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arXiv · 2104.04972

Data-driven predictive control with estimated prediction matrices and integral action

P. C. N. Verheijen·G. R. Gonçalves da Silva·M. Lazar
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Abstract

This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a system model such as, state-space or input-output models. Instead, we directly estimate the prediction matrices relating future outputs with current and future inputs from measured data, off-line. On-line, the developed data--driven predictive controller reduces to solving a quadratic program with a similar structure and complexity as linear MPC. Additionally, we develop a new procedure for estimating prediction matrices from data for predictive controllers with integral action, corresponding to the rate-based formulation of linear MPC. The effectiveness of the developed data-driven predictive controller is illustrated on position control of a linear motor model.

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P. C. N. Verheijen, G. R. Gonçalves da Silva, M. Lazar. 2021-04-11. Data-driven predictive control with estimated prediction matrices and integral action. https://arxiv.org/abs/2104.04972

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