Data-Enabled Predictive Control: In the Shallows of the DeePC
Jeremy Coulson,John Lygeros,Florian Dörfler +2 more
- pp 307-312
TLDR
In this paper, a data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies using real-time feedback driving the unknown system along a desired trajectory while satisfying system constraints.Abstract:
We consider the problem of optimal trajectory tracking for unknown systems. A novel data-enabled predictive control (DeePC) algorithm is presented that computes optimal and safe control policies using real-time feedback driving the unknown system along a desired trajectory while satisfying system constraints. Using a finite number of data samples from the unknown system, our proposed algorithm uses a behavioural systems theory approach to learn a non-parametric system model used to predict future trajectories. The DeePC algorithm is shown to be equivalent to the classical and widely adopted Model Predictive Control (MPC) algorithm in the case of deterministic linear time-invariant systems. In the case of nonlinear stochastic systems, we propose regularizations to the DeePC algorithm. Simulations are provided to illustrate performance and compare the algorithm with other methods.read more
Citations
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Journal ArticleDOI
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
TL;DR: The presented results provide the first (theoretical) analysis of closed-loop properties, resulting from a simple, purely data-driven MPC scheme, including a slack variable with regularization in the cost.
Journal ArticleDOI
Willems’ Fundamental Lemma for State-Space Systems and Its Extension to Multiple Datasets
TL;DR: It is shown that all trajectories of a linear system can be obtained from a given finite number of trajectories, as long as these are collectively persistently exciting, which enables the identification of linear systems from data sets with missing samples.
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From noisy data to feedback controllers: non-conservative design via a matrix S-lemma
TL;DR: A new method to obtain feedback controllers of an unknown dynamical system directly from noisy input/state data and derive nonconservative design methods for quadratic stabilization, and data-based linear matrix inequalities, enables control design from large datasets.
Posted Content
Willems' Fundamental Lemma for State-space Systems and its Extension to Multiple Datasets
TL;DR: In this article, the authors extend Willems' lemma to the situation where multiple (possibly short) system trajectories are given instead of a single long one, and introduce a notion of collective persistency of excitation.
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Combining Prior Knowledge and Data for Robust Controller Design.
TL;DR: The approach leads to linear matrix inequality (LMI) based feasibility criteria which guarantee stability, $\mathcal{H}_2$-performance, or quadratic performance robustly for all closed-loop systems consistent with the prior knowledge and the available data.
References
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