11,836 research outputs found

    Robust H-infinity finite-horizon control for a class of stochastic nonlinear time-varying systems subject to sensor and actuator saturations

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    Copyright [2010] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This technical note addresses the robust H∞ finite-horizon output feedback control problem for a class of uncertain discrete stochastic nonlinear time-varying systems with both sensor and actuator saturations. In the system under investigation, all the system parameters are allowed to be time-varying, the parameter uncertainties are assumed to be of the polytopic type, and the stochastic nonlinearities are described by statistical means which can cover several classes of well-studied nonlinearities. The purpose of the problem addressed is to design an output feedback controller, over a given finite-horizon, such that the H∞ disturbance attenuation level is guaranteed for the nonlinear stochastic polytopic system in the presence of saturated sensor and actuator outputs. Sufficient conditions are first established for the robust H∞ performance through intensive stochastic analysis, and then a recursive linear matrix inequality (RLMI) approach is employed to design the desired output feedback controller achieving the prescribed H∞ disturbance rejection level. Simulation results demonstrate the effectiveness of the developed controller design scheme.This work was supported under Australian Research Council’s Discovery Projects funding scheme (project DP0880494) and by the German Science Foundation (DFG) within the priority programme 1305: Control Theory of Digitally Networked Dynamical Systems. Recommended by Associate Editor H. Ito

    Decentralised delay-dependent static output feedback variable structure control

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    In this paper, an output feedback stabilisation problem is considered for a class of large scale interconnected time delay systems with uncertainties. The uncertainties appear in both isolated subsystems and interconnections. The bounds on the uncertainties are nonlinear and time delayed. It is not required that either the known interconnections or the uncertain interconnections are matched. Then, a decentralised delay-dependant static output feedback variable structure control is synthesised to stabilise the system globally uniformly asymptotically using the Lyapunov Razumikhin approach. A case study relating to a river pollution control problem is presented to illustrate the proposed approach

    Stabilization, pointing and command control of a balloon-borne 1-meter telescope

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    A 1-meter balloon-borne telescope has been constructed and flown to observe far-infrared radiation from celestial sources. The attitude control systems must perform to the diffraction limit of the telescope for stabilization and have positioning capability for source acquisition. These and associated systems are discussed in detail, as is the command control of the payload as a whole

    Learning how to be robust: Deep polynomial regression

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    Polynomial regression is a recurrent problem with a large number of applications. In computer vision it often appears in motion analysis. Whatever the application, standard methods for regression of polynomial models tend to deliver biased results when the input data is heavily contaminated by outliers. Moreover, the problem is even harder when outliers have strong structure. Departing from problem-tailored heuristics for robust estimation of parametric models, we explore deep convolutional neural networks. Our work aims to find a generic approach for training deep regression models without the explicit need of supervised annotation. We bypass the need for a tailored loss function on the regression parameters by attaching to our model a differentiable hard-wired decoder corresponding to the polynomial operation at hand. We demonstrate the value of our findings by comparing with standard robust regression methods. Furthermore, we demonstrate how to use such models for a real computer vision problem, i.e., video stabilization. The qualitative and quantitative experiments show that neural networks are able to learn robustness for general polynomial regression, with results that well overpass scores of traditional robust estimation methods.Comment: 18 pages, conferenc

    Structural Analysis and Control of a Model of Two-site Electricity and Heat Supply

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    This paper introduces a control problem of regulation of energy flows in a two-site electricity and heat supply system, where two Combined Heat and Power (CHP) plants are interconnected via electricity and heat flows. The control problem is motivated by recent development of fast operation of CHP plants to provide ancillary services of power system on the order of tens of seconds to minutes. Due to the physical constraint that the responses of the heat subsystem are not necessary as fast as those of the electric subsystem, the target controlled state is not represented by any isolated equilibrium point, implying that stability of the system is lost in the long-term sense on the order of hours. In this paper, we first prove in the context of nonlinear control theory that the state-space model of the two-site system is non-minimum phase due to nonexistence of isolated equilibrium points of the associated zero dynamics.Instead, we locate a one-dimensional invariant manifold that represents the target controlled flows completely. Then, by utilizing a virtual output under which the state-space model becomes minimum phase, we synthesize a controller that achieves not only the regulation of energy flows in the short-term regime but also stabilization of an equilibrium point in the long-term regime. Effectiveness of the synthesized controller is established with numerical simulations with a practical set of model parameters

    Nonlinear control of feedforward systems with bounded signals

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    Decentralized reliable control for large-scale LTI systems

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    Reliable control concerns the ability of closed loop system to maintain stability and regulation properties during arbitrary sensor, controller, and actuator failure. Reliable control research has been an active research topic for more than 10 years. Recent approach for reliable control includes the H∞ method, the algebraic factorization design, and the robust servomechanism control. These methods have been surveyed and discussed in this thesis with the robust servomechanism control methodology serving as the basis of the research development of this work. In this thesis, the reliable control for large-scale, multi-input/output linear system is considered. Two concepts of reliable control are introduced in this work: (1) Decentralized Robust Servomechanism Problem with Complete Reliability (DRSPwCR) and (2) Block Decentralized Robust Servo Problem with Complete Reliability (BDRSPwCR). The DRSPwCR solves the reliable control problem by applying strict diagonal decentralized controller configurations. The BDRSPwCR solves the reliable control problem by applying block diagonal decentralized controller configurations. Research results of solving DRSPwCR for the class of minimum phase systems is first developed in this work. The problem is solved by applying strict decentralized PIDr control to an otherwise unreliable plant and thus significantly extending the class of processes that can be controlled reliably. Research results of solving BDRSPwCR is developed for plants which have a pre-imposed block diagonal structure or plants with non-minimum phase minors. The reliable control conditions for an arbitrary linear system is then analyzed, and a general controller synthesis for solving the reliable control problem for arbitrary linear system is given in this work. The DRSPwCR can be applied in many industry areas as well as in the transportation area. In this work, the reliable control results are applied in the urban vehicle traffic network. A traffic queue length model is developed, a control algorithm is synthesized, and simulations are made under different traffic subsystem failure modes such as non-functioning traffic lights, traffic accidents, and intersection blockage, etc. Finally, future research topics such as to relax the constraints of plants to achieve reliable control and to optimize the closed loop system dynamic performances, etc. are proposed

    Delay-independent decentralised output feedback control for large-scale systems with nonlinear interconnections

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    In this paper, a stabilisation problem for a class of large-scale systems with nonlinear interconnections is considered. All the uncertainties are nonlinear and are subject to the effects of time delay. A decentralised static output feedback variable structure control is synthesised and the stability of the corresponding closed-loop system is analysed based on the Lyapunov Razumikhin approach. A set of conditions is developed to guarantee that the large-scale interconnected system is stabilised uniformly asymptotically. Further study shows that the conservatism can be reduced by employing additive controllers if the known interconnections are separated into matched and mismatched parts. It is not required that the subsystems are square. The designed controller is independent of time delay and thus it does not require memory. Simulation results show the effectiveness of the proposed approach
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