In this paper we consider the robust partial quadratic eigenvalue assignment problem for second-order control systems by state feedback. To simultaneously reduce the feedback norms and the sensitivity of the close-loop eigenvalues, we formulate the problem as an unconstraint minimization problem for a new proposed cost function. An explicit analytic expression of the gradient of the cost functi...

We propose a general, numerically reliable computational approach to solve the pole and eigenstructure assignment problem for descriptor systems. In the multi-input case, the proposed approach addresses the intrinsic non-uniqueness of the pole assignment problem solution by simultaneously minimizing the sensitivity of the feedback gain and of closed-loop eigenvalues. For this purpose, a minimum...

| This paper presents a fast and eecient algorithm to estimate the area cost of a given RTL datapath. This is achieved by considering the physical length of components (provided by a component library) and connections data (given by the datapath description) within an actual layout model and using analytical formulas in a constructive algorithm. Our layout estimator uses a non-probabilistic bas...

A difficulty with constrained nonlinear control is the minimization of the cost function. With complex system representations such as fundamental models, the required optimization algorithm may be complex to implement, setting its parameters may be difficult and the calculation time may be long. To overcome these problems, an innovative optimization-free predictive control scheme is proposed. T...

water and energy are key commodities utilized in the process industries.water minimization and energy minimization have been studied separately. in this paper, a new systematic design methodology has been developed for the simultaneous management of energy and water systems that also feature maximum re-use of water. in addition to allowing re-use of water, issues about heat losses inside unit o...

This paper addresses signal denoising when large-amplitude coefficients form clusters (groups). The L1-norm and other separable sparsity models do not capture the tendency of coefficients to cluster (group sparsity). This work develops an algorithm, called ‘overlapping group shrinkage’ (OGS), based on the minimization of a convex cost function involving a group-sparsity promoting penalty functi...