One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized Robust adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) controller. By applying a single line to ground fault in the network, the Robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized Robust adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

Non-cooperative intelligent control agents (ICAs) with dedicated cost functions, can lead the system to poor performance and in some cases, closed-loop instability. A Robust solution to this challenge is to place the ICAs at the feedback Nash equilibrium point (FNEP) of the differential game between them. This paper introduces the designation of a Robust decentralized infinite horizon LQR control system based on the FNEP for a linear time-invariant system. For this purpose, two control strategies are defined. The first one is a centralized infinite horizon LQR (CIHLQR) problem (i.e. a supervisory problem), and the second one is a decentralized control problem (i.e. an infinite horizon linear-quadratic differential game). Then, while examining the optimal solution of each of the above strategies on the performance of the other, the necessary and sufficient conditions for the equivalence of the two problems are presented. In the absence of the conditions, by using the least-squares error criterion, an approximated CIHLQR controller is presented. It is shown that the theorems could be extended from a two-agent control system to a multi-agent system. Finally, the results are evaluated using the simulation results of a Two-Area non-reheat power system.

In this study, novel Robust impedance control for lower-limb rehabilitation robotic system using voltage control strategy is used. Most existing control approaches are based on control torque strategy, which requires knowledge of robot dynamics as well as dynamics of patients. This obliges the controller to overcome complex problems such as uncertainty and nonlinearity involved in the dynamics of the system, robot and patients. Conversely, the voltage-based control approaches are free from the system dynamics. In addition, it considers the actuator dynamics. The performance of voltage-based approaches is demonstrated by experimental result in robotic applications. Compared with torque control scheme, it is simpler, less computational and more efficient. Nevertheless, uncertainty of actuator dynamics results in challenges for the voltage control strategy applications. The present paper presents novel Robust impedance control based on the voltage control strategy. To overcome uncertainties, the adaptive fuzzy estimator is designed based on the voltage-based strategy. The proposed control is verified by stability analysis. To illustrate the effectiveness of the control approach, 1-DOF lower-limb rehabilitation robot is designed. Both torque-based impedance control and the voltage-based impedance control are compared through therapeutic exercise. It is shown that the voltage-based impedance control performs better than the traditional torque-based impedance control. Simulation and experimental results both show that the proposed voltage-based Robust impedance control is superior to voltage-based impedance control in presence of uncertainties.

A sensorless vector control strategy for induction machine (IM) operating in variable speed systems is presented. The sensorless control is based on a reduced-order linear observer based on terminal voltage and current as input signals. An estimation algorithm based on this observer is proposed to compute speed. It is shown that the proposed sensorless control is more sensitive to the stator resistance than to the rotor resistance. In order to tune the observer and to compensate for the parameter variations and the uncertainties, a separate estimation of the stator resistance is introduced. The equations to estimate the stator resistance are derived from the machine differential equations. For certain operating regions of the machine, it is verified that the stator resistance can be accurately estimated regardless of wide stator resistance variation. It is shown that design and hardware implementation of this method is simpler than the previous works. The simulation and experimental results demonstrate the good performance of the proposed observer and estimation algorithm and of the overall indirect-field-oriented-controlled system.

In this paper, the problem of attitude control of a 1D non-linear flexible spacecraft is investigated. Three controllers are presented. The first is a non-linear dynamic inversion, the second is a linear m-synthesis and the third is a composition of dynamic inversion and a m-synthesis controller. It is assumed only one reaction wheel is used. Actuator saturation is considered in the design of controllers. The performances of the proposed controllers are compared in terms of nominal performance, Robustness to uncertainties, vibration suppression of panels, sensitivity to measurement noise, environment disturbance and non-linearity in large maneuvers. To evaluate the performance of the proposed controllers, an extensive number of simulations on a non-linear model of the spacecraft are performed. Simulation results show the ability of the proposed controller in tracking the attitude trajectory and damping panel vibration. It is also verified that the perturbations, environment disturbance and measurement errors have only slight effects on the tracking and damping responses.

control charts are the most useful tools for controlling the processes statistically. The construction of the control charts requires the estimation of the process parameters using random sample data. Usually the classical estimators of the process parameters are used to construct the control charts. The classical estimators of the parameters of the processes generating auto correlated data are sensitive to the presence of the outlier observations. Applying classical methods of estimation while outliers are present, introduce biased estimates of the model parameters which result in wrong interpretation of the control chart. In this research a method called Iteratively Robust Filtered Fast Tau (IRFFT) which is insensitive to the presence of the outliers is proposed for estimating the parameters of the auto correlated models. The newly introduced estimators are used to construct Robust control chart for auto correlated data. The suggested Robust control chart is compared with the control chart whose parameters are estimated using LS method. Results of the simulation study for the two methods indicate that the ARL for the suggested Robust control chart is much smaller under different scenarios. The findings may be extended to the other time series models.

Microgrids (MG) are a branch of distributed energy sources, which often use renewable energies to produce electrical power and gives service to scattered loads in Island and Grid connected operation modes. Because of the uncertainties of power system and natural variations of the power produced by renewable energies, in this paper, fractional order PID (FOPID) is used to control the frequency of the microgrid. The output of a fuzzy system is the input of the FOPID controller which results in Fuzzy fractional order PID (FFOPID). Imperialist competitive algorithm (ICA) determines the optimal values of the controller parameters. Comparison of the proposed FFOPID with FOPID and classical PID controllers in several load changes scenarios shows a better performance of the proposed controller in terms of RMS, overshoot and undershoot, number of oscillations, and settling time of the frequency deviations. Simulations indicate the Robust performance of the proposed controller against the changes of system parameters as well.