Event-based global stabilization of linear systems via a saturated linear controller

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Design, test, and evaluation of three active flutter suppression controllers

Three control law design techniques for flutter suppression are presented. Each technique uses multiple control surfaces and/or sensors. The first method uses traditional tools (such as pole/zero loci and Nyquist diagrams) for producing a controller that has minimal complexity and which is sufficiently robust to handle plant uncertainty. The second procedure uses linear combinations of several accelerometer signals and dynamic compensation to synthesize the model rate of the critical mode for feedback to the distributed control surfaces. The third technique starts with a minimum-energy linear quadratic Gaussian controller, iteratively modifies intensity matrices corresponding to input and output noise, and applies controller order reduction to achieve a low-order, robust controller. The resulting designs were implemented digitally and tested subsonically on the active flexible wing wind-tunnel model in the Langley Transonic Dynamics Tunnel. Only the traditional pole/zero loci design was sufficiently robust to errors in the nominal plant to successfully suppress flutter during the test. The traditional pole/zero loci design provided simultaneous suppression of symmetric and antisymmetric flutter with a 24-percent increase in attainable dynamic pressure. Posttest analyses are shown which illustrate the problems encountered with the other laws

Hidden attractors in fundamental problems and engineering models

Recently a concept of self-excited and hidden attractors was suggested: an attractor is called a self-excited attractor if its basin of attraction overlaps with neighborhood of an equilibrium, otherwise it is called a hidden attractor. For example, hidden attractors are attractors in systems with no equilibria or with only one stable equilibrium (a special case of multistability and coexistence of attractors). While coexisting self-excited attractors can be found using the standard computational procedure, there is no standard way of predicting the existence or coexistence of hidden attractors in a system. In this plenary survey lecture the concept of self-excited and hidden attractors is discussed, and various corresponding examples of self-excited and hidden attractors are considered

Robust sampled-data control: An input delay approach

International audienceA method for robust sampled-data stabilization of linear continuous-time systems is introduced. This method is based on the continuous-time model with time-varying input delay. Delay-dependent sufficient LMIs conditions for stabilization of systems with polytopic type uncertainty and for regional stabilization of systems with sampled-data saturated state-feedback are derived. The method may be applied to a wide spectrum of robust sampled-data control problems

Proton beam steering control system for high precision radiotherapy at iThemba LABS : an investigation on actuator saturation constraints

Includes abstract.Includes bibliographical references (leaves 101-106).This thesis aims at studying some of the techniques used to deal with constraints with special application to the Proton beam steering control at iThemba LABS. The steering of charged particles occurring in research plants is one of the interests of control systems. In this work an investigation of the algorithm for the control of the proton beam steering system in the radiotherapy treatment facility at iThemba LABS is conducted. This algorithm is intended to autonomously maintain the beam centered with reference to the axis of the beamline, and keep the beam front parallel to the central axis of the beamline as stated by van Tubbergh and De Kock, 2006. Furthermore, the algorithm is responsible for monitoring the distribution of the proton beam, in a plane normal to the beam travel direction

Recent advances on filtering and control for nonlinear stochastic complex systems with incomplete information: A survey

This Article is provided by the Brunel Open Access Publishing Fund - Copyright @ 2012 Hindawi PublishingSome recent advances on the filtering and control problems for nonlinear stochastic complex systems with incomplete information are surveyed. The incomplete information under consideration mainly includes missing measurements, randomly varying sensor delays, signal quantization, sensor saturations, and signal sampling. With such incomplete information, the developments on various filtering and control issues are reviewed in great detail. In particular, the addressed nonlinear stochastic complex systems are so comprehensive that they include conventional nonlinear stochastic systems, different kinds of complex networks, and a large class of sensor networks. The corresponding filtering and control technologies for such nonlinear stochastic complex systems are then discussed. Subsequently, some latest results on the filtering and control problems for the complex systems with incomplete information are given. Finally, conclusions are drawn and several possible future research directions are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61104125, 61028008, 61174136, 60974030, and 61074129, the Qing Lan Project of Jiangsu Province of China, the Project sponsored by SRF for ROCS of SEM of China, the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Computationalcost Reduction of Robust Controllers Foractive Magnetic Bearing Systems

This work developed strategies for reducing the computational complexity of implementing robust controllers for active magnetic bearing (AMB) systems and investigated the use of a novel add-on controller for gyroscopic effect compensation to improve achievable performance with robust controllers. AMB systems are multi-input multi-output (MIMO) systems with many interacting mechanisms that needs to fulfill conflicting performance criteria. That is why robust control techniques are a perfect application for AMB systems as they provide systematic methods to address both robustness and performance objectives. However, robust control techniques generally result in high order controllers that require high-end control hardware for implementation. Such controllers are not desirable by industrial AMB vendors since their hardware is based on embedded systems with limited bandwidths. That is why the computational cost is a major obstacle towards industry adaptation of robust controllers. Two novel strategies are developed to reduce the computational complexity of singlerate robust controllers while preserving robust performance. The first strategy identifies a dual-rate configuration of the controller for implementation. The selection of the dualrate configuration uses the worst-case plant analysis and a novel approach that identifies the largest tolerable perturbations to the controller. The second strategy aims to redesign iv the controller by identifying and removing negligible channels in the context of robust performance via the largest tolerable perturbations to the controller. The developed methods are demonstrated both in simulation and experiment using three different AMB systems, where significant computational savings are achieved without degrading the performance. To improve the achievable performance with robust controllers, a novel add-on controller is developed to compensate the gyroscopic effects in flexible rotor-AMB systems via modal feedback control. The compensation allows for relaxing the robustness requirements in the control problem formulation, potentially enabling better performance. The effectiveness of the developed add-on controller is demonstrated experimentally on two AMB systems with different rotor configurations. The effects of the presence of the add-on controller on the performance controller design is investigated for one of the AMB systems. Slight performance improvements are observed at the cost of increased power consumption and increased computational complexity