the ability of damping vibrations is very important against the external and internal disturbances. Generally, it is impossible to eliminate all of this disturbances. So, it is essential to use the robust control. In this study, the state feedback H∞ control for a semi-active quarter car suspension using an Electro-Rheological damper with spring and damper has been utilized. The dynamic system of the suspension systems is first formed in terms of the control objectives, i.e., ride comfort, road holding, suspension deflection, and maximum actuator control force. Furthermore, using Lyapanov theory and linear matrix inequality (LMI) approach, the existence of admissible controllers is formulated in terms of LMIs. Finally, a quarter-car vehicle model is exploited to demonstrate the effectiveness of the proposed method. A comparison of the results between the semi-active and passive suspension systems shows that the attenuation of the disturbance against the bump and stochastic input is obtained.

In this study, a force reflection control structure is developed for the surgery training haptic system. In the surgery training haptic system, the surgical operation is cooperatively performed by a trainer and a trainee. The participation of each surgeon in the operation is established through their own haptic consoles. Although the operation is primarily performed by the trainee, the trainer is able to interfere into the procedure in case of observing any deviation on the part of trainee and correct the probable mistakes. To transform the task authority between the trainer and the trainee, the hand force of the trainer is reflected to the hands of the trainee. Utilizing the haptic system, the position of the trainee is transformed to the hands of the trainer, thus the trainer has necessary information regarding the current position of the surgical operation. Stabilizing controllers are developed for each haptic console. The stability of the closed loop system is analyzed using the Input-to-State Stability (ISS) approach. Our simulation results confirmed the appropriate performance of the proposed structure.

In this paper, a method is proposed for the control of a quadrotor based on sliding mode control by using Chebyshev neural networks. The proposed approach is a combination of the sliding mode controller and the Chebyshev neural network approximator that the neural network weights are tuned in real-time by using robust adaptive techniques. In this research, the dynamic model of the quadrotor is divided into two subsystems for the purpose of the position and orientation tracking control: a fully-actuated subsystem and an underactuated subsystem. For the former, the sliding surfaces are designed by using one state variable, and for the latter, the sliding manifolds are defined by a linear combination of two state variables. In this paper, the system stability is analyzed by Lyapunov theory-based techniques and the accuracy of the controller performance will be illustrated by the simulation results.