Data-driven control system

Résumé

Data-driven control systems are a broad family of control systems, in which the identification of the process model and/or the design of the controller are based entirely on experimental data collected from the plant. In many control applications, trying to write a mathematical model of the plant is considered a hard task, requiring efforts and time to the process and control engineers. This problem is overcome by data-driven methods, which fit a system model to the experimental data collected, choosing it in a specific models class. The control engineer can then exploit this model to design a proper controller for the system. However, it is still difficult to find a simple yet reliable model for a physical system, that includes only those dynamics of the system that are of interest for the control specifications. The direct data-driven methods allow to tune a controller, belonging to a given class, without the need of an identified model of the system. In this way, one can also simpl

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https://en.wikipedia.org/wiki/Data-driven_control_system

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Publications associées (50)

Model-based and data-driven model-reference control: a comparative analysis

Alireza Karimi, Klaske van Heusden

In the scientific literature, two main approaches have been proposed for control system design from data. In the "model-based" approach, a model of the system is first derived from data and then a controller is computed based on the model. In the "data-driven" approach, the controller is directly computed from data. In this work, the above approaches are compared from a novel perspective. The main finding of the paper is that, although from the standard perspective of parameter variance analysis the model-based approach is always statistically more efficient, the data-driven controller might outperform the model-based solution for what concerns the final control cost.

Ieee2013

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A comparison of model-based and data-driven controller tuning

Alireza Karimi, Klaske van Heusden

In many industrial applications, finding a model from physical laws that is both simple and reliable for control design is a hard and time-consuming undertaking. When a set of input/output measurements is available, one can derive the controller directly from data, without relying on the knowledge of the physics. In the scientific literature, two main approaches have been proposed for control system design from data. In the 'model-based' approach, a model of the system is first derived from data and then a controller is computed-based on the model. In the 'data-driven' approach, the controller is directly computed from data. In this work, the previous approaches are compared from a novel perspective. The main finding of the paper is that, although from the standard perspective of parameter variance analysis the model-based approach is always statistically more efficient, the data-driven controller might outperform the model-based solution for what concerns the final control cost. Copyright (C) 2013 John Wiley & Sons, Ltd.

Wiley-Blackwell2014

Chargement

Model-based and data-driven model-reference control: a comparative analysis

Alireza Karimi, Klaske van Heusden

In many industrial applications, finding a model from physical laws that is both simple and reliable for control design is a hard and time-consuming undertaking. When a set of input/output (I/O) measurements is available, one can derive the controller directly from data, without relying on the knowledge of the physics. In the scientific literature, two main approaches have been proposed for control system design from data. In the “model-based” approach, a model of the system is first derived from data and then a controller is computed based on the model. In the “data-driven” approach, the controller is directly computed from data. In this work, the above approaches are compared from a novel perspective. The main finding of the paper is that, although from the standard perspective of parameter variance analysis the model-based approach is always statistically more efficient, the data-driven controller might outperform the model-based solution for what concerns the final control cost.

2013

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This course covers some theoretical and practical aspects of robust and adaptive control. This includes H-2 and H-infinity control in model-based and data-driven framework by convex optimization, direct, indirect and switching adaptive control. The methods are implemented in a hands-on lab.

This course consists of two parts:

architecture of automation systems, hands-on lab
handling of faults and failures in real-time systems, including fault-tolerant computing

This course introduces students to continuous, nonlinear optimization. We study the theory of optimization with continuous variables (with full proofs), and we analyze and implement important algorithms to solve constrained and unconstrained problems.

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