Structured Control of LPV Systems with Application to Wind Turbines

Abstract — This paper deals with structured control of linear parameter varying systems (LPV) with application to wind turbines. Instead of attempting to reduce the problem to linear matrix inequalities (LMI), we propose to design the controllers via an LMI-based iterative algorithm. The proposed algorithm can synthesize structured controllers like decentralized, static output and reduced order output feedback for discrete-time LPV systems. Based on a coordinate decent, it relies on a sufficient matrix inequality condition extended with slack variables to an upper bound on the induced L2-norm of the closed-loop system. Algorithms for the computation of feasible as well as optimal controllers are presented. The general case where no restrictions are imposed on the parameter dependence is treated here due to its suitability for modeling wind turbines. A comprehensive numerical example of a gain-scheduled LPV controller design with prescribed pattern for wind turbines illustrate the utilization of the proposed algorithm. I

Macroscopic modelling and robust control of bi-modal multi-region urban road networks

The paper concerns the integration of a bi-modal Macroscopic Fundamental Diagram (MFD) modelling for mixed traffic in a robust control framework for congested single- and multi-region urban networks. The bi-modal MFD relates the accumulation of cars and buses and the outflow (or circulating flow) in homogeneous (both in the spatial distribution of congestion and the spatial mode mixture) bi-modal traffic networks. We introduce the composition of traffic in the network as a parameter that affects the shape of the bi-modal MFD. A linear parameter varying model with uncertain parameter the vehicle composition approximates the original nonlinear system of aggregated dynamics when it is near the equilibrium point for single- and multi-region cities governed by bi-modal MFDs. This model aims at designing a robust perimeter and boundary flow controller for single- and multi-region networks that guarantees robust regulation and stability, and thus smooth and efficient operations, given that vehicle composition is a slow time-varying parameter. The control gain of the robust controller is calculated off-line using convex optimisation. To evaluate the proposed scheme, an extensive simulation-based study for single- and multi-region networks is carried out. To this end, the heterogeneous network of San Francisco where buses and cars share the same infrastructure is partitioned into two homogeneous regions with different modes of composition. The proposed robust control is compared with an optimised pre-timed signal plan and a single-region perimeter control strategy. Results show that the proposed robust control can significantly: (i) reduce the overall congestion in the network; (ii) improve the traffic performance of buses in terms of travel delays and schedule reliability, and; (iii) avoid queues and gridlocks on critical paths of the network