Switching and Learning in Feedback Systems

1. Front Matter

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2. Switching and Control

   1. From Feedback Control to Complexity Management: A Personal Perspective

      • Kumpati S. Narendra

      Pages 1-30

   2. Convex Cones, Lyapunov Functions, and the Stability of Switched Linear Systems

      • Robert Shorten, Oliver Mason, Kai Wulff

      Pages 31-46

   3. Survey of Explicit Approaches to Constrained Optimal Control

      • Alexandra Grancharova, Tor Arne Johansen

      Pages 47-97

3. Gaussian Processes

   1. Analysis of Some Methods for Reduced Rank Gaussian Process Regression

      • Joaquin Quiñonero-Candela, Carl Edward Rasmussen

      Pages 98-127

   2. Filtered Gaussian Processes for Learning with Large Data-Sets

      • Jian Qing Shi, Roderick Murray-Smith, D. Mike Titterington, Barak A. Pearlmutter

      Pages 128-139

   3. Self-tuning Control of Non-linear Systems Using Gaussian Process Prior Models

      • Daniel Sbarbaro, Roderick Murray-Smith

      Pages 140-157

   4. Gaussian Processes: Prediction at a Noisy Input and Application to Iterative Multiple-Step Ahead Forecasting of Time-Series

      • Agathe Girard, Roderick Murray-Smith

      Pages 158-184

   5. Nonlinear Predictive Control with a Gaussian Process Model

      • Juš Kocijan, Roderick Murray-Smith

      Pages 185-200

4. Applications of Switching & Learning

   1. Control of Yaw Rate and Sideslip in 4-Wheel Steering Cars with Actuator Constraints

      • Miguel A. Vilaplana, Oliver Mason, Douglas J. Leith, William E. Leithead

      Pages 201-222

   2. A Second-Order Cone Bounding Algorithm for Robust Minimum Variance Beamforming

      • Ngai Wong, Venkataramanan Balakrishnan, Tung-Sang Ng

      Pages 223-247

   3. Joint Optimization of Wireless Communication and Networked Control Systems

      • Lin Xiao, Mikael Johansson, Haitham Hindi, Stephen Boyd, Andrea Goldsmith

      Pages 248-272

   4. Reconciliation of Inconsistencies in the Theory of Linear Systems

      • Emanuele Ragnoli, William Leithead

      Pages 273-289

   5. An Introduction to Nonparametric Hierarchical Bayesian Modelling with a Focus on Multi-agent Learning

      • Volker Tresp, Kai Yu

      Pages 290-312

   6. Simultaneous Localization and Surveying with Multiple Agents

      • Sam T. Roweis, Ruslan R. Salakhutdinov

      Pages 313-332

   7. Hex: Dynamics and Probabilistic Text Entry

      • John Williamson, Roderick Murray-Smith

      Pages 333-342

5. Back Matter

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A central theme in the study of dynamic systems is the modelling and control of uncertain systems. While ‘uncertainty’ has long been a strong motivating factor behind many techniques developed in the modelling, control, statistics and mathematics communities, the past decade, in particular, has witnessed remarkable progress in this area with the emergence of a number of powerful newmethodsforbothmodellingandcontrollinguncertaindynamicsystems. The speci?c objective of this book is to describe and review some of these exciting new approaches within a single volume. Our approach was to invite some of the leading researchers in this area to contribute to this book by submitting both tutorial papers on their speci?c area of research, and to submit more focussed research papers to document some of the latest results in the area. We feel that collecting some of the main results together in this manner is particularly important as many of the important ideas that emerged in the past decade were derived in a variety of academic disciplines. By providing both tutorial and researchpaperswehopetobeabletoprovidetheinterestedreaderwithsu?cient background to appreciate some of the main concepts from a variety of related, but nevertheless distinct ?elds, and to provide a ?avor of how these results are currently being used to cope with ‘uncertainty. ’ It is our sincere hope that the availability of these results within a single volume will lead to further cro- fertilization of ideas and act as a spark for further research in this important area of applied mathematics.

• Monte Carlo Simulation
• Non-Para
• algorithmic learning
• algorithms
• calculus
• complexity
• dynamic systems
• learning
• machine learning
• modeling
• multi-agent systems
• networked control
• optimization
• switching theory
• uncertainty

• Hamilton Institute, NUI Maynooth, Ireland

  Roderick Murray-Smith

• Hamilton Institute, NUIM, Ireland

  Robert Shorten

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