Multiobjective identification in computer-aided design of automatic control systems

The problems of parametrical identification of dynamical plants in scalar and vector formulations are considered. The techniques-scenarios of sequential (stage-by-stage) linear and nonlinear multiobjective identification are presented, which use scalar search with integral criteria on initial stages, indirect sounding on weight grids of additive criteria and direct sounding with vector criteria on final stages using genetic algorithm. The examples are given to Pareto-optimal parameter estimation for linear and nonlinear models of an electromechanical plant.