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Direct Discrete-Time Design for Sampled-Data Hamiltonian Control Systems

  • Dina Shona Laila
  • Alessandro Astolfi
Conference paper
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 366)

Abstract

The success in a model-based direct discrete-time design for nonlinear sampled-data control systems depends on the availability of a good discrete-time plant model to use for the design. Unfortunately, even if the continuous-time model of a plant is known, we cannot in general compute the exact discrete-time model of the plant, since it requires computing an explicit analytic solution of a nonlinear differential equation. One way to solve the problem of finding a good model is by using an approximate model of the plant. A general framework for stabilization of sampled-data nonlinear systems via their approximate discrete-time models was presented in [11]. It is suggested that approximate discrete-time models can be obtained using various numerical algorithms, such as Runge-Kutta and multistep methods. Yet, to the best of the authors knowledge, almost all available results on this direction view the systems as dissipative systems, whereas for design purpose, there are many systems that are better modeled as Hamiltonian conservative systems.

Keywords

Hamiltonian System Euler Model Discrete Gradient Explicit Analytic Solution Underactuated Mechanical System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Dina Shona Laila
    • 1
  • Alessandro Astolfi
    • 2
    • 3
  1. 1.Institute for Design and Control of Mechatronical SystemsJohannes Kepler UniversityLinzAustria
  2. 2.Electrical&Electronic Engineering DepartmentImperial CollegeLondonUK
  3. 3.Dipartimento di Informatica, Sistemi e ProduzioneUniversity of RomeRomeItaly

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