Neural Network Training Using Closed-Loop Data: Hazards and an Instrumental Variable (IVNN) Solution
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Instrumental variable estimation of the proportional hazards model by presmoothing
We consider instrumental variable estimation of the proportional hazards model of Cox (1972). The instrument and the endogenous variable are discrete but there can be (possibly continuous) exogenous covariables. By makin…
quantile regressionregressionConsistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop
Refined instrumental variable methods have been broadly used for identification of continuous-time systems in both open and closed-loop settings. However, the theoretical properties of these methods are still yet to be f…
Consistency Analysis of the Closed-loop SRIVC Estimator
The Consistency of the Closed-Loop Simplified Refined Instrumental Variable method for Continuous-time system (CLSRIVC) is analysed based on sampled data. It is proven that the CLSRIVC estimator is not consistent when a …
Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control
Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identific…
Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems
Medical Cyber-physical Systems (MCPS) are vulnerable to accidental or malicious faults that can target their controllers and cause safety hazards and harm to patients. This paper proposes a combined model and data-driven…