Explicit model predictive control accuracy analysis

    •  Knyazev, A.; Zhu, P.; Di Cairano, S., "Explicit Model Predictive Control Accuracy Analysis", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC.2015.7402565, ISBN: 978-1-4799-7884-7, December 2015, pp. 2389-2394.
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      • @inproceedings{Knyazev2015dec3,
      • author = {Knyazev, A. and Zhu, P. and {Di Cairano}, S.},
      • title = {Explicit Model Predictive Control Accuracy Analysis},
      • booktitle = {IEEE Conference on Decision and Control (CDC)},
      • year = 2015,
      • pages = {2389--2394},
      • month = dec,
      • publisher = {IEEE},
      • doi = {10.1109/CDC.2015.7402565},
      • isbn = {978-1-4799-7884-7},
      • url = {}
      • }
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Model Predictive Control (MPC) can efficiently control constrained systems in real-time applications. MPC feedback law for a linear system with linear inequality constraints can be explicitly computed off-line, which results in an off-line partition of the state space into non-overlapped convex regions, with affine control laws associated to each region of the partition. An actual implementation of this explicit MPC in low cost micro-controllers requires the data to be "quantized", i.e. represented with a small number of memory bits. An
aggressive quantization decreases the number of bits and the controller manufacturing costs, and may increase the speed of the controller, but reduces accuracy of the control input computation. We derive upper bounds for the absolute error in the control depending on the number of quantization bits and system parameters. The bounds can be used to determine how many quantization bits are needed in order to guarantee a specific level of accuracy in the control input.