In this paper, we are interested in linearization techniques for exact resolution of the Unconstrained Quadratic (0-1) Problem. Our purpose is to propose ”economical” linear formulations. We first extend the current techniques in a general linearization framework containing many other schemes and propose a new linear formulation. Numerical results comparing classical, Glover’s and the new linea...

The introduction of state equation model of the electrical machine based on direct and quadrature axes variables has paved the way for powerful control theories to be brought to bear on the problem of control of electric machines. Exact linearization has been applied to the control of permanent magnet synchronous motor (PMSM). In this paper, application of approximate linearization is proposed ...

u = a(x) + b(x)v, (3) where b(x) is a nonsingular matrix, and v ∈ Rp is a transformed or virtual input, so that the inputoutput response between v and y is linear. This requirement is called input-output linearization because only the input-output map h is made linear. On the other hand, full state linearization refers to the case when the whole state x must be “linearized”. Linearization of th...

Maneuver Regulation, Transverse Feedback Linearization and Zero Dynamics Christopher Nielsen Masters of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2004 The maneuver regulation (or path following) problem has received considerable attention in the control literature where feedback linearization has proven an effective approach to solving the ...

u = a(x) + b(x)v, (3) where b(x) is a nonsingular matrix, and v ∈ Rp is a transformed or virtual input, so that the inputoutput response between v and y is linear. This requirement is called input-output linearization because only the input-output map h is made linear. On the other hand, full state linearization refers to the case when the whole state x must be “linearized”. Linearization of th...

This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of system model. The is realized by regressing model using online process data and incremental algorithm. proposed control (MPC) approach requires less computational load compared to other neural network MPC approaches. More importantly, precision enhanced with reduced opera...

Abstract Current generalizations of the central ideas single-objective branch-and-bound to multiobjective setting do not seem follow their train thought all way. The present paper complements various suggestions for partial lower bounds and overall upper by general constructions from bounds, corresponding termination criteria node selection steps. In particular, our concept employs a new enclos...

We investigate the transfer function emanating from linearization of a car-following model for human drivers when taking into account their reaction time, which is known to be cause stop-and-go traffic phenomenon. This leads nonrational function, implying nontrivial stability conditions are explicitly given. They in particular satisfied whenever string holds. also show how this time can introdu...