IMPLEMENTATION MODEL PREDICTIVE CONTROL (MPC) WITH RASPBERRY PI 3 AS A POSITION CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR (PMSM)
This research presents the design of Predictive Control Model (MPC) for position control of Permanent Magnet Synchronous Motor (PMSM). As a Linear Quadratic <br /> <br /> Regulator, MPC aims to design and produce optimum control signals with anticipated control signal saturation and so...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/25392 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | This research presents the design of Predictive Control Model (MPC) for position control of Permanent Magnet Synchronous Motor (PMSM). As a Linear Quadratic <br />
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Regulator, MPC aims to design and produce optimum control signals with anticipated control signal saturation and some future prediction steps. System design is divided into two parts, namely hardware and software. The hardware consists of Raspberry Pi 3 connected via MODBUS with PMSM controlled by the ASDA-A2 driver. Software designs include MATLAB and Python that are included on the Raspbian OS. In the design of MPC techniques to adjust the speed of PMSM to take position tracking action. MPC computing is done inside <br />
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Raspberry, and the results are sent to the ASDA driver in speed mode. The performance index optimization is done by adjusting the value of N horizons, weight Q and weight R. The results show that PMSM can reach desired setpoint. <br />
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Comparison of system responses modeled by the order 1 and order 2 approaches with 1 zero at the velocity yield an identical response. Both MPC control approaches result in smaller error rates and faster in achieving settling time (± 2% steady state) compared to Auto Gain PI manufacturer control, and than MPC computation time is not exceeding 0.03s sampling time.. |
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