Abstract
This paper describes implementation of the Generalized Predictive Control (GPC) algorithm on an STM32 microcontroller with the ARM Cortex M7 core. The algorithm is implemented in its analytical (explicit) version which requires computationally simple matrix and vector operations in real time, no on-line optimisation is necessary. As a result, the algorithm may be used for controlling very fast dynamic processes characterised by sampling periods of millisecond order. Results of real experiments are demonstrated for two example processes.
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References
1. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (1999)
2. Chaber, P., Ławryńczuk, M.: Effectiveness of PID and DMC control algorithms automatic code generation for microcontrollers: application to a thermal process. Proceedings of the 3rd Conference Control and Fault-Tolerant Systems, SysTol 2016, pp. 618–623, Barcelona, Spain (2016)
3. Chaber, P., Ławryńczuk, M.: Auto-generation of advanced control algorithms’ code for microcontrollers using transcompiler. Proceedings of the 21th IEEE International Conference on Methods and Models in Automation and Robotics, MMAR 2016, pp. 454–459, Miedzyzdroje, Poland (2016)
4. Clarke, D.W., Mohtadi, C., Tuffs, P.S.: Generalized predictive control. Automatica 23 137–160 (1987)
5. Houska, B., Ferreau, H.J., Diehl, M.: An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range. Automatica 60 2279–2285 (2011).
6. Kufoalor, D.K.M., Aaker, V., Johansen, T.A., Imsland, L., Eikrem, G.O.: Automatically generated embedded model predictive control: Moving an industrial PC-based MPC to an embedded platform. Optimal Control Applications and Methods 36, 705–727 (2015)
7. Kunz, K., Huck, S.M., Summers, T.H.: Fast model predictive control of miniature helicopters. Proceedings of the European Control Conference, ECC 2013, pp. 1377–1382, Zurich, Switzerland (2013)
8. Kvasnica, M., Rauová I., Fikar M.: Automatic code generation for real-time implementation of model predictive control. Proceedings of the IEEE International Symposium on Computer-Aided Control System Design, 2010 IEEE Multi-Conference on Systems and Control, pp. 993–998, Yokohama, Japan (2010)
9. Liniger L., Domahidi A., Morari M.: Optimization-based autonomous racing of 1:43 scale RC cars. Optimal Control Applications and Methods 36, 628–647 (2015)
10. Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)
11. Takács G., Batista G., Gulan, M., Rohal’-Ilkiv B.: Embedded explicit model predictive vibration control. Mechatronics 36, 54–62 (2016)
12. Tatjewski, P.: Advanced control of industrial processes, structures and algorithms. Springer, London (2007)
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Chaber, P., Ławryńczuk, M. (2017). Implementation of Analytical Generalized Predictive Controller for Very Fast Applications Using Microcontrollers: Preliminary Results. In: Mitkowski, W., Kacprzyk, J., Oprzędkiewicz, K., Skruch, P. (eds) Trends in Advanced Intelligent Control, Optimization and Automation. KKA 2017. Advances in Intelligent Systems and Computing, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-319-60699-6_37
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DOI: https://doi.org/10.1007/978-3-319-60699-6_37
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