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Robust model agnostic predictive control algorithm for randomly excited
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AI
DOI：10.1016/j.probengmech.2023.103517.png)
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Abstract
En 中文
We propose a novel robust model agnostic predictive control (RoMAn-MPC) algorithm and illustrate its application in randomly excited dynamical systems. Unlike conventional model predictive control algorithms, the proposed RoMAn-MPC requires no information about the underlying physics of the system; instead, the governing physics is identified by using the recently proposed stochastic equation discovery framework. One key advantage of the proposed approach resides in its capability to generalize; this eliminates the repeated retraining phase - a major bottleneck with other machine learning-based model agnostic control algorithms. Overall, the proposed RoMAn-MPC (a) is robust against measurement noise, (b) works with sparse measurements, (c) can tackle set-point changes, (d) works with multiple control variables, and (e) can incorporate dead time. We have obtained state-of-the-art results on several benchmark examples. Finally, we use the proposed approach for vibration mitigation of a 76-storey building under seismic loading.
Keywords:
Robust model predictive control
Stochastic control
Bayesian inference
Knowledge discovery
Nonlinear control systems
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