The Experts below are selected from a list of 300 Experts worldwide ranked by ideXlab platform

Anthony Tzes - One of the best experts on this subject based on the ideXlab platform.

  • Weighted minimum uncertainty prediction control
    Automatica, 1996
    Co-Authors: Anthony Tzes
    Abstract:

    The control design problem for a Discrete Time System with structured parametric uncertainty, that minimizes its predicted output uncertainty is addressed in this note. The System's ARMA parameter vector coefficients are bounded within an interval. For a given future input sequence, this parameter uncertainty induces an uncertainty in the predicted System output. The control objective is to derive the input sequence that minimizes the predicted output uncertainty while tracking a reference input.

X Zhao - One of the best experts on this subject based on the ideXlab platform.

  • linear matrix inequality approach to static output feedback stabilisation of Discrete Time networked control Systems
    Iet Control Theory and Applications, 2010
    Co-Authors: Fei Hao, X Zhao
    Abstract:

    This study is concerned with the static output-feedback stabilisation problem of Discrete-Time networked control Systems. If the controlled plant is a Discrete-Time System, the networked control System with Time-varying network-induced delays and data packet dropouts in the transmission is modelled as a Discrete-Time System with Time-varying delays in the state. The network-induced delays are assumed to have both an upper bound and a lower bound. Next, an asymptotic stability condition for the networked control Systems is established, which depends on the upper and lower bounds of delay Times. Then, three approaches to the static output-feedback controller are proposed, where the effect of both network-induced delays and data packet dropouts has been considered. Furthermore, the robust stability condition and controller design method for such networked control Systems with structured uncertainties are presented. All the results are formulated in the terms of linear matrix inequalities (LMIs), which are numerically very efficiently solved via LMI toolbox in the Matlab. Finally, three examples are worked out to illustrate the feasibility and effectiveness of the proposed method.

S Jagannathan - One of the best experts on this subject based on the ideXlab platform.

  • stochastic optimal design for unknown linear Discrete Time System zero sum games in input output form under communication constraints
    Asian Journal of Control, 2014
    Co-Authors: S Jagannathan, Frank L Lewis
    Abstract:

    In this paper, stochastic optimal strategy for unknown linear Discrete-Time System quadratic zero-sum games in input-output form with communication imperfections such as network-induced delays and packet losses, otherwise referred to as networked control System (NCS) zero-sum games, relating to the H∞ optimal control problem is solved in a forward-in-Time manner. First, the linear Discrete-Time zero sum state space representation is transformed into a linear NCS in the state space form after incorporating random delays and packet losses and then into the input-output form. Subsequently, the stochastic optimal approach, referred to as adaptive dynamic programming (ADP), is introduced which estimates the cost or value function to solve the infinite horizon optimal regulation of unknown linear NCS quadratic zero-sum games in the presence of communication imperfections. The optimal control and worst case disturbance inputs are derived based on the estimated value function in the absence of state measurements. An update law for tuning the unknown parameters of the value function estimator is derived and Lyapunov theory is used to show that all signals are asymptotically stable (AS) and that the estimated control and disturbance signals converge to optimal control and worst case disturbances, respectively. Simulation results are included to verify the theoretical claims.

  • neural network based adaptive event triggered control of affine nonlinear Discrete Time Systems with unknown internal dynamics
    American Control Conference, 2013
    Co-Authors: Avimanyu Sahoo, S Jagannathan
    Abstract:

    In this paper, the design of a neural network (NN) based adaptive model-based event-triggered control of an uncertain single input single output (SISO) nonlinear Discrete Time System in affine form is presented. The controller uses an adaptive estimator consisting of a single-layer NN not only to approximate the internal dynamics of an affine nonlinear Discrete-Time System but also to provide an estimate of the state vector during inter event interval. The NN weights of the adaptive NN estimator are tuned in a aperiodic manner at the event trigger instants unlike periodic updates in standard adaptive neural network (NN) control. A dead zone operator is used to reset the event trigger error to zero as long as the System states continue to remain in a bounded region due to NN reconstruction errors. Lyapunov method is used to derive the event trigger condition, prove uniform ultimate boundedness (UUB) of the NN weight estimation error and System states.

  • neural network based state feedback control of a nonlinear Discrete Time System in nonstrict feedback form
    IEEE Transactions on Neural Networks, 2008
    Co-Authors: S Jagannathan
    Abstract:

    In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear Discrete-Time System expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in Discrete-Time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.

Jinhua She - One of the best experts on this subject based on the ideXlab platform.

  • output feedback stabilization for a Discrete Time System with a Time varying delay
    IEEE Transactions on Automatic Control, 2008
    Co-Authors: Guoping Liu, Jinhua She
    Abstract:

    This study employs the free-weighting matrix approach to investigate the output feedback control of a linear Discrete-Time System with an interval Time-varying delay. First, the delay-dependent stability is analyzed using a new method of estimating the upper bound on the difference of a Lyapunov function without ignoring any terms; and based on the results, a design criterion for a static output feedback (SOF) controller is derived. Since the conditions thus obtained for the existence of admissible controllers are not expressed strictly in terms of linear matrix inequalities, a modified cone complementarity linearization algorithm is employed to solve the nonconvex feasibility SOF control problem. Furthermore, the problem of designing a dynamic output feedback controller is formulated as one of designing an SOF controller. Numerical examples demonstrate the effectiveness of the method and its advantage over existing methods.

Magdi S. Mahmoud - One of the best experts on this subject based on the ideXlab platform.

  • Positive real analysis and synthesis of uncertain Discrete Time Systems
    IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 2000
    Co-Authors: Magdi S. Mahmoud, Lihua Xie
    Abstract:

    The positive real analysis and synthesis problems for a class of uncertain Discrete-Time Systems are considered. The uncertainties are expressed in a linear fractional form. The analysis problem is to characterize conditions for a which the uncertain Discrete-Time System is positive real for all admissible uncertainties. A linear matrix inequality (LMI)-based analysis result is derived. The synthesis problem is to design linear state-feedback and dynamic output-feedback controllers that robustly stabilize the uncertain Discrete-Time System and achieve the strict positive realness for the closed-loop transfer functions. It has been established that the problems above can be cast into a class of parameterized strict positive real control problems without uncertainties.

  • Robustness measures for a class of Discrete-Time Systems
    Journal of The Franklin Institute-engineering and Applied Mathematics, 1994
    Co-Authors: Magdi S. Mahmoud
    Abstract:

    Abstract A class of linear Discrete-Time Systems subject to cone-bounded perturbations as a function of the delayed state is considered. Upper bounds on these perturbations are derived so that the Discrete-Time System remains uniformly asymptotically stable. The effect of state transformation and the choice of Lyapunov matrices on stability robustness are examined in detail and it is established that improvement of the perturbation bounds is possible in some particular cases.