Fast Nonlinear Model Predictive Control Algorithm with Neural Approximation for Embedded Systems: Preliminary Results
- 484 Downloads
This work presents preliminary results of research concerned with a fast nonlinear Model Predictive Control (MPC) algorithm implemented in an embedded system. In order to obtain a computationally efficient solution, a linear approximation of the predicted trajectory of the controlled variables is calculated for each sampling instant on-line which leads to a quadratic optimisation problem. Furthermore, the matrix of derivatives, which defines the linearised trajectory, is not determined analytically, but it is calculated (approximated) by a specially trained neural network. In order to show effectiveness of the discussed approach, a dynamic process with two inputs and two outputs is considered for which not only simulation results, but also results of real experiments performed in an embedded system based on a microcontroller are given.
KeywordsEmbedded systems Microcontrollers Model Predictive Control Neural networks Nonlinear control
- 2.Chaber, P., Ławryńczuk, M.: AutoMATiC: Code generation of model predictive control algorithms for microcontrollers. IEEE Trans. Industr. Inf. 16(7), 4547–4556 (2020). https://doi.org/10.1109/TII.2019.2946842
- 6.Ławryńczuk, M.: Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach. Studies in Systems, Decision and Control, vol. 3. Springer, Cham (2014)Google Scholar
- 9.Stellato, B., Banjac, G., Goulart, P., Bemporad, A., Boyd, S.: OSQP: an operator splitting solver for quadratic programs. arXiv e-prints https://arxiv.org/abs/1711.08013 (2017)
- 12.Wojtulewicz, A., Ławryńczuk, M.: Computationally efficient implementation of dynamic matrix control algorithm for very fast processes using programmable logic controller. In: 2018 23rd International Conference on Methods & Models in Automation & Robotics (MMAR), pp. 579–584 (2018)Google Scholar