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ABSTRACT: This paper considers the control problem for networked control systems (NCSs) with unreliable data communication. The unreliable
data communication simultaneously exists in both control channel (from controller to actuator) and measurement channel (from
sensor to controller) and may cause consecutive packet delays and losses. A new model is established based on all possible
consecutive packet delays and losses. The observer-based controller is designed to exponentially stabilize the networked system
in the sense of mean square, and also achieve the prescribed H
∞ disturbance attenuation level. An iterative algorithm is developed to compute the optimal H
∞ disturbance attenuation and the controller parameters by solving the semi-definite programming problem via interior-point
approach. An illustrative example is provided to show the applicability of the proposed method.
Keywords
H
∞ control-networked control systems-packet delay and loss-semi-definite programming problem-unreliable data communication
International Journal of Control Automation and Systems 04/2012; 8(4):769-775. · 0.75 Impact Factor
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ABSTRACT: In this paper, the problem of set-membership filtering is considered for discrete-time systems with equality and inequality constraints between their state variables. We formulate the problem of set-membership filtering as finding the set of estimates that belong to an ellipsoid. A centre and a shape matrix of the ellipsoid are used to describe the set of estimates and the solution to the set of estimates is obtained in terms of matrix inequality. Unknown but bounded process and measurement noises are handled under the inequality constraints by using S-procedure. We apply Finsler's lemma to project the set of estimates onto the constrained surface. A recursive algorithm is developed for computing the ellipsoid that guarantees to contain the true state under the state constraints, which is easily implemented by semi-definite programming via interior-point approach. A vehicle tracking example is provided to demonstrate the effectiveness of the proposed set-membership filtering with state equality constraints.
IEEE Transactions on Aerospace and Electronic Systems 11/2009; · 1.10 Impact Factor
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ABSTRACT: In this technical note, the problem of set-membership filtering is considered for discrete-time systems with nonlinear equality constraint between their state variables. The nonlinear equality constraint is first linearized and transformed into a state linear equality constraint with two uncertain quantities related to linearizing truncation error and base point error. S-procedure method is then applied to merge all inequalities into one inequality and the solution to the unconstrained set-membership filtering problem is provided. The set-membership filter with state constraint is finally derived from projecting the unconstrained set-membership filter onto the constrained surface by using Finsler's Lemma. A time-varying linear matrix inequality optimization based approach is proposed to design the set-membership filter with nonlinear equality constraint. A recursive algorithm is developed for computing the state estimate ellipsoid that guarantees to contain the true state. An illustrative example is provided to demonstrate the effectiveness of the proposed set-membership filtering with nonlinear equality constraint.
IEEE Transactions on Automatic Control 11/2009; · 2.11 Impact Factor
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ABSTRACT: This paper is concerned with the set-membership filtering (SMF) problem for discrete-time nonlinear systems. We employ the Takagi-Sugeno (T-S) fuzzy model to approximate the nonlinear systems over the true value of state and to overcome the difficulty with the linearization over a state estimate set rather than a state estimate point in the set-membership framework. Based on the T-S fuzzy model, we develop a new nonlinear SMF estimation method by using the fuzzy modeling approach and the S-procedure technique to determine a state estimation ellipsoid that is a set of states compatible with the measurements, the unknown-but-bounded process and measurement noises, and the modeling approximation errors. A recursive algorithm is derived for computing the ellipsoid that guarantees to contain the true state. A smallest possible estimate set is recursively computed by solving the semidefinite programming problem. An illustrative example shows the effectiveness of the proposed method for a class of discrete-time nonlinear systems via fuzzy switch.
IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 08/2009; 40(1):116-24. · 3.01 Impact Factor
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Automatica. 01/2009; 45:1896-1902.
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ABSTRACT: In this paper, a variance constrained filtering problem is considered for systems with both non-Gaussian noises and polytopic uncertainty. A novel filter is developed to estimate the systems states based on the current observation and known deterministic input signals. A free parameter is introduced in the filter to handle the uncertain input matrix in the known deterministic input term. In addition, unlike the existing variance constrained filters, which are constructed by the previous observation, the filter is formed from the current observation. A time-varying linear matrix inequality (LMI) approach is used to derive an upper bound of the state estimation error variance. The optimal bound is obtained by solving a convex optimisation problem via Semi-Definite Programming (SDP) approach. Simulation results are provided to demonstrate the effectiveness of the proposed method.
Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on; 05/2008
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ABSTRACT: In this letter, an error square constrained filtering problem is considered for systems with both non-Gaussian noises and polytopic uncertainty. A novel filter is developed to estimate the systems states based on the current observation and known deterministic input signals. A free parameter is introduced in the filter to handle the uncertain input matrix in the known deterministic input term. In addition, unlike the existing variance constrained filters, which are constructed by the previous observation, the filter is formed from the current observation. A time-varying linear matrix inequality (LMI) approach is used to derive an upper bound of the state estimation error square. The optimal bound is obtained by solving a convex optimization problem via semi-definite programming (SDP) approach. Simulation results are provided to demonstrate the effectiveness of the proposed method.
IEEE Signal Processing Letters 02/2008; · 1.39 Impact Factor
