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Real-time proximal gradient method for embedded linear MPC
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DOI：10.1016/j.mechatronics.2019.02.004.png)
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Abstract
En 中文
This paper presents and experimentally validates an embedded linear model predictive control (MPC) approach that is particularly useful for implementation on resource-constrained embedded hardware. The MPC scheme is based on a real-time implementation of the proximal gradient method (PGM) and generates input signals with guaranteed constraint satisfaction. Given standard linear MPC assumptions, asymptotic stability of the resulting closed loop is proven. Applied to linear systems with simple input constraints, the real-time PGM results in very simple arithmetics that are rapidly executed on resource-constrained hardware. As a proof on concept, the algorithm is demonstrated on two experimental setups in which it is implemented on a micro-controller. These experimental validations and related simulations demonstrate how the proposed real-time PGM allows fast control rates, especially compared to state-of-the-art linear MPC approaches, and this while preserving good closed-loop performance.
Keywords:
Linear model predictive control
Proximal gradient method
Embedded control
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