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Stochastic Event-Based Control and Estimation

Författare:
  • Toivo Henningsson
Publiceringsår: 2012
Språk: Engelska
Sidor: 183
Dokumenttyp: Doktorsavhandling

Sammanfattning

Digital controllers are traditionally implemented using periodic sampling, computation, and actuation events. As more control systems are implemented to share limited network and CPU bandwidth with other tasks, it is becoming increasingly attractive to use some form of event-based control instead, where precious events are used only when needed.

Forms of event-based control have been used in practice for a very long time, but mostly in an ad-hoc way. Though optimal solutions to most event-based control problems are unknown, it should still be viable to compare performance between suggested approaches in a reasonable manner.

This thesis investigates an event-based variation on the stochastic linear-quadratic (LQ) control problem, with a fixed cost per control event. The sporadic constraint of an enforced minimum inter-event time is introduced, yielding a mixed continuous-/discrete-time formulation. The quantitative trade-off between event rate and control performance is compared between periodic and sporadic control. Example problems for first-order plants are investigated, for a single control loop and for multiple loops closed over a shared medium.

Path constraints are introduced to model and analyze higher-order event-based control systems. This component-based approach to stochastic hybrid systems allows to express continuous- and discrete-time dynamics, state and switching constraints, control laws, and stochastic disturbances in the same model. Sum-of-squares techniques are then used to find bounds on control objectives using convex semidefinite programming.

The thesis also considers state estimation for discrete time linear stochastic systems from measurements with convex set uncertainty. The Bayesian observer is considered given log-concave process disturbances and measurement likelihoods. Strong log-concavity is introduced, and it is shown that the observer preserves log-concavity, and propagates strong log-concavity like inverse covariance in a Kalman filter. A recursive state estimator is developed for systems with both stochastic and set-bounded process and measurement noise terms. A time-varying linear filter gain is optimized using convex semidefinite programming and ellipsoidal over-approximation, given a relative weight on the two kinds of error.

Disputation

2012-12-18
10:15
Lecture hall M:B, M-building, Ole Römers väg 1, Lund University Faculty of Engineering
  • Joao Pedro Hespanha (Professor)

Nyckelord

  • Technology and Engineering
  • event-based control
  • event-based estimation
  • sporadic control
  • stochastic control
  • stochastic hybrid systems
  • sum-of-squares methods
  • control over networks
  • quantized measurements

Övriga

In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Lund University's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
  • Anton Cervin (PhD)
  • ISSN: 0280-5316
  • ISBN: 978-91-7473-410-2
  • ISRN LUTFD2/TFRT--1095--SE

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