4.7 Article

Stability and Fast Transient Performance Oriented Motion Control of a Direct-Drive System With Modeling Uncertainties, Velocity, and Input Constraints

Journal

IEEE-ASME TRANSACTIONS ON MECHATRONICS
Volume 27, Issue 6, Pages 5926-5935

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2022.3191201

Keywords

Linear motor; precision motion control; saturated adaptive robust control (SARC)

Funding

  1. National Natural Science Foundation of China [62003176]
  2. Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems [GZKF-201919]
  3. China Postdoctoral Science Foundation [2020M670628]
  4. Fundamental Research Funds for the China Central Universities of Nankai University [63211114]
  5. Tianjin Science Fund for Distinguished Young Scholars [19JCJQJC62100]

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This article proposes an online time-optimal trajectory optimization-based saturated adaptive robust control algorithm to guarantee the stability and fast transient performance of a direct-drive system subject to modeling uncertainties, velocity, and input constraints. The proposed control approach consists of two levels, with a saturated adaptive robust controller (SARC) handling modeling uncertainties and feedback constraint, and an online time-optimal trajectory optimization algorithm dealing with feedforward and velocity constraints. Comparative experiments on a linear motor demonstrate the theoretical results and superiorities of the proposed two-level control approach.
Guaranteeing the stability and fast transient performance of a direct-drive system simultaneously, which is practically subject to various modeling uncertainties, velocity, and input constraints, is both theoretically and technically challenging. To this end, an online time-optimal trajectory optimization-based saturated adaptive robust control algorithm is proposed in this article. The structure of the proposed control approach consists of two levels. In the lower level, a saturated adaptive robust controller (SARC) is synthesized. Meanwhile, an online time-optimal trajectory optimization algorithm is integrated in the upper level. Typically, the input constraint is divided into the feedforward constraint and feedback constraint, respectively. In this way, the modeling uncertainties and feedback constraint are handled by the SARC in the lower level, while the online time-optimal trajectory optimization algorithm in the upper level deals with the feedforward and velocity constraints. The stability analysis of the overall two-level control framework is presented and a minimum-time transient response is also theoretically guaranteed. Comparative experiments on a linear motor justify the theoretical results and superiorities of the proposed two-level control approach.

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