Position Control of A Permanent Magnet Brushless DC Motor using A Model Predictive Control Method with Laguerre Functions
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Abstract
This article focuses on reducing the high computational load in the MPC method with long prediction horizons, by using orthogonal basis functions (Laguerre functions) for controlling the position of a BLDC motor. MPC is one of the controllers that can be used to achieve the desired and optimal dynamics of the BLDC motor. One of the main challenges in the practical implementation of MPC, which limits its application, is the computational volume, especially with high prediction horizons. In fast systems and systems with complex dynamics and small sampling times, the computational load increases exponentially, which leads to a decrease in real-time execution speed and requires shorter prediction horizons. This high computational load can be approximated using discrete orthogonal basis functions, such as Laguerre polynomials. The main advantage of this approach is the optimization of coefficients with fewer Laguerre functions instead of optimizing the control trajectory itself, making it possible to choose a longer prediction horizon. Simulation and implementation result clearly demonstrate that the Laguerre-based MPC method benefits from reduced computation time, indicating a significant reduction in computational load.
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