Abstract
This paper describes implementation of the Dynamic Matrix Control (DMC) algorithm performed on an Altera Field Programmable Gate Array (FPGA) with the Cyclone IV chip. The DMC 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. The test-bench application is prepared for fast comparison between C and HDL versions of code. A large number of independent logic cells can provide multi-parallel operations to achieve very fast operations. As a result, the algorithm may be used for controlling very fast dynamic processes characterised by sampling periods of millisecond order. Preliminary results of real experiments are demonstrated. The discussed control structure provides possibility to fast change of algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
1. Camacho, E.F., Bordons, C.: Model Predictive Control. Springer, London (1999)
2. Chaber, P., Ławrynczuk, 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)
3. Cutler, C.R., Ramaker, B.L.: Dynamic matrix control – a computer control algorithm. In: Proceedings of the Joint Automatic Control Conference, San Francisco, USA (1979)
4. Lan, J., Li, D., Xi, Y., Implementation of Dynamic Matrix Control on FPGA. Proceedings of the 29th Chinese Control Conference, Beijing, pp. 5970-5974 (2010)
5. Ling, K.V., Wu, B.F., Maciejowski, J.M.: Embedded model predictive control (MPC) using a FPGA. IFAC Proceedings Volumes 41 (2), 15250–15255 (2008)
6. Ling, K.V., Yue, S.P., Maciejowski J. M.: A FPGA implementation of model predictive control. American Control Conference pp. 6 (2006)
7. 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)
8. Ławrynczuk, M.: Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach, Studies in Systems, Decision and Control, vol. 3, Springer, Cham (2014)
9. Qin, S.J., Badgwell, T.A.: A survey of industrial model predictive control technology. Control Engineering Practice 11, 733–764 (2003)
10. Tatjewski, P.: Advanced control of industrial processes, structures and algorithms. Springer, London (2007)
11. Wojtulewicz, A., Chaber, P., Ławrynczuk, M.: Multiple-Input Multiple-Output Laboratory Stand for Process Control Education, 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, 2016, pp. 466–471.
12. Petko, M., Staworko, M., Lubieniecki, M.: Automatic software-hardware implementation of process control algorithms using FPGA (in Polish), Pomiary, Automatyka, Kontrola 5, 297–300 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Wojtulewicz, A. (2017). Implementation of Dynamic Matrix Control Algorithm Using Field Programmable Gate Array: 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_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-60699-6_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60698-9
Online ISBN: 978-3-319-60699-6
eBook Packages: EngineeringEngineering (R0)