Robust nonlinear control of vectored thrust aircraft

An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations

Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: A survey

Copyright © 2013 Jun Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Some recent advances on the recursive filtering and sliding mode design problems for nonlinear stochastic systems with network-induced phenomena are surveyed. The network-induced phenomena under consideration mainly include missing measurements, fading measurements, signal quantization, probabilistic sensor delays, sensor saturations, randomly occurring nonlinearities, and randomly occurring uncertainties. With respect to these network-induced phenomena, the developments on filtering and sliding mode design problems are systematically reviewed. In particular, concerning the network-induced phenomena, some recent results on the recursive filtering for time-varying nonlinear stochastic systems and sliding mode design for time-invariant nonlinear stochastic systems are given, respectively. Finally, conclusions are proposed and some potential future research works are pointed out.This work was supported in part by the National Natural Science Foundation of China under Grant nos. 61134009, 61329301, 61333012, 61374127 and 11301118, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant no. GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Risk Management in Action. Robust monetary policy rules under structured uncertainty.

Recent interest in ‘Risk Management’ has highlighted the relevance of Bayesian analysis for robust monetary- policy making. This paper sets out a comprehensive methodology for designing policy rules inspired by such considerations. We design rules that are robust with respect to model uncertainty facing both the policy-maker and private sector. We apply our methodology to three simple interest-rate rules: inflation-forecast- based (IFB) rules with a discrete forward horizon, one targeting a discounted sum of forward inflation, and a current wage inflation rule. We use an estimated DSGE model of the euro area and estimated measures of structured exogenous and parameter uncertainty for the exercise. We find that IFB rules with a long horizon perform poorly with or without robust design. Our discounted future targeting rule performs much better, indicating that policy can be highly forward-looking without compromising stabilization. The wage inflation rule dominates whether it is designed to have good robust properties or not. JEL Classification: E52, E37, E58Interest-rate rules, Robustness, structured uncertainty

Robust Monetary Policy under Model Uncertainty and Inflation Persistence.

This paper examines the relationship between the preference for ro- bustness of central bank (when it fears that its model is misspecified), the inflation persistence and the output cost of disinflation. Using a simple monetary game model in which higher preference for robustness of central bank is positively associated with the inflation persistence and thus nega- tively with the speed of disinflation, this paper shows that the output cost of disinflation is higher when the less the central bank believes that its reference model is robust.Model uncertainty, Robust control, Minmax policies, Inflation persistence, Sacrifice ratio.

Control System Analysis and Synthesis via Linear Matrix Inequalities

A wide variety of problems in systems and control theory can be cast or recast as convex problems that involve linear matrix inequalities (LMIs). For a few very special cases there are "analytical solutions" to these problems, but in general they can be solved numerically very efficiently. In many cases the inequalities have the form of simultaneous Lyapunov or algebraic Riccati inequalities; such problems can be solved in a time that is comparable to the time required to solve the same number of Lyapunov or Algebraic Riccati equations. Therefore the computational cost of extending current control theory that is based on the solution of algebraic Riccati equations to a theory based on the solution of (multiple, simultaneous) Lyapunov or Riccati inequalities is modest. Examples include: multicriterion LQG, synthesis of linear state feedback for multiple or nonlinear plants ("multi-model control"), optimal transfer matrix realization, norm scaling, synthesis of multipliers for Popov-like analysis of systems with unknown gains, and many others. Full details can be found in the references cited

Robust control for independently rotating wheelsets on a railway vehicle using practical sensors

This paper presents the development of H-infinity control strategy for the active steering of railway vehicles with independently rotating wheelsets. The primary objective of the active steering is to stabilize the wheelset and to provide a guidance control. Some fundamental problems for active steering are addressed in the study. The developed controller is able to maintain stability and good performance when parameter variations occur, in particular at the wheel-rail interface. The control is also robust against structured uncertainties that are not included in the model such as actuator dynamics. Furthermore the control design is formulated to use only practical sensors of inertial and speed measurements, as some basic measurements required for active steering such as wheel-rail lateral displacement cannot be easily and economically measured in practice

Risk Hedging Strategies under Energy System and Climate Policy Uncertainties

The future development of the energy sector is rife with uncertainties. They concern virtually the entire energy chain, from resource extraction to conversion technologies, energy demand, and the stringency of future environmental policies. Investment decisions today need thus not only to be cost-effective from the present perspective, but have to take into account also the imputed future risks of above uncertainties. This paper introduces a newly developed modeling decision framework with endogenous representation of above uncertainties. We employ stochastic modeling techniques within a system engineering model of the global energy system and implement several alternative representations of risk. We aim to identify salient characteristics of least-cost risk hedging strategies that are adapted to considerably reduce future risks and are hence robust against a wide range of future uncertainties. These lead to significant changes in response to energy system and carbon price uncertainties, in particular, (i) higher short- to medium-term investments into advanced technologies, (ii) pronounced emissions reductions, and (iii) diversification of the technology portfolio. From a methodological perspective, we find that there are strong interactions and synergies between different types of uncertainties. Cost-effective risk hedging strategies thus need to take a holistic view and comprehensively account for all uncertainties jointly. With respect to costs, relatively modest risk premiums (or hedging investments) can significantly reduce the vulnerability of the energy system against the associated uncertainties. The extent of early investments, diversification and emissions reductions, however, depends on the risk premium that decision makers are willing to pay to respond to prevailing uncertainties, and remains thus one of the key policy variables