From model-based control to data-driven control: Survey, classification and perspective

Hou, Zhongsheng; Wang, Zhuo

doi:10.1016/j.ins.2012.07.014

Cited by 1,107 publications

(560 citation statements)

References 102 publications

Supporting

Mentioning

544

Contrasting

Unclassified

Order By: Relevance

“…Like most control problems dissipativity theory requires system models (state space, high order differential/difference equations); consequently, when such models are not available it is mandatory to perform system identification before studying dissipative systems. In recent developments of data-based approaches to control (Shi and Skelton (2000); Safonov and Tsao (1997); Rapisarda (2016, 2017); Markovsky and Rapisarda (2008)), where the design of a controller is based on system data rather than models, which are in many real-life situations not available, (see Hou and Wang (2013) for a formal definition of data-driven control and a summary of approaches in the literature). It has become important to link the theory of dissipativity and system data.…”

Section: Introductionmentioning

confidence: 99%

On Lyapunov functions and data-driven dissipativity

2017

View full text Add to dashboard Cite

Our contribution in this paper is twofold. In the first part, we study Lyapunov functions when a plant is interconnected with a dissipative stabilizing controller. In the second, we present results on data-driven approach to dissipative systems. In particular, we provide conditions under which an observed trajectory can be used to determine whether a system is dissipative with respect to a given supply rate. Our results are based on linear difference systems for which the use of quadratic difference forms play a central role for dissipativity and Lyapunov theory.

show abstract

Section: Introductionmentioning

confidence: 99%

On Lyapunov functions and data-driven dissipativity

2017

View full text Add to dashboard Cite

show abstract

“…Clearly more cost-effective model generation methods are urgently required if MPC is to be economically widely-adopted. Possibly the area of data-driven control (Hou & Wang, 2013) may present a way forward. Given the large amount building-specific information required to formulate accurate white-box and grey-box models , black-box models seem to be a highly-feasible approach.…”

Section: Discussion: Open Questions In Mpc Buildings Researchmentioning

confidence: 99%

Model-predictive control for non-domestic buildings: a critical review and prospects

Rockett

Hathway

2016

Building Research & Information

View full text Add to dashboard Cite

Model-predictive control (MPC) has recently excited a great deal of interest as a new control paradigm for non-domestic buildings. Since it is based on the notion of optimisation, MPC is, in principle, well-placed to deliver significant energy savings and reduction in carbon emissions compared to existing rule-based control systems. In this paper, we critically review the prospects for buildings MPC and, in particular, the central role of the predictive mathematical model that lies at its heart; our clear emphasis is on practical implementation rather than control-theoretic aspects, and covers the role of occupants as well as the form of the predictive model. The most appropriate structure for such a model is still an open question, which we discuss alongside the development of the initial model, and the process of updating the model during the building's operational life. The importance of sensor placement is highlighted alongside the possibility of updating the model with occupants' comfort perception. We conclude that there is an urgent need for research on the automated creation and updating of predictive models if MPC is to become an economically-viable control methodology for non-domestic buildings. Finally, more evidence through operating full scale buildings with MPC is required to demonstrate the viability of this method.

show abstract

“…In this approach, the state space model and pole placement feedback gain are identified simultaneously from the set of state measurements and control input sequences. The method proposed in [5] is based on the data-driven control framework ( [6] and references therein) such as unfalsified control [7], virtual reference feedback tuning (VRFT) [8] [9], or fictitious reference iterative tuning (FRIT) [10] [11] [12] [13]. In the data-driven control framework, where no explicit mathematical plant model is used, a feedback controller must be derived that satisfies the prescribed closed-loop performance and fits to known experimental data.…”

Section: Introductionmentioning

confidence: 99%

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

Shwe¹,

Yamamoto²

2017

ICA

View full text Add to dashboard Cite

The recently proposed data-driven pole placement method is able to make use of measurement data to simultaneously identify a state space model and derive pole placement state feedback gain. It can achieve this precisely for systems that are linear time-invariant and for which noiseless measurement datasets are available. However, for nonlinear systems, and/or when the only noisy measurement datasets available contain noise, this approach is unable to yield satisfactory results. In this study, we investigated the effect on data-driven pole placement performance of introducing a prefilter to reduce the noise present in datasets. Using numerical simulations of a self-balancing robot, we demonstrated the important role that prefiltering can play in reducing the interference caused by noise.

show abstract

From model-based control to data-driven control: Survey, classification and perspective

Cited by 1,107 publications

References 102 publications

On Lyapunov functions and data-driven dissipativity

On Lyapunov functions and data-driven dissipativity

Model-predictive control for non-domestic buildings: a critical review and prospects

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

Contact Info

Product

Resources

About