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.

This article presents a new Robust Adaptive sliding mode controller for a class of uncertain nonlinear systems whereas only the system output is measurable. Firstly, a Robust Adaptive Fuzzy observer is designed for the system in order to estimate its state variables. The Robust asymptotic convergence of the proposed observer is proven by Lyapunov direct method. Then based on the observation states, a Robust Adaptive sliding mode controller is suggested such that the closed loop system to be asymptotically stable. Robust asymptotic stability of the overall system suggested by the controller is also confirmed based on Lyapunov theory. Simulation results illustrate practicality and effectiveness of the proposed technique for controlling uncertain nonlinear systems.

The imprecision related to measurements can be managed in terms of Fuzzy features, which are characterized by two components: center and spread. Outliers affect the outcome of the clustering models. In trying to overcome this problem, this paper proposes a Fuzzy clustering model for L-R Fuzzy data, which is based on a dissimilarity measure between each pair of Fuzzy data defined as an Adaptive weighted sum of the L1-norms of the centers and the spreads. The proposed method is Robust based on the metric and weighting approaches. It estimates the weight of a given Fuzzy feature on a given Fuzzy cluster by considering the relevance of that feature to the cluster; if outlier Fuzzy features are present in the dataset, it tends to assign them weights close to 0.To deeply investigate the capability of our model, i.e., alleviating undesirable effects of outlier Fuzzy data, we provide a wide simulation study. We consider the ability to classify correctly and the ability to recover the true prototypes, both in the presence of outliers. The comparison made with other existing Robust methods indicates that the proposed methodology is more Robust to the presence of outliers than other methods. Moreover, the performance of our method decreases more slowly than others when the percentage of outliers increases. An application of the suggested method to a real-world categorical dataset is also provided.

In this paper, Blood Glucose control in type 1 diabetic patients in the presence of structured and unstructured uncertainties is studied. In order to increase the effectiveness of the proposed control approach, assumed that all the dynamics describing the regulation of Blood Glucose in type 1 diabetic patients are completely unknown. Based on the Fuzzy approximation function, which is equipped with the Adaptive algorithm and employing the approach of reducing the number of Adaptive Fuzzy parameters, the unknown dynamics of the Bergman model approximated. Then, based on the feedback linearization control approach and Robust Adaptive compensator, the design of feedback linearization Robust Fuzzy controller to regulate Blood Glucose in type 1 diabetic patients in the presence of meal is studied for the first time. Using Lyapunov theory, it is shown that all signals of the closed-loop system are uniformly ultimately bounded and the Blood Glucose of diabetic patients converges to the neighborhood of the desired value. Finally, the simulation results show a good controller performance in reducing effect of the meal disturbance, and Robustness against uncertain dynamics and meal estimation errors. Moreover, in comparison with some existing results, a good performance of the introduced controller in controlling Blood Glucose of diabetic patients (i. e., keeping Blood Glucose in allowed range 70-120 mg/dl) validated.

In this paper, a new dynamical model is suggested for the Brush-Less DC (BLDC) thruster motors, by an observer-based Robust Adaptive Fuzzy controller. The proposed control method utilizes an accurate thrust model which is more efficient than the other approach. The suggested scheme is very simple, accurate and Robust, so that, a control method of thrust, torque, and speed of BLDC motors is available. Based on the Adaptive Fuzzy algorithm an observer-based estimator is presented that applies feedback error function as the input of the Fuzzy system to estimate and Adaptively compensate the external disturbance and unknown uncertainties of the system under control. Although the proposed controller scheme requires the uncertainties to be bounded, it does not require these bounds to be known. An H∞ Robust controller is employed to attenuate the residual error to the desired level and recompense both the 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. Finally, in simulation studies, to demonstrate the usefulness and effectiveness of the proposed technique, a BLDC motor system is employed.

In this study, an observer based indirect Adaptive Fuzzy controller for the anti-lock braking system (ABS) is proposed. First, using the Lugre internal friction model in the single corner model of automobile, road profile is estimated. Then, using the estimator results, the optimum slip is estimated. It should be noted that in the optimum slip, the maximum longitudinal friction created between the tire and road is achieved, so that the automobile stops in the least possible distance.Since some of state variables of the system are difficult to measure or contaminated by noise, and observer based controller is designed in this paper. The controller objective is to realize the reference slip obtained from the road condition estimator. Using Lyapunov stability theory, it is verified that the values obtained for road condition and vehicle speed will converge to their actual values. In addition, it is shown that the indirect Adaptive Fuzzy controller results in asymptotic tracking of the optimum slip. Simulations results show the proper performance of the proposed antilock breaking control system in achieving the optimum slip and quick stop of the car without locking the wheel in various road conditions.

In this study, a Robust H_2/H_∞ multi-objective state-feedback controller and tracking design are presented for a mobile two-wheeled inverted pendulum (MTWIP). The proposed control has to track the desired angular velocity while keeping the mobile two-wheeled inverted pendulum balanced. First, error of output states are added to the dynamic of system for better tracking control. And uncertainties of parameters are defined by affine parameters. Next, Takagi-Sugeno (T-S) Fuzzy model is used for estimating the uncertainty of nonlinear model parameters. Robust H_2/H_∞ controller is designed and analyzed for each local linear subsystem of mobile two-wheeled inverted pendulum by using a linear matrix inequalities method. To sum up, in order to calculate the whole dynamic of system from each local linear subsystem, weight average defuzzifer method is used and the total controller is designed and analyzed according to parallel distribute compensation. The simulation indicate that the proposed scheme has high accuracy, Robustness, good tracking, fast transient responses and lower control effort for a mobile two-wheeled inverted pendulum despite the uncertainties and external disturbance.

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 attenuate the residual error to the desired level and recompenses both the Fuzzy approximation errors and the 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 to only controlling 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.

In most of the researches that have been done in the position control of robot manipulator, the assumption is that robot manipulator kinematic or robot Jacobian matrix turns out from the joint-space to the task-space. Despite the fact that none of the existing physical parameters in the equations of the robot manipulator cannot be calculated with high precision. In addition, when the robot manipulator picks up an object, uncertainties occur in length, direction and contact point of the end-effector with it. So, it follows that the robot manipulator kinematic is also has the uncertainty and for the various operations that the robot manipulator is responsible, its kinematics be changed too, certainly. To overcome these uncertainties, in this paper, a simple Adaptive Fuzzy sliding mode control has been presented for tracking the position of the robot manipulator end-effector, in the presence of uncertainties in dynamics, kinematics and Jacobian matrix of robot manipulator. In the proposed control, bound of existing uncertainties is set online using an Adaptive Fuzzy approximator and in the end, controller performance happens in a way that the tracking error of the robot manipulator will converge to zero. In the proposed approximator design, unlike conventional methods, single input-single output Fuzzy rules have been used. Thus, in the practical implementation of the proposed control, the need for additional sensors is eliminated and calculations volume of control input decreases too. Mathematical proofs show that the proposed control, is global asymptotic stability. To evaluate the performance of the proposed control, in a few steps, simulations are implemented on a two-link elbow robot manipulator. The simulation results show the favorable performance of the proposed control.