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.

In this paper, an innovative ADAPTIVE output feedback CONTROL scheme is proposed for general multi-input multi-output (MIMO) plants with unknown parameters in a regulation task; such that the outputs of the plant converge to zero as well as the CONTROL gains remain uniformly bounded. First an ADAPTIVE observer is designed to estimate the state variables and system parameters by using the inputs and outputs of the plant. Then a linear combination of the estimated states by ADAPTIVE CONTROL gains is used to design a ROBUST ADAPTIVE CONTROLler. Some theorems are given to show the convergence of the modeling errors and the CONTROL gains. The proposed CONTROLler is used to CONTROL a two degree of freedom ROBOT MANIPULATOR such that the ROBOT moves from any initial configuration to zero position. Simulation results exhibit the effectiveness of the proposed scheme to CONTROL the ROBOT MANIPULATOR with different initial conditions and parameter perturbations.

Cell injection system in medicine is used to inject the materials into the cells. The injection system consists of injector and rotating plate. The CONTROLler sets height, position and orientation of the rotating plate. The proposal of this article is to replace SCARA ROBOT injection tool including ability in desired position tracking and is applied to time-varying force. In recent articles the CONTROL system is applied to the rotating plate of cells and this method can cause damage. The proposed method is fixed plate and to increase the success rate, the ROBOT is been CONTROLled. The parameters of environmental models are estimated by nonlinear proposed models and by using the recursive method, the minimum of square errors will be optimal. The voltage strategy can CONTROL ROBOT actuators. This method is simpler and free from the MANIPULATOR dynamics. In all recent studies, the impedance CONTROL is based on the torque CONTROL method and the proposed method of this article is to apply the impedance CONTROL using voltage CONTROL. The ROBUST ADAPTIVE impedance CONTROLler is designed in the presence of uncertainties. The simulation's results demonstrate desired performance of the proposed method.

A mobile MANIPULATOR ROBOT is a complex system due to properties such as coupling between the MANIPULATOR and mobile chassis, holonomic and nonholonomic constraints, MULTIVARIABLE and nonlinear dynamics. The CONTROL of ROBOT faces the external disturbance, parametric uncertainty and unmodeled dynamics. Therefore, the use of an ADAPTIVE fuzzy system is suggested for its capability in overcoming uncertainties and approximating of nonlinear functions based on the universal approximation theorem. However, the tracking error does not converge asymptotically to zero due to the approximation error of fuzzy system. This paper presents a novel ADAPTIVE fuzzy CONTROL for a mobile MANIPULATOR ROBOT. The novelty of paper is compensating the approximation error of fuzzy system for asymptotic convergence in tracking the desired trajectory in the presence of uncertainties. For this purpose, the closed loop system in the error space converges to a linear system with poles having negative real parts. The CONTROL design consists of two parts; the kinematic CONTROL and dynamic CONTROL. Advantages of the proposed design are the simplicity and very good performance in tracking of the desired trajectory in the presence of uncertainties. The stability of CONTROL system and convergence to the desired trajectory are proven by the Lyapunov method. The simulation results show the superiority of the proposed CONTROL over a ROBUST ADAPTIVE CONTROL.

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.

In this study, an ADAPTIVE proportional-derivative (PD) CONTROL scheme is proposed for trajectory tracking of multidegree-of-freedom ROBOT MANIPULATORs in the presence of model uncertainties and external disturbances whose upper bounds are unknown but bounded. The developed CONTROLler takes the advantages of linear CONTROL in the sense of simplicity and easy design, but simultaneously possesses high ROBUSTness against model uncertainties and disturbances while avoiding the necessity of precise knowledge of the system dynamics. Due to the linear feature of the proposed method, both the transient and steady-state responses are easily CONTROLled to meet desired specifications. Also, an ADAPTIVE law for CONTROL gains using only position and velocity measurements is introduced so that parameter uncertainties and disturbances are successfully compensated, where the prior knowledge about their upper bounds is not required. Stability analysis is conducted using the Lyapunov’ s direct method and brief guidelines on how to select CONTROL parameters are also provided. Simulation results corroborate that the ADAPTIVE PD CONTROL law proposed in this paper can achieve a fast convergence rate, small tracking errors, low CONTROL effort, and small computational cost and its performance is compared with that of an existing nonlinear sliding mode CONTROL method.

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 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.

This article investigates the problem of simultaneous attitude and vibration CONTROL of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. ADAPTIVE integral sliding mode CONTROL is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant algorithm considering rigid-flexible body dynamic interactions. The discontinuous structure of fault-tolerant CONTROL led to discontinuous commands in the CONTROL signal, resulting in chattering. This issue was resolved by introducing an ADAPTIVE rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its ROBUSTness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback CONTROL algorithm has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body dynamic interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear dynamics. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

In this paper, an optimal ADAPTIVE fuzzy integral sliding mode CONTROL is presented to CONTROL the ROBOT MANIPULATOR position tracking in the presence of uncertainties and permanent magnet DC motor. In the proposed CONTROL, sliding surface of the sliding mode CONTROL is defined according to the information of position tracking error, derivatives, and error integral. In order to estimate bounds of the existing structured and unstructured uncertainties in the dynamics of the ROBOT MANIPULATOR and the permanent magnet DC motor, a MIMO fuzzy ADAPTIVE approximator is designed. This helps to overcome the undesired chattering phenomenon in the CONTROL input by using fuzzy logic. Mathematical proof shows that the closed-loop system with the ADAPTIVE fuzzy integral sliding mode CONTROL in the presence of all the uncertainties has the global asymptotic stability. Furthermore, modified harmony search optimization algorithm is used to define the input coefficients of the proposed CONTROL and also to reduce the CONTROL input amplitude. In order to validate performance of the proposed CONTROLler, a case study on the SCARA ROBOT MANIPULATOR is conducted in the presence of permanent magnet DC motor. Results of the Simulation show desired performance of the proposed CONTROLler.