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Guanghong Yang - One of the best experts on this subject based on the ideXlab platform.

  • non fragile fuzzy h filter design for nonlinear Continuous Time Systems with d stability constraints
    2012
    Co-Authors: Xiaoheng Chang, Guanghong Yang
    Abstract:

    This paper is concerned with the problem of designing non-fragile H"~ filters for a class of nonlinear Continuous-Time Systems. The considered nonlinear plant is represented by a Takagi-Sugeno (T-S) fuzzy model. Attention is focused on the design of a filter such that the filtering error system guarantees a prescribed H"~ performance level with D stability constraints, where the filter to be designed is assumed to be with multiplicative gain variations. A sufficient condition for the non-fragile H"~ filter design is proposed in terms of linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of a desired H"~ fuzzy filter is given. A simulation example will be given to show the efficiency of the proposed design methods.

  • brief paper insensitive h filter design for Continuous Time Systems with respect to filter coefficient variations
    2010
    Co-Authors: Guanghong Yang, Xianggui Guo
    Abstract:

    This paper is concerned with the problem of designing insensitive H"~ filters for linear Continuous-Time Systems. Coefficient sensitivity functions of transfer functions with respect to filter additive/multiplicative coefficient variations are defined, and the H"~ norms of the sensitivity functions are used to measure the sensitivity of the transfer functions with respect to additive filter coefficient variations. In addition, in order to deal with the filter design problem for the multiplicative filter coefficient variation case, new measures based on the average of the sensitivity functions are also defined. Consequently, the insensitive H"~ filter design problem is reduced to a multi-objective filter design problem, which minimizes the coefficient's sensitivity and meets the prescribed H"~ norm constraint simultaneously. First, a novel method for designing insensitive H"~ filters subjected to additive filter coefficient variations is given in terms of the linear matrix inequality (LMI) optimization techniques. Furthermore, based on the new sensitivity measures, the obtained results are extended to the multiplicative coefficient variation case. In addition, an indirect method for solving the multiplicative variations is also proposed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

  • h model reduction of linear Continuous Time Systems over finite frequency interval
    2010
    Co-Authors: Xin Du, Guanghong Yang
    Abstract:

    This article studies the H∞ model reduction problem for linear Continuous-Time Systems over a finite-frequency interval. Different from the existing methods in the literature, we resort to the aid of recently developed generalised Kalman–Yakubovich–Popov (GKYP) lemma. Based on a in-depth exploitation of the GKYP lemma and the Projection lemma, sufficient conditions for the finite-frequency H∞ model reduction problems are derived and expressed in terms of solutions to a set of linear matrix inequalities (LMIs), which can be handled easily by using the available toolbox. Numerical examples are included for illustration.

  • h model reduction of linear Continuous Time Systems over finite frequency interval lmi based approach
    2009
    Co-Authors: Guanghong Yang
    Abstract:

    This paper studies the model reduction problem for linear Continuous-Time Systems over finite frequency interval. Different form the existing methods in the literature, we resort the problem to the aid of recently developed Generalized Kalman-Yakubovich-Popov (GKYP) lemma. A finite frequency H ∞ model reduction design method is presented in terms of solutions to a set of linear matrix inequalities (LMIs). Numerical examples are included for illustration.

  • brief paper non fragile h filter design for linear Continuous Time Systems
    2008
    Co-Authors: Guanghong Yang, Weiwei Che
    Abstract:

    This paper studies the problem of non-fragile H"~ filter design for linear Continuous-Time Systems. The filter to be designed is assumed to include additive gain variations, which result from filter implementations. A notion of structured vertex separator is proposed to approach the problem, and exploited to develop sufficient conditions for the non-fragile H"~ filter design in terms of solutions to a set of linear matrix inequalities (LMIs). The designs guarantee the asymptotic stability of the estimation errors, and the H"~ performance of the system from the exogenous signals to the estimation errors below a prescribed level. A numerical example is given to illustrate the effect of the proposed method.

Zhongping Jiang - One of the best experts on this subject based on the ideXlab platform.

  • reinforcement learning for linear Continuous Time Systems an incremental learning approach
    2019
    Co-Authors: Tao Bian, Zhongping Jiang
    Abstract:

    In this paper, we introduce a novel reinforcement learning ( RL ) scheme for linear Continuous-Time dynamical Systems. Different from traditional batch learning algorithms, an incremental learning approach is developed, which provides a more efficient way to tackle the on-line learning problem in real-world applications. We provide concrete convergence and robust analysis on this incremental-learning algorithm. An extension to solving robust optimal control problems is also given. Two simulation examples are also given to illustrate the effectiveness of our theoretical result.

  • value iteration and adaptive dynamic programming for data driven adaptive optimal control design
    2016
    Co-Authors: Tao Bian, Zhongping Jiang
    Abstract:

    This paper presents a novel non-model-based, data-driven adaptive optimal controller design for linear Continuous-Time Systems with completely unknown dynamics. Inspired by the stochastic approximation theory, a Continuous-Time version of the traditional value iteration (VI) algorithm is presented with rigorous convergence analysis. This VI method is crucial for developing new adaptive dynamic programming methods to solve the adaptive optimal control problem and the stochastic robust optimal control problem for linear Continuous-Time Systems. Fundamentally different from existing results, the a priori knowledge of an initial admissible control policy is no longer required. The efficacy of the proposed methodology is illustrated by two examples and a brief comparative study between VI and earlier policy-iteration methods.

  • h _ infty tracking control of completely unknown Continuous Time Systems via off policy reinforcement learning
    2015
    Co-Authors: Hamidreza Modares, Frank L Lewis, Zhongping Jiang
    Abstract:

    This paper deals with the design of an $ {H}_{ {\infty }}$ tracking controller for nonlinear Continuous-Time Systems with completely unknown dynamics. A general bounded $L_{2} $ -gain tracking problem with a discounted performance function is introduced for the $ {H}_{ {\infty }}$ tracking. A tracking Hamilton–Jacobi–Isaac (HJI) equation is then developed that gives a Nash equilibrium solution to the associated min–max optimization problem. A rigorous analysis of bounded $L_{2}$ -gain and stability of the control solution obtained by solving the tracking HJI equation is provided. An upper-bound is found for the discount factor to assure local asymptotic stability of the tracking error dynamics. An off-policy reinforcement learning algorithm is used to learn the solution to the tracking HJI equation online without requiring any knowledge of the system dynamics. Convergence of the proposed algorithm to the solution to the tracking HJI equation is shown. Simulation examples are provided to verify the effectiveness of the proposed method.

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

  • Optimal Control of Nonlinear Continuous-Time Systems in Strict-Feedback Form
    2015
    Co-Authors: Hassan Zargarzadeh, Travis Dierks, Sarangapani Jagannathan
    Abstract:

    This paper proposes a novel optimal tracking control scheme for nonlinear Continuous-Time Systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear Continuous-Time Systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear Continuous-Time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton–Jacobi–Bellman estimation error in a forward-in-Time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.

  • optimal control of affine nonlinear Continuous Time Systems
    2010
    Co-Authors: Travis Dierks, Sarangapani Jagannathan
    Abstract:

    In this paper, the optimal regulation and tracking control of affine nonlinear Continuous-Time Systems with known dynamics is undertaken using a novel single online approximator (SOL)-based scheme. The SOLA-based adaptive approach is designed to learn the infinite horizon Continuous-Time Hamilton-Jacobi-Bellman (HJB) equation and its corresponding optimal control input. A novel parameter tuning algorithm is derived which not only ensures the optimal cost (HJB) function and control input are achieved, but also ensures the system states remain bounded during the online learning process. Lyapunov techniques show that all signals are uniformly ultimately bounded (UUB) and the approximated control signal approaches the optimal control input with small bounded error. In the absence of OLA reconstruction errors, asymptotic convergence to the optimal control is shown. Simulation results illustrate the effectiveness of the approach.

Tianmin Huang - One of the best experts on this subject based on the ideXlab platform.

  • static output feedback control for positive linear Continuous Time Systems
    2013
    Co-Authors: Cuihong Wang, Tianmin Huang
    Abstract:

    SUMMARY This paper studies the problem of designing the static output feedback controller for the positive linear Continuous-Time Systems. On the basis of a system augmentation approach, a novel characterization on the stable condition of the closed-loop system is firstly established. Then, a necessary and sufficient condition is given to ensure the existence of the desired static output feedback controller, and an iterative linear matrix inequality algorithm is presented to compute the feedback gain matrix. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.

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

  • neural network based event triggered state feedback control of nonlinear Continuous Time Systems
    2016
    Co-Authors: Avimanyu Sahoo, S Jagannathan
    Abstract:

    This paper presents a novel approximation-based event-triggered control of multi-input multi-output uncertain nonlinear Continuous-Time Systems in affine form. The controller is approximated using a linearly parameterized neural network (NN) in the context of event-based sampling. After revisiting the NN approximation property in the context of event-based sampling, an event-triggered condition is proposed using the Lyapunov technique to reduce the network resource utilization and to generate the required number of events for the NN approximation. In addition, a novel weight update law for aperiodic tuning of the NN weights at triggered instants is proposed to relax the knowledge of complete system dynamics and to reduce the computation when compared with the traditional NN-based control. Nonetheless, a nonzero positive lower bound for the inter-event Times is guaranteed to avoid the accumulation of events or Zeno behavior. For analyzing the stability, the event-triggered system is modeled as a nonlinear impulsive dynamical system and the Lyapunov technique is used to show local ultimate boundedness of all signals. Furthermore, in order to overcome the unnecessary triggered events when the system states are inside the ultimate bound, a dead-zone operator is used to reset the event-trigger errors to zero. Finally, the analytical design is substantiated with numerical results.

  • a decentralized fault detection and prediction scheme for nonlinear interconnected Continuous Time Systems
    2012
    Co-Authors: Hasan Ferdowsi, Deepthi L Raja, S Jagannathan
    Abstract:

    Complex nonlinear Systems such as an aircraft, trains, automobiles, power plants and chemical plants are represented as nonlinear interconnected subSystems. Therefore, in this paper a novel decentralized fault diagnosis and prognosis (FDP) methodology is proposed for such large-scale Systems. Current FDP approaches require the knowledge of the entire state or its estimated vector. But the main goal in this work is to design a local fault detector (LFD) or observer for each subsystem based on the measured local states of the subsystem alone. A local residual signal is generated via the measured states of the local subsystem and the estimated states provided by the LFD. A fault is detected when this local residual exceeds a predefined threshold. The adaptive online approximator in each LFD is activated upon detection to compensate the fault dynamics due to local and non-local faults. A novel update law for tuning the parameters of the online approximator is derived. Upon detection, faults local to the subsystem and to other subSystems are isolated. In addition, the proposed scheme provides the Time to failure (or remaining useful life) information by using local measurements and the parameter update law of the LFD. Simulation results verify the effectiveness of the proposed decentralized FDP scheme.

  • optimal control of affine nonlinear Continuous Time Systems using an online hamilton jacobi isaacs formulation
    2010
    Co-Authors: Travis Dierks, S Jagannathan
    Abstract:

    Solving the Hamilton-Jacobi-Isaacs (HJI) equation, commonly used in H ∞ optimal control, is often referred to as a two-player differential game where one player tries to minimize the cost function while the other tries to maximize it. In this paper, the HJI equation is formulated online and forward-in-Time using a novel single online approximator (SOLA)-based scheme to achieve optimal regulation and tracking control of affine nonlinear Continuous-Time Systems. The SOLA-based adaptive approach is designed to learn the infinite horizon HJI equation, the corresponding optimal control input, and the worst case disturbance. A novel parameter tuning algorithm is derived which not only achieves the optimal cost function, control input, and the disturbance, but also ensures the system states remain bounded during the online learning. Lyapunov methods are used to show that all signals are uniformly ultimately bounded (UUB) while ensuring the approximated signals approach their optimal values with small bounded error. In the absence of OLA reconstruction errors, asymptotic convergence to the optimal signals is demonstrated, and simulation results illustrate the effectiveness of the approach.