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Related Experiment Videos

Direct descent curvature optimal control with a modification algorithm and its application to nonlinear process.

Yasar Becerikli1, Ahmet Ferit Konar

  • 1Department of Computer Engineering, Kocaeli University, Izmit, Turkey. becer@kou.edu.tr

ISA Transactions
|February 17, 2006
PubMed
Summary

This study introduces a modified direct descent second-order algorithm for optimal control, offering robust solutions and generating time-varying optimal feedback gains for enhanced system stability.

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Area of Science:

  • Control Engineering
  • Applied Mathematics
  • Computational Science

Background:

  • Optimal control problems are crucial in various engineering disciplines.
  • Existing Hamiltonian methods can face challenges with numerical robustness.
  • Developing efficient and stable algorithms for optimal control is an ongoing research area.

Purpose of the Study:

  • To present a modified direct descent second-order algorithm for optimal control computations.
  • To compare the proposed algorithm's performance against established Hamiltonian methods.
  • To demonstrate the algorithm's robustness and applicability to complex systems.

Main Methods:

  • Implementation of a direct descent second-order algorithm with modifications.
  • Development of a weighting matrix updating scheme for performance enhancement.

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  • Comparative analysis with existing Hamiltonian-based optimal control techniques.

    • Main Results:

    • The proposed algorithm yields numerically robust solutions, particularly concerning conjugate points.
    • The algorithm demonstrates effective performance on benchmark and industrial processes.
    • Time-varying optimal feedback (TVOFB) gains are generated as byproducts, enabling trajectory correction.

    Conclusions:

    • The modified direct descent second-order algorithm offers a robust and effective approach to optimal control.
    • The algorithm's ability to generate TVOFB gains enhances system resilience to disturbances and parameter variations.
    • The method is validated through simulations on nonlinear systems like the Van der Pol oscillator and bioreactors.