Optimization of Model Predictive Control Weights for Control of Permanent Magnet Synchronous Motor by Using the Multi Objective Bees Algorithm

Abstract

In this study, the model predictive control (MPC) method was used within the scope of the control of the permanent magnet synchronous motor (PMSM). The strongest aspect of the MPC, the ability to control multiple components with a single function, is also one of the most difficult parts of its design. The fact that each component of the function has different effects requires assigning different weight coefficients to these components. In this study, the Bees Algorithm (BA) is used to determine the weights. Using the multi-objective function in BA, it has been tried to determine the weights that reduce the current values together with the speed error. Three different PI controllers have been designed to compare the MPC method. The coefficients of one of these are tuned with BA. Good Gain Method and Tyreus-Luyben Method were used in the other two. As a result of experimental studies, it has been observed that MPC can control PMSM more smoothly and accurately than PI controllers, with weights optimized with BA. With MPC, PMSM has been controlled with 15% settling time than other controllers and also with no overshoot.

Position Control of External Rotor Permanent Magnet Synchronous Motor with Model Predictive Control

“…Since the electrical characteristic of PMSM is much faster than the mechanical characteristic, there is no velocity dependent expression in Eq. (11).…”

“…Since position data is needed as feedback in position control of PMSMs, sensorless control methods cannot be applied in these applications. In such applications, the control methods preferred recently are intelligent control methods or model-based control methods [8][9][10][11][12].…”

Recent Achievements in the Control of Interior Permanent-Magnet Synchronous Machine Drives: A Comprehensive Overview of the State of the Art

“…Cost functions with more than two features, or in other words multi-objective cost functions, pose a more complex design issue. The bees algorithm, which is a population-based search algorithm, is used to obtain the optimal value of weighting factors in [169]. An artificial neural network for the selection of weighting factors for an MPTC scheme is introduced in [170].…”