Data-driven simulation and control

2008/09/18 by Ivan Markovsky, Paolo Rapisarda · 27 citations
Engineering · Physics and Astronomy · #Control Systems and Identification #Fault Detection and Control Systems #Model Reduction and Neural Networks

paper · doi:10.1080/00207170801942170

Abstract

Classical linear time-invariant system simulation methods are based on a transfer function, impulse response, or input/state/output representation. We present a method for computing the response of a system to a given input and initial conditions directly from a trajectory of the system, without explicitly identifying the system from the data. Similar to the classical approach for simulation, the classical approach for control is model-based: first a model representation is derived from given data of the plant and then a control law is synthesised using the model and the control specifications. We present an approach for computing a linear quadratic tracking control signal that circumvents the identification step. The results are derived assuming exact data and the simulated response or control input is constructed off-line.

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