Closed-Loop System Identification with Genetic Algorithms

High-performance control design for a flexible space structure is challenging as high-fidelity 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 on-orbit 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 control-relevant 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.