4.8 Article

Designing Robust Control for Mechanical Systems: Constraint Following and Multivariable Optimization

期刊

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 16, 期 8, 页码 5267-5275

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2951842

关键词

Optimization; Mechanical systems; Robust control; Uncertainty; Force; Performance analysis; System performance; Constraint following; mechanical systems; multivariable optimization; robust control; uncertainty

资金

  1. Natural Science Foundation of China [51805263]
  2. Provincial Natural Science Foundation of Jiangsu [BK20180474]
  3. Fundamental Research Funds for the Central Universities [309181B8811, 300102258305]
  4. Graduate Student Scientific Research Innovation Projects of Jiangsu Province [SJKY19_0289, TII-19-4632]

向作者/读者索取更多资源

This article proposes a novel robust control design for mechanical systems based on constraint following and multivariable optimization. The state of the concerned system is affected by (possibly fast) time-varying and bounded uncertainty. The objective is to drive the system to obey a set of prescribed constraints. A beta-measure is defined to gauge the constraint-following error; based on which, a feedback robust control scheme, which invokes design parameters, is proposed. For the seeking of optimal design parameters, a multivariable constrained optimization problem is formulated. The problem is successfully solved: with the existence, uniqueness, and analytical expression (i.e., closed form) of the optimal design parameters demonstrated. With the optimal parameters, the proposed robust control can render dual performance: guaranteed and optimal. As the guaranteed performance, the beta-measure is assured to be uniform boundedness and uniform ultimate boundedness. As the optimal performance, the performance index is globally minimized. This article is the first ever endeavour to cast both the constraint following and multivariable optimization into the control framework for uncertain mechanical systems.

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