in this paper, a two-phase algorithm, namely ivns, is proposed for solving nonlinear optimal control problems. in each phase of the algorithm, we use a variable neighborhood search (vns), which performs a uniform distribution in the shaking step and the successive quadratic programming, as the local search step. in the first phase, vns starts with a completely random initial solution of control...

Needs for increased product quality, reduced pollution, and reduced energy and material consumption are driving enhanced process integration. This increases the number of manipulated and measured variables required by the control system to achieve its objectives. This paper addresses the question of whether processes tend to become increasingly more difficult to identify and control as the proc...

In this paper we consider the identification of a linear module that is embedded in a dynamic network using noisy measurements of the internal variables of the network. This is an extension of the errors-in-variables (EIV) identification framework to the case of dynamic networks. The consequence of measuring the variables with sensor noise is that the prediction error identification methods no ...