Abstract
Model predictive control (MPC) has been used in the process industries for more than 40 years because of its ability to control multivariable systems in an optimized way under constraints on input and output variables. Traditionally, MPC requires the solution of a quadratic program (QP) online to compute the control action, sometimes restricting its applicability to slow processes. Explicit MPC completely removes the need for online solvers by precomputing the control law off-line, so that online operations reduce to a simple function evaluation. Such a function is piecewise affine in most cases, so that the MPC controller is equivalently expressed as a lookup table of linear gains, a form that is extremely easy to code, requires only basic arithmetic operations, and requires a maximum number of iterations that can be exactly computed a priori.
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Bemporad, A. (2019). Explicit Model Predictive Control. In: Baillieul, J., Samad, T. (eds) Encyclopedia of Systems and Control. Springer, London. https://doi.org/10.1007/978-1-4471-5102-9_10-2
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DOI: https://doi.org/10.1007/978-1-4471-5102-9_10-2
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Latest
Explicit Model Predictive Control- Published:
- 14 September 2019
DOI: https://doi.org/10.1007/978-1-4471-5102-9_10-2
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Original
Explicit Model Predictive Control- Published:
- 20 March 2014
DOI: https://doi.org/10.1007/978-1-4471-5102-9_10-1