Laleh Ravanbod

An extension of the linear quadratic gaussian-loop transfer recovery procedure

The linear quadratic Gaussian-loop transfer recovery procedure is a classical method to desensibilise a system in closed loop with respect to disturbances and system uncertainty. Here an extension is discussed, which avoids the usual loss of performance in LTR, and which is also applicable for non-minimum phase systems. It is also shown how the idea can be extended to other control structures. In particular, it is shown how proportional integral derivative controllers can be desensibilised with this new approach. The method is tested on several examples, including in particular the lateral flight control of an F-16 aircraft.

Gain-Scheduled Two-Loop Autopilot for an Aircraft

We present a new method to compute output gain-scheduled controllers for nonlinear systems. We use structured H∞-control to precompute an optimal controller parametrization as a reference. We then propose three practical methods to implement a control law which has only an acceptable loss of performance with regard to the optimal reference law. Our method is demonstrated in longitudinal flight control, where the dynamics of the aircraft depend on the operational conditions velocity and altitude. We design a structured controller consisting of a PI-block to control vertical acceleration, and another I-block to control the pitch rate.

Branch and bound algorithm with applications to robust stability

We discuss a branch and bound algorithm for global optimization of NP-hard problems related to robust stability. This includes computing the distance to instability of a system with uncertain parameters, computing the minimum stability degree of a system over a given set of uncertain parameters, and computing the worst case

Robustified H2-control of a system with large state dimension

H_\infty

Computing the structured distance to instability.

H∞ norm over a given parameter range. The success of our method hinges (1) on the use of an efficient local optimization technique to compute lower bounds fast and reliably, (2) a method with reduced conservatism to compute upper bounds, and (3) the way these elements are favorably combined in the algorithm.

Branch and bound algorithm for the robustness analysis of uncertain systems

We use a non-smooth trust-region method for H∞-control of infinite-dimensional systems. Our method applies in particular to distributed and boundary control of partial differential equations. It is computationally attractive as it avoids the use of system reduction or identification. For illustration the method is applied to control a reaction-convection-diffusion system, a Van de Vusse reactor, and to a cavity flow control problem.

Determination of Set-Membership Identifiability Sets

Abstract We consider the design of an output feedback controller for a large scale system like the linearized Navier–Stokes equation. We design an observer-based controller for a reduced system that achieves a compromise between concurring performance and robustness specifications. This controller is then pulled back to the large scale system such that closed-loop stability is preserved, and such that the trade-off between the H2- and H ∞ -criteria achieved in reduced space is preserved. The procedure is tested on a simulated fluid flow study.