Robust State Feedback Control Design with Probabilistic System Parameters
For control engineers designing robust controllers for systems with uncertain parameters, this method offers a computationally efficient alternative to Monte-Carlo approaches.
The paper presents a polynomial chaos framework for stability analysis and optimal controller synthesis in linear systems with probabilistic parameters, achieving exponential mean square stability guarantees with significant computational advantages over Monte-Carlo methods, demonstrated on an F-16 aircraft model.
In this paper, a new polynomial chaos based framework for analyzing linear systems with probabilistic parameters is presented. Stability analysis and synthesis of optimal quadratically stabilizing controllers for such systems are presented as convex optimization problems, with exponential mean square stability guarantees. A Monte-Carlo approach for analysis and synthesis is also presented, which is used to benchmark the polynomial chaos based approach. The computational advantage of the polynomial chaos approach is shown with an example based on the design of an optimal EMS-stabilizing controller, for an F-16 aircraft model.