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Xin Hua Yang

Bio: Xin Hua Yang is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Convex optimization & Bounded function. The author has an hindex of 3, co-authored 3 publications receiving 845 citations.

Papers
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Journal ArticleDOI
TL;DR: This work uses a bounding technique based on a parameter-dependent Lyapunov function, and then solves the control synthesis problem by reformulating the existence conditions into a semi-infinite dimensional convex optimization.
Abstract: A linear, finite-dimensional plant, with state-space parameter dependence, is controlled using a parameter-dependent controller. The parameters whose values are in a compact set, are known in real time. Their rates of variation are bounded and known in real time also. The goal of control is to stabilize the parameter-dependent closed-loop system, and provide disturbance/error attenuation as measured in induced L2 norm. Our approach uses a bounding technique based on a parameter-dependent Lyapunov function, and then solves the control synthesis problem by reformulating the existence conditions into a semi-infinite dimensional convex optimization. We propose finite dimensional approximations to get sufficient conditions for successful controller design.

798 citations

Proceedings ArticleDOI
21 Jun 1995
TL;DR: In this paper, a linear, finite-dimensional plant, with state-space parameter dependence, is controlled using a parameter-dependent controller, whose parameters whose values are in a compact set, are known in real time.
Abstract: A linear, finite-dimensional plant, with state-space parameter dependence, is controlled using a parameter-dependent controller. The parameters whose values are in a compact set, are known in real time. Their rates of variation are bounded and known in real time also. The goal of control is to stabilize the parameter-dependent closed-loop system, and provide disturbance/error attenuation as measured in induced L/sub 2/ norm. The authors' approach uses a parameter-dependent Lyapunov function, and solves the control synthesis problem by reformulating the existence conditions into an semi-infinite dimensional convex optimization. The authors propose finite dimensional approximations to get sufficient conditions for successful controller design.

76 citations

Proceedings ArticleDOI
21 Jun 1995
TL;DR: In this article, the adaptive output tracking problem in a state feedback configuration, with induced /spl Lscr/sub 2/norm tracking objective, was considered, where the updated parameter vector /spl Theta/ was kept in the prescribed parameter set.
Abstract: Considers the adaptive output tracking problem in a state feedback configuration, with induced /spl Lscr//sub 2/ norm tracking objective The authors' scheme does not guarantee the convergence of the estimated parameter, but the updated parameter vector /spl Theta/ is kept in the prescribed parameter set

8 citations


Cited by
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Journal ArticleDOI
TL;DR: Current research on gain scheduling is clarifying customary practices as well as devising new approaches and methods for the design of nonlinear control systems.

1,621 citations

Journal ArticleDOI
TL;DR: The scope of this paper includes the main theoretical results and design procedures relating to continuous gain-scheduling (in the sense of decomposition of non-linear design into linear sub-problems) control with the aim of providing both a critical overview and a useful entry point into the relevant literature.
Abstract: The gain-scheduling approach is perhaps one of the most popular non-linear control design approaches which has been widely and successfully applied in fields ranging from aerospace to process control. Despite the wide application of gainscheduling controllers and a diverse academic literature relating to gain-scheduling extending back nearly thirty years, there is a notable lack of a formal review of the literature. Moreover, whilst much of the classical gain-scheduling theory originates from the 1960s, there has recently been a considerable increase in interest in gain-scheduling in the literature with many new results obtained. An extended review of the gain-scheduling literature therefore seems both timely and appropriate. The scope of this paper includes the main theoretical results and design procedures relating to continuous gain-scheduling (in the sense of decomposition of non-linear design into linear sub-problems) control with the aim of providing both a critical overview and a useful entry point...

933 citations

Journal ArticleDOI
TL;DR: Two alternative design techniques for constructing gain-scheduled controllers for uncertain linear parameter-varying systems are discussed and are amenable to linear matrix inequality problems via a gridding of the parameter space and a selection of basis functions.
Abstract: This paper is concerned with the design of gain-scheduled controllers for uncertain linear parameter-varying systems. Two alternative design techniques for constructing such controllers are discussed. Both techniques are amenable to linear matrix inequality problems via a gridding of the parameter space and a selection of basis functions. These problems are then readily solvable using available tools in convex semidefinite programming. When used together, these techniques provide complementary advantages of reduced computational burden and ease of controller implementation. The problem of synthesis for robust performance is then addressed by a new scaling approach for gain-scheduled control. The validity of the theoretical results are demonstrated through a two-link flexible manipulator design example. This is a challenging problem that requires scheduling of the controller in the manipulator geometry and robustness in face of uncertainty in the high-frequency range.

887 citations

Journal ArticleDOI
TL;DR: In this article, a tutorial on the mathematical theory and process control applications of linear matrix inequalities and bilinear matrix inequalities (BMIs) is presented, and a software for solving LMI and BMI problems is reviewed.

624 citations

Journal ArticleDOI
TL;DR: A new method of model reduction for nonlinear control systems is introduced, which requires only standard matrix computations and shows that when it is applied to linear systems it results in the usual balanced truncation.
Abstract: In this paper, we introduce a new method of model reduction for nonlinear control systems. Our approach is to construct an approximately balanced realization. The method requires only standard matrix computations, and we show that when it is applied to linear systems it results in the usual balanced truncation. For nonlinear systems, the method makes use of data from either simulation or experiment to identify the dynamics relevant to the input}output map of the system. An important feature of this approach is that the resulting reduced-order model is nonlinear, and has inputs and outputs suitable for control. We perform an example reduction for a nonlinear mechanical system.

570 citations