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<p>Parallel robots have advantages such as high speed and accuracy that have made them very useful in the industry. In this paper, the type-2 fuzzy and Model Predictive Control (MPC) are used for the parallel delta robots. Their precise control in following the path and not encountering obstacles is one of the challenging issues. This paper introduces a new MPC method based on nonlinear model, as the main controller, and with compensation by type-2 fuzzy system for a parallel robot fuzzy type-2 system can play a role as a main controller supplement in two places, one as a complement to the control signal and the other for estimating the control coefficients. The existence of types of uncertainty such as variable loads, variable obstacles, highly nonlinear dynamics of the system, etc. makes the mathematical model of the system not fixed, and therefore, the model-based controller alone is not responsive. Simulation with several different scenarios shows the efficiency of the proposed control system.</p>

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Advanced fuzzy model predictive control for precision trajectory tracking in parallel robots

  • 期刊论文

  • 发表日期：

    2026年3月7日

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  • Muneera Altayeb,
  • Laith S. Ismail,
  • Raed Alfilh,
  • B. Spoorthi,
  • Satish Kumar Samal,
  • A. Rameshbabu,
  • Gaganjot Kaur,
  • Abhayveer Singh,
  • Abdolreza Khorasani

摘要

Parallel robots have advantages such as high speed and accuracy that have made them very useful in the industry. In this paper, the type-2 fuzzy and Model Predictive Control (MPC) are used for the parallel delta robots. Their precise control in following the path and not encountering obstacles is one of the challenging issues. This paper introduces a new MPC method based on nonlinear model, as the main controller, and with compensation by type-2 fuzzy system for a parallel robot fuzzy type-2 system can play a role as a main controller supplement in two places, one as a complement to the control signal and the other for estimating the control coefficients. The existence of types of uncertainty such as variable loads, variable obstacles, highly nonlinear dynamics of the system, etc. makes the mathematical model of the system not fixed, and therefore, the model-based controller alone is not responsive. Simulation with several different scenarios shows the efficiency of the proposed control system.