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Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane

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  • Xuejuan Shao
  • Jinggang Zhang
  • Xueliang Zhang

Abstract

The dynamic model of overhead crane is highly nonlinear and uncertain. In this paper, Takagi-Sugeno (T-S) fuzzy modeling and PSO-based robust linear quadratic regulator (LQR) are proposed for anti-swing and positioning control of the system. First, on the basis of sector nonlinear theory, the two T-S fuzzy models are established by using the virtual control variables and approximate method. Then, considering the uncertainty of the model, robust LQR controllers with parallel distributed compensation (PDC) structure are designed. The feedback gain matrices are obtained by transforming the stability and robustness of the system into linear matrix inequalities (LMIs) problem. In addition, particle swarm optimization (PSO) algorithm is used to overcome the blindness of LQR weight matrix selection in the design process. The proposed control methods are simple, feasible, and robust. Finally, the numeral simulations are carried out to prove the effectiveness of the methods.

Suggested Citation

  • Xuejuan Shao & Jinggang Zhang & Xueliang Zhang, 2019. "Takagi-Sugeno Fuzzy Modeling and PSO-Based Robust LQR Anti-Swing Control for Overhead Crane," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-14, April.
    • Handle: RePEc:hin:jnlmpe:4596782
    DOI: 10.1155/2019/4596782
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    Cited by:

    1. Alyazidi, Nezar M. & Hassanine, Abdalrahman M. & Mahmoud, Magdi S., 2023. "An Online Adaptive Policy Iteration-Based Reinforcement Learning for a Class of a Nonlinear 3D Overhead Crane," Applied Mathematics and Computation, Elsevier, vol. 447(C).

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