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This chapter presents a number of simple methods for accelerating the convergence of the Norm-Optimal Iterative Learning control (NOILC) algorithm as measured by the number of online experiments required to achieve a desired tracking accuracy. The approaches require algorithm modifications and/or additional offline, model-based calculations but have discernible beneficial effects although some reduction in robustness can be anticipated. The notation and models assumed are precisely those used in the NOILC Chap. 5. A familiarity with the ideas, techniques, and examples used in that chapter will be of great value to the reader.

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Accelerating NOILC Convergence

  • 章节

  • 发表日期：

    2025年

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  • Bing Chu,
  • David H. Owens

摘要

This chapter presents a number of simple methods for accelerating the convergence of the Norm-Optimal Iterative Learning control (NOILC) algorithm as measured by the number of online experiments required to achieve a desired tracking accuracy. The approaches require algorithm modifications and/or additional offline, model-based calculations but have discernible beneficial effects although some reduction in robustness can be anticipated. The notation and models assumed are precisely those used in the NOILC Chap. 5. A familiarity with the ideas, techniques, and examples used in that chapter will be of great value to the reader.