Search

This paper presents a study of a robust control strategy based on the Attractive Ellipsoid Method (AEM) to achieve trajectory tracking for a Permanent Magnet Synchronous Generator (PMSG). For the PMSG system, trajectory tracking control is necessary such that the maximum power point tracking can be carried out, which depends on controlling time-varying references (the generator angular speed), mainly when the generator motion is originated from a variable and intermittent energy source such as the wind. The proposed controller is based on the selection of the best gain matrix by numerically employing the AEM with the bilinear matrix inequality technique application. The controller guarantees that the trajectory tracking error reaches an ellipsoid of small enough size, which is determined in an optimized way, such that the practical stability for the tracking error is ensured. The effectiveness of the proposed controller is verified via simulations for a PMSG coupled with a three-phase rectifier, where the variables to be controlled reach the desired time-varying references, additionally a comparison is made against a classical Proportional Integral (PI) control scheme and a state feedback LQR controller. [ABSTRACT FROM AUTHOR]

A class of controlled objects is considered, the dynamics of which are determined by a vector system of ordinary differential equations with a partially known right-hand side. It is presumed that the state variables and their velocities can be measured. Designing a robust tracking controller under some constraints to admissible state variables is the research goal. This construction, which extends the results for the average subgradient technique (ASG), and is an update of the subgradient descent technique (SDM) and integral sliding mode (ISM) approach, is realized by using the Legendre–Fenchel transform. A two-link robot manipulator with three revolute joints, powered by individual PMDC motors, is presented as an illustrative example of the suggested approach implementation. [ABSTRACT FROM AUTHOR]

This paper proposes the study of a robust control scheme for an Unmanned Surface Vehicle (USV) in a urban waterway using Visual Odometry (VO) for position estimation of the vehicle instead of traditional sensors. For different applications, a USV is required to operate in autonomous navigation mode and under various disturbances inherent to its environment as currents and waves. To achieve efficient operation, a robust control scheme is required to cope with such disturbances. The autonomous navigation is achieved by using VO to estimate the vehicle's position for denied Global Positioning Systems (GPS) environments. A robust control scheme based on Sliding Modes (SM) control theory is designed and its convergence is guaranteed by means of Lyapunov analysis. The tracking capabilities under disturbance conditions using VO for position estimation are verified in simulation using the virtual world tool from Matlab and Simulink. [ABSTRACT FROM AUTHOR]

This paper deals with the finite-time stabilization problem for a class of uncertain disturbed systems using linear robust control. The proposed algorithm is designed to provide the robustness of a linear feedback control scheme such that system trajectories arrive at a small-size attractive set around an unstable equilibrium in a finite time. To this end, an optimization problem with a linear matrix inequality constraint is presented. This means that the effects of external disturbances, as well as matched and mismatched uncertain dynamics, can be significantly reduced. Finally, the performance of the suggested closed-loop control strategies is shown by the trajectory tracking of an unmanned aerial vehicle flight. [ABSTRACT FROM AUTHOR]

We study the behaviour of stochastic discrete-time models controlled by an output linear feedback during a tracking process. The controlled system is assumed to be nonlinear satisfying the global ‘quasi-Lipschitz’ condition and subjected to stochastic input and output disturbances. Two gain matrices (in a feedback and in an observer) define an ‘averaged’ ellipsoid in the tracking-error space where all system's trajectories arrive ‘with probability one’. The selection of the ‘best’ gain matrices is realised numerically by application of the robust attractive ellipsoid method with the linear matrix inequality technique application. The suggested approach is illustrated by designing of a robust tracking controller for a benchmark example in the presence of stochastic noises both in the state dynamics and in the output observations. [ABSTRACT FROM AUTHOR]

• A Markov jump modeling for power system under a series of lightning stokes+stochastic external independent disturbances which are admitted to have any distribution with bounded second moment. • Designing a state estimator to generate the missing states when both the dynamic of states model and measurable outputs are disturbed by stochastic noise. • Developing a new output feedback controller based on the AEM to obtain the controller-observer gain matrices: before the AEM has never been applied to stochastic models with jumping parameters. Power systems are subject to stochastic fluctuations which can be classified as topological or parametric. This paper models the random topological changes due to a series of lightning strokes hitting a transmission line (associated with open/reclose of circuit breakers) in a power system as a Markov jump system. Whereas the line reactance (depending on the phase conductors spacing) changes due to the random wind speed fluctuations, are represented as a stochastic external disturbances. A novel excitation control for a stochastic power system is developed. The optimal two-controller and observer gains are designed based on the attracting ellipsoid method and the linear matrix inequalities (LMI) optimization. The effectiveness of the proposed control is tested on a multimachine system. [ABSTRACT FROM AUTHOR]