Linear tracking MPC for nonlinear systems Part II: The data-driven case

Berberich, Julian; Köhler, Johannes; Müller, Matthias A.; Allgöwer, Frank

doi:10.48550/arxiv.2105.08567

Cited by 6 publications

(13 citation statements)

References 46 publications

(161 reference statements)

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“…This condition ensures that the offsets Lu and L y will be carried through from the pre-collected data ūd , d , ȳd to the trajectory (ū ini , ini , ȳini , ū, , ȳ). Similar conditions can be found in [46], [47].…”

Section: Privacy-preserving Deep-lcc Reformulationsupporting

confidence: 84%

Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic

Zhang¹,

Chen²,

Li³

et al. 2022

Preprint

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Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models. However, employing the data-driven strategy involves the collection and sharing of privacy-sensitive vehicle information, which is vulnerable to privacy leakage and might further lead to malicious activities. In this paper, we develop a privacy-preserving data-enabled predictive control scheme for CAVs in a mixed traffic environment, where human-driven vehicles and CAVs coexist. We tackle external eavesdropper and honest-but-curious central unit eavesdropper who wiretap the communication channel of the mixed traffic system and intend to infer the CAVs' state and input information. An affine masking-based privacy protection method is designed to conceal the true state and input signals, and an extended form of the data-enabled predictive leading cruise control under different matrix structures is derived to achieve privacy-preserving optimal control for CAVs. The proposed scheme can protect the privacy of CAVs against the attackers without affecting control performance or incurring heavy computations. Numerical simulations demonstrate the efficacy of the developed approach.

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Section: Privacy-preserving Deep-lcc Reformulationsupporting

confidence: 84%

Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic

Zhang¹,

Chen²,

Li³

et al. 2022

Preprint

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“…The second is adding a perturbation to the solved predictive control trajectory. The third is including the level of excitation in the cost function [20]. However, buildings are significantly perturbed by the weather, which cannot be controlled.…”

Section: Persistent Excitation In Dual Controlmentioning

confidence: 99%

Comparison of behavioral systems theory and conventional linear models for predicting building zone temperature in long-term in situ measurements

Koch¹,

Jones²

2023

Preprint

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The potential of Model Predictive Control in buildings has been shown many times, being successfully used to achieve various goals, such as minimizing energy consumption or maximizing thermal comfort. However, mass deployment has thus far failed, in part because of the high engineering cost of obtaining and maintaining a sufficiently accurate model. This can be addressed by using adaptive data-driven approaches. The idea of using behavioral systems theory for this purpose has recently found traction in the academic community. In this study, we compare variations thereof with different amounts of data used, different regularization weights, and different methods of data selection. Autoregressive models with exogenous inputs (ARX) are used as a well-established reference. All methods are evaluated by performing iterative system identification on two long-term data sets from real occupied buildings, neither of which include artificial excitation for the purpose of system identification. We find that: (1) Sufficient prediction accuracy is achieved with all methods. (2) The ARX models perform slightly better, while having the additional advantages of fewer tuning parameters and faster computation. (3) Adaptive and non-adaptive schemes perform similarly. (4) The regularization weights of the behavioral systems theory methods show the expected trade-off characteristic with an optimal middle value. ( 5) Using the most recent data yields better performance than selecting data with similar weather as the day to be predicted. ( 6) More data improves the model performance.

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“…Guarantees for recursive feasibility, stability, and robustness (in the presence of measurement noise) of the closed loop were first proven by Berberich et al [4]. In recent years, various further properties and extensions of this data-driven MPC framework have been studied, compare, e.g., the works by Coulson et al [5], Huang et al [6], Yin et al [7], [8], Xue and Matni [9], Furieri et al [10], Berberich et al [11] and the overview paper by Markovsky and Dörfler [12].…”

Section: Introductionmentioning

confidence: 99%

A novel constraint tightening approach for robust data-driven predictive control

Klöppelt¹,

Berberich²,

Allgöwer³

et al. 2022

Preprint

Self Cite

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In this paper, we present a data-driven model predictive control (MPC) scheme that is capable of stabilizing unknown linear time-invariant systems under the influence of process disturbances. To this end, Willems' lemma is used to predict the future behavior of the system. This allows the entire scheme to be set up using only a priori measured data and knowledge of an upper bound on the system order. First, we develop a state-feedback MPC scheme, based on input-state data, which guarantees closed-loop practical exponential stability and recursive feasibility as well as closed-loop constraint satisfaction. The scheme is extended by a suitable constraint tightening, which can also be constructed using only data. In order to control a priori unstable systems, the presented scheme contains a pre-stabilizing controller and an associated input constraint tightening. We first present the proposed data-driven MPC scheme for the case of full state measurements, and also provide extensions for obtaining similar closed-loop guarantees in case of output feedback. The presented scheme is applied to a numerical example.

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Linear tracking MPC for nonlinear systems Part II: The data-driven case

Cited by 6 publications

References 46 publications

Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic

Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic

Comparison of behavioral systems theory and conventional linear models for predicting building zone temperature in long-term in situ measurements

A novel constraint tightening approach for robust data-driven predictive control

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