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丁宝苍, Baocang Dinga, ?, Yugeng Xib, Marcin T. Cychowskic, Thomas O’Mahonyc
Automatica 44(2008)258– 264,-0001,():
-1年11月30日
This paper addresses the synthesis approach for output feedback robust model predictive control for systems with polytopic description, bounded state disturbance and measurement noise. Off-line, it calculates a sequence of output feedback laws based on the state estimators, by solving linear matrix inequality (LMI) optimization problems. On-line, at each sampling time, it chooses an appropriate output feedback law from this sequence.The primary contribution is to present a rigorous method to guarantee satisfaction of input/state constraints. A numerical example is given to illustrate the effectiveness of the controller.
Robust model predictive control, Output feedback, State estimator, Constraint
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丁宝苍, BaoCang Ding?, , HeXu Sun, PengYang
Automatica 42(2006)503-508,-0001,():
-1年11月30日
This paper further studies stabilization of nonlinear systems represented by a Takagi–Sugeno (T-S) discrete fuzzy model. By extending a nonquadratic Lyapunov function and applying a nonparallel distributed compensation (non-PDC) law, a new stabilization conclusion is presented that is a generalization of some previous results in the literature. The new conclusion under a special situation is also suitable for a PDC law, which presents to be more relaxed than some existing results. An example is provided to demonstrate the effectiveness of the new conclusions.
T-S fuzzy systems, Parameter-dependent Lyapunov function, Parallel distributed compensation, Nonquadratic Lyapunov function
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【期刊论文】Constrained robust model predictive control for time-delaysystems with polytopic description
丁宝苍, B. DING*yz and B. HUANGy
,-0001,():
-1年11月30日
This paper considers an infinite-horizon min–max model predictive control (MPC) problemfor time-delay discrete systems with a polytopic uncertainty description, which has rarely beenaddressed in the literature. Several new solutions to this problem are proposed which are intercomplementary.If there is an input delay, then the single state feedback solution requires allthe system matrices to be stable. This deficiency can be removed by adding free control movesbefore a modified state feedback law. If there is no input delay, then a computationallyefficient off-line state feedback solution can be applied. In order to obtain desirable region ofattraction while at the same time preserve reasonably low on-line computational effort, onecan choose the standard solution. Simulation results are given to illustrate these new solutions.
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【期刊论文】Stability analysis of generalized predictive control based on Kleinman’s controllers
丁宝苍, DING Baocang & XI Yugeng
,-0001,():
-1年11月30日
With Kleinman's controller, its extended form and Riccati iteration as analyzing tools, the stability of GPC under various parameter cases is discussed. The overall closed-loop stability conclusions of GPC in equivalence with Kleinman's controller are obtained, which cover some existing results and provide the theoretical foundation for stable design of predictive control.
Kleinman', s controller,, Riccati iteration,, generalized predictive control,, stability.,
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丁宝苍, Baocang Ding*
,-0001,():
-1年11月30日
This article addresses the robust model predictive control (MPC) for systems with polytopic uncertaintydescription. In the case of multiple state delays, some results are given in the previous works. However, previousworks are not well established for input-delay systems. This article proposes new approaches that can effectivelyhandle not only the input-delay, but also the multiple delayed input. For the time-delay system, a general MPCsolution is based on the use of augmented delay-free state-space description, which can be computationallyexpensive. Hence, in general there are simplified formulations based on the time-delay description, which can beapplied in different situations. This article proposes three simplified formulations, with the first incorporating aprevious result as a special case, the second being a solution for a unique class of systems and the third being amore general solution without special assumptions on the plant. Numerical examples illustrate the effectivenessof the proposed approaches.
model predictive control, uncertain systems, time-delay systems, state feedback, Lyapunov method
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