Neural Network Training Using Closed-Loop Data
Hazards and an Instrumental Variable (IVNN) Solution
Johan Kon (Eindhoven University of Technology)
Marcel Heertjes (Eindhoven University of Technology, ASML)
Tom Oomen (TU Delft - Mechanical Engineering, Eindhoven University of Technology)
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Abstract
An increasing trend in the use of neural networks in control systems is being observed. The aim of this paper is to reveal that the straightforward application of learning neural network feedforward controllers with closed-loop data may introduce parameter inconsistency that degrades control performance, and to provide a solution. The proposed method employs instrumental variables to ensure consistent parameter estimates. A nonlinear system example reveals that the developed instrumental variable neural network (IVNN) approach asymptotically recovers the optimal solution, while pre-existing approaches are shown to lead to inconsistent estimates.
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