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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, iterative learning control (ILC) with field oriented control is proposed to reject periodic velocity ripple which are casually detent torque and other various sources in permanent magnet stepper motor (PMSM). Single-input single-output (SISO) system structure with field-oriented control (FOC) is proposed to be applied to ILC. To make the error convergence fast, current error based ILC...
This paper describes an automatic methodology for optimizing sample point selection for using in the framework of model order reduction (MOR). The procedure, based on the maximization of the dimension of the subspace spanned by the samples, iteratively selects new samples in an efficient and automatic fashion, without computing the new vectors and with no prior assumptions on the system behavior....
The Affine Scaling Transformation (AST) family of algorithms can solve the minimization of the l(ples1), p-norm-like diversity measure for an underdetermined linear inverse problem. The AST algorithms can therefore be used to solve the sparse signal recovery problem that arises in Compressive Sensing. In this paper, we continue to investigate the application of the iterative AST family of algorithms...
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