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In this paper design of switching controllers for linear systems with analog uncertainty is considered. The controllers are LQ controllers and switching sequence is determined by minimization of suitable defined priority function. The priority function includes switching penalty term which introduces cautiousness in switching discrete state. First in the paper are finded conditions for weighting matrices...
This technical note deals with robust state estimation when parametric uncertainties nonlinearly affect a plant state-space model, based on a simultaneous minimization of nominal estimation errors and their sensitivities. An analytic solution is derived for the optimal estimator which can be recursively realized. This estimator has a form similar to the robust estimator of , and its computational...
Based on the main concept of sliding mode control (SMC) theory, this study proposes a robust control law for a class of multivariable linear uncertain systems. As in conventional SMC, the developed method provides robustness against to matched model uncertainty and unknown external disturbances. A control gain used for system transformation is obtained by incorporating linear matrix inequalities (LMIs)...
In this paper, we present a robust Iterative Learning Control (ILC) design for linear systems in the presence of time-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update where the system model contains time-varying parametric uncertainties. An upper bound of the worst-case...
This paper deals with robust regulation problem for discrete-time linear systems subject to uncertainties. The uncertainties are assumed bounded. A new functional based on the combination of penalty functions and weighted game-type cost function is defined to deal with this problem. The solution provided is based on recursive Riccati equations. An interesting feature of this approach is that the recursiveness...
This paper presents a novel algorithm of the robust iterative learning control for linear systems subject to time-invariant parametric uncertainties. The design problem is formulated as a min-max problem with a quadratic performance criterion. Then, we derive an upper-bound of the worst-case performance. Applying Lagrange duality to the minimization problem leads to a dual problem which can be reformulated...
In this paper, a new robust Iterative Learning Control (ILC) algorithm has been proposed for linear systems in the presence of iteration-varying parametric uncertainties. The robust ILC design is formulated as a min-max problem using a quadratic performance criterion subject to constraints of the control input update. An upper bound of the maximization problem is derived, then, the solution of the...
A joint uncertainty model identification and mu-synthesis algorithm is presented for linear time-invariant (LTI) systems. The goal is 1) to construct an uncertainty model set characterized by parameterized weighting functions of dynamic perturbations in the general linear fractional transformation (LFT) form and additive disturbances - customary representation in modern robust control and 2) to select...
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