Abstract

1. Stability Certificates for Networks of Heterogeneous Linear Systems by Richard Pates

Abstract: We derive robust, decentralized and scalable stability conditions for networks of heterogeneous linear systems. The results are based on a graphical Nyquist type test which depends only on the frequency responses of an individual system and its nearest neighbours. If every system in the network satisfies such a condition, the network is guaranteed to be stable. The results are suitable for a broad class of networks, allowing arbitrary interconnection asymmetries and heterogeneous multi-input-multi-output systems.

2. Robust Network Reconstruction in Polynomial Time by David Hayden

Abstract: We present an efficient algorithm for robust network reconstruction of linear time-invariant systems in the presence of noise, estimation errors and unmodelled nonlinearities. The method here builds on previous work on robust reconstruction to provide a practical implementation with polynomial computational complexity. The algorithm greedily obtains a set of structurally-related candidate solutions spanning every level of sparsity. One of these structures is guaranteed to be the correct solution if a magnitude bound on the noise is satisfied. Model selection tools may then be used to select a single solution from this set. Extensive simulations quantify the expected performance for different levels of noise and show that significantly more noise can be tolerated in comparison to the original method.

3. LQ optimal and risk-sensitive control for vehicle suspensions by Panos Brezas

Abstract: The talk is concerned with time-domain optimal control of active suspensions. The optimal control problem formulation has been generalised by incorporating both road disturbances (ride quality) and a representation of driver inputs (handling quality) into the optimal control formulation. A regular optimal control problem as well as a risk-sensitive exponential optimal control performance index is considered. Emphasis has been given to practical considerations including the issue of state estimation in the presence of load disturbances (driver inputs).