Due to uncertainties in system modeling as well as system parameters, current excitation systems are unable to perform quite satisfactorily over a wide range of operating conditions. In this paper a QFT-based excitation robust control is proposed which the above mentioned uncertainties are, somehow, considered. The Horowitz second method is employed in the design of the nonlinear QFT controller.

In this paper a new and completely linear algorithm is proposed for robust control of flexible joint robots. Moreover, the robust stability of the closed loop system in the presence of structured and unstructured uncertainties is analyzed. In order to develop the controller, flexible joint robot with structured and unstructured uncertainties is modeled and converted into a singular perturbation form. A robust linear control algorithm is proposed for the slow dynamics and its robust stability conditions are derived using Thikhonov's theorem. Then, the robust stability of the total system considering the proposed composite controller is analyzed, and the sufficient conditions for robust stability of the overall system are determined. Finally the effectiveness of the proposed controller is verified through simulations. It is shown that not only the tracking performance of the proposed controller is very suitable, but also the actuator effort is much smaller than that in the previous result.

This paper offers a design procedure for robust stability, robust H-infinity control and robust H2 control via dynamic output feedback for a class of uncertain linear systems. The uncertainties are of norm bounded type. Then in order to support a high-speed energy storage flywheel, these procedures are applied to an active radial magnetic bearing system. The state space matrices of this controller are the solution of some linear matrix inequalities (LMIs).

This paper presents a new stability preserving interpolation technique for robust gain scheduling autopilot design. The interpolation method is based on interpolation in the common stability region of local controllers and generates a gain-scheduled controller that is stabilizing at every operating point of a closed loop system. For selection of stability region of local controllers, the notion of the v-gap metric and its connection to robust loop-shaping theory is used. The proposed method facilitates the design of gain-scheduled controllers that preserves stability of the closed loop system. The simulation results given show the generality and effectivneness of the proposed control strategy in terms of the stability, performance and robustness, of the system.

In this paper, the robust stability analysis and stability bound improvement of perturbed parameter (e) in singularly perturbed systems are considered, using linear fractional transformations and structured singular values (m) approach. In this direction, by introducing the parametric and dynamic uncertainty in the singularly perturbed systems, the mentioned system is rewritten as a standard p,-interconnection framework by using linear fractional transformations. Also, a set of new stability conditions for the system is derived in the frequency domain. The exact solution of g-bound is characterized. It is shown that the g-bound obtained through this approach is larger compared to that of [1], in which only parametric uncertainty is considered. Simulation results show the efficiency of the approach.

In this paper, a new comparative approach was proposed for reliable controller design. Scientists and engineers are often confronted with the analysis, design, and synthesis of real-life problems. The first step in such studies is the development of a 'mathematical model' which can be considered as a substitute for the real problem. The mathematical model is used here as a plant. Fractional integrals and derivatives have found wide application in the control of dynamical systems when the controlled system and the controller are described by a set of fractional order differential equations. Here the stability and robustness of fractional order system is checked at the different level and it is found that the stability region is large in the complex plane. This large stability region provides the more flexibility for system implementation in the control engineering. Generally, an analytically or experimentally approaches are used for designing the controller. If a fractional order controller design approach used for a given plant then the controlled parameter gives the better result.

In this paper, we investigate the delay-dependent robust stability of fuzzy Cohen-Grossberg neural networks with Markovian jumping parameter and mixed time varying delays by delay decomposition method. A new Lyapunov-Krasovskii functional (LKF) is constructed by nonuniformly dividing discrete delay interval into multiple subinterval, and choosing proper functionals with different weighting matrices corresponding to different subintervals in the LKFs. A new delay-dependent stability condition is derived with Markovian jumping parameters by T-S fuzzy model. Based on the linear matrix inequality (LMI) technique, maximum admissible upper bound (MAUB) for the discrete and distributed delays are calculated by the LMI Toolbox in MATLAB. Numerical examples are given to illustrate the effectiveness of the proposed method.

Many advanced robot applications such as assembly and manufacturing require mechanical interaction of the robot manipulator with the environment. Any back-stepping based control strategy proposed for position control of electrical flexible joint robots requires a convergence of internal signals to its desired value called a fictitious control signal. This problem is complicated and time-consuming, whereas a 5th-order nonlinear differential equation describes each joint of the robot. The best idea is to focus on the convergence of main signals while the other signals in the system remain bounded. With this in mind, this paper present a robust Lyapunov-based controller for the flexible joint electrically driven robot (FJER) considering input nonlinearities associated with actuator constraints. It also finds uncertainties associated with robot dynamics. The proposed approach is based on a third-order model instead of a fifth-order model of the robotic system. The stability is guaranteed in the presence of both structured and unstructured uncertainties. The actuator/link position errors asymptotically converge to zero while the other signals are bounded. Simulation results on a 2-DOF electrical robot manipulator effectively verify the efficiency of the proposed strategy.