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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Embedded Model Control: outline of the theory.

Enrico Canuto1

  • 1Politecnico di Torino, Dipartimento di Automatica e Informatica, Corso Duca degli Abruzzi 24, 10129 Torino, Italy. enrico.canuto@polito.it

ISA Transactions
|April 17, 2007
PubMed
Summary

Embedded Model Control systematically designs control algorithms by defining controllable dynamics, disturbances, and neglected dynamics. The

Area of Science:

  • Control Engineering
  • System Dynamics
  • Model-Based Design

Background:

  • Systematic design of control algorithms requires a structured approach.
  • Embedded Models (EM) are central to control design, integrating plant dynamics and requirements.
  • Distinguishing between noise and system dynamics is crucial for effective control.

Purpose of the Study:

  • To present a systematic methodology for Embedded Model Control design.
  • To define the components of an Embedded Model: controllable dynamics, disturbance class, and neglected dynamics.
  • To introduce the 'error loop' concept for discriminating error sources and ensuring stability.

Main Methods:

  • Defining controllable and disturbance dynamics observable from plant measurements.

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  • Designing control algorithms based on controllable and disturbance dynamics.
  • Utilizing the 'error loop' concept to map error sources and identify destabilizing factors.
  • Analyzing neglected dynamics to ensure stability and performance constraints.
  • Main Results:

    • A systematic procedure for developing Embedded Models (EM) from plant dynamics and control requirements.
    • The 'error loop' concept effectively maps error sources to performance and aids in discriminating destabilizing dynamics.
    • Analytical and simulated results demonstrate the practical application of the proposed design steps.

    Conclusions:

    • Embedded Model Control provides a systematic framework for control system design.
    • The 'error loop' is a key tool for ensuring stability and performance by managing uncertainties and disturbances.
    • This approach facilitates the development of robust control algorithms through a clear definition of model components.