Closed-Loop System Identification with Genetic Algorithms

High-performance control design for a flexible space structure is challenging as highfidelity plant models are difficult to obtain a priori. Uncertainty in the control design models typically require a very robust, low-performance control design, which must be tuned onorbit to achieve the required performance. Closed-loop system identification is often required to obtain a multivariable open-loop plant model based on closed-loop response data. To provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is used to mitigate the nonuniqueness and overparameterization of general state space realizations. This controlrelevant system identification procedure stresses the joint nature of the system identification and control design problem by seeking to obtain a model that minimizes the difference between the predicted and actual closed-loop performance.