Abstract:
This paper proposes a novel optimal control law to improve control performance in an average sense and to guarantee robust stability for discrete-time linear systems with...Show MoreMetadata
Abstract:
This paper proposes a novel optimal control law to improve control performance in an average sense and to guarantee robust stability for discrete-time linear systems with time-invariant stochastic parameters. The time-invariant stochastic parameters cause difficulties in the optimal control problem: the principle of optimality does not hold, and a cost function includes the high-order moments of the parameters which are hard to be computed. The class of linear feedback controllers is focused on to simplify the problem so that it becomes solvable form. To optimize a feedback gain not using the principle of optimality, this paper derives a relation between the objective cost function and the gain. The gradient of the objective function is derived analytically without direct calculation of the high-order moments, which allows us to apply gradient-based optimization methods to the optimal control problem. Robust stability is exactly guaranteed even in the proposed stochastic optimization approach by employing a barrier function ensuring quadratic stability. A numerical simulation demonstrates the control performance and the stability of the proposed method.
Published in: 2016 IEEE 55th Conference on Decision and Control (CDC)
Date of Conference: 12-14 December 2016
Date Added to IEEE Xplore: 29 December 2016
ISBN Information: