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, 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.

Stratospheric airships have introduced interesting solutions for many challenges in the aerospace industries. Buoyant and propulsion forces produced by airships make them capable of long-time flight and efficient operation. In spite of much progress, there are still many challenges in this exciting field of study. In this paper, first the dynamic model of fully-actuated stratospheric airship with 6-DOF is expressed by the generalized coordinates, then desired values of the airship attitude, linear and angular velocities are obtained according to desired path and using pseudo inversion of the kinematics and dynamics equations. In view of the unknown inertial parameters, first in Adaptive inverse dynamic Control, inertial parameters are estimated online using linearization parameters and gradient update law.Next, Control law and nonlinear dynamic equation arededuced by designing passivity based Control algorithm, and according to that, Adaptive and Robust Control based on passivity is applied to Control the airship. The stability of the closed loop Control system is briefly proved using the Lyapunov stability theory. Finally, the simulation results for tracking of a desired path are shown and comparison between the results of all methods is presented.

Drones are among the most valuable and versatile technologies in the world, with applications in a vast number of fields such as traffic Control, agriculture, firefighting and rescue, and filmmaking, to name a few. As the development of unmanned aerial vehicles (UAVs) accelerates, the safety of UAVs becomes increasingly important. In this paper, a Robust Adaptive Controller is designed to improve the safety of a hexa-rotor UAV, and a Robust Adaptive Controller is developed to Control our system. In doing so, the wind parameters from the aerodynamic forces and moments acting on the hexa-rotor are estimated using an observer with the Adaptive algorithm. This proposed Controller guarantees stability and reliable function in the midst of parametric and non-parametric uncertainties. The process’, s global stability and tracking convergence are investigated using the Lyapunov theorem. The performance and effectiveness of the proposed Controller are tested through two simulation studies, which take into account external disturbances that are a function of time.

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.

When the process is highly uncertain, even linear minimum phase systems must sacrifice desirable feedback Control benefits to avoid an excessive ‘cost of feedback’, while preserving the Robust stability. In this paper, the Control structure of supervisory based switching Quantitative Feedback Theory (QFT) Control is proposed to Control highly uncertain plants. According to this strategy, the uncertainty region is suitably divided into smaller regions. It is assumed that a QFT Controller-prefilter exits for Robust stability and Robust performance of the individual uncertain sets. The proposed Control architecture is made up by these local Controllers, which commute among themselves in accordance with the decision of a high level decision maker called the supervisor. The supervisor makes the decision by comparing the candidate local model behavior with the one of the plant and selects the Controller corresponding to the best fitted model. A hysteresis switching logic is used to slow down switching for stability reasons. Besides, each Controller is designed to be stable in the whole uncertainty domain, and as accurate in command tracking as desired in its uncertainty subset to preserve the Robust stability from any failure in the switching.

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.

Today, the magnetic levitation system is widely used in various industries. This system is inherently unstable and nonlinear, which is presented by nonlinear equations. On the other hand, the existence of a time delay in these systems also causes system instability or even chaos, which creates additional problems in their Control, thus requiring the design of Robust and optimal Control. In this paper, a Robust Adaptive Intelligent Controller based on the backsteppingsliding mode is proposed for the stability and proper tracking of the magnetic levitation system in the presence of time delay, uncertainty, and external disturbances. Due to changes in the equilibrium point, comparative Control is used to update the system’ s momentary information and Intelligent Controller to estimate uncertainties and disturbances and non-linearity of the system. A Robust Controller is used to asymptomatic stabilize the Maglev system. The Lyapunov stability theory is used to analyze the stability of the magnetic levitation system with the proposed Controller. In the end, in order to demonstrate the performance of the proposed Controller, numerical simulations have been used in MATLAB software. The simulation results show that good tracking has been performed and the Controller is very good against noise and disturbance.