Multidisciplinary Optimal Design of an Active Control System and State Estimator for an Aircraft Wing

Abstract

A multidisciplinary and multi-objective optimization approach that integrates the design of the control surfaces’ sizes, active control system, and estimator for an aircraft’s wing with three control surfaces is developed in this paper. Four objectives are considered: minimizing impacts of external gust loads, maximizing stability robustness, reducing control energy consumption, and minimizing the Frobenius norm of the estimator gains. For simulation purposes, a mathematical model of a flexible wing having three ailerons is used. The control system and observer are designed simultaneously. The optimization problem is formulated and solved by NSGA-II (non-dominated sorting genetic algorithm II). The solution of the optimization problem is called the Pareto set and the corresponding set of function evaluations is called Pareto front. The properties of the Pareto set and Pareto front; sensitives of the dominant poles of the open-loop system, closed-loop system, and estimator to the airspeed; and responses of the controlled, uncontrolled, and observer models at selected objective values are obtained. The results shows that the simultaneous design of the control and estimator algorithms, and the geometry of the ailerons in the multi-objective settings is very effective, the closed-loop control system can suppress the flutter and stabilize the system, and the estimator converges very quickly and always stable regardless of the air stream velocity.