Global optimization for identification

Abstract

The formulation of the transfer function identification problem leads directly to a nonlinear optimization problem. This nonlinear optimization problem is nonconvex and may exibit many local optima. As a result of the presence of local optimum, optimization methods based upon gradient techniques cannot be guaranteed to converge to the Global Optimum. A relaxation branch and bound technique is proposed to solve the problem. The algorithm will be presented and its convergence prop erties discussed. In addition several simulation examples utilizing the global technique are provided.

Large scale inequality constrained optimization and control

“…In contrast, the performance of IP methods for these problems is independent of the problem and constraint size consequently, significant improvements in speed are realized. Moreover, similar results have been observed in the identification of dynamic systems [74].…”

“…Consequently, the simulations illustrate that solutions to nonlinear optimal control problems can be solved efficiently. Moreover, interior point QP strategies have also been applied to large problems in controller design and state estimation strategies [73], [74].…”

Optimization of IMC-PID Tuning Parameters for Adaptive Control: Part 1

“…Then the PID controller parameters, corresponding phase margin and bandwidth can be calculated from Eq. was identi d using global optimization as show [10]. Fig.…”