Structured Hammerstein-Wiener Model Learning for Model Predictive Control
This paper aims to improve the reliability of optimal control using models constructed by machine learning methods. Optimal control problems based on such models are generally non-convex and difficult to solve online. In this paper, we propose a model that combines the Hammerstein-Wiener model with input convex neural networks, which have recently been proposed in the field of machine learning. An important feature of the proposed model is that resulting optimal control problems are effectively solvable exploiting their convexity and partial linearity while retaining flexible modeling ability. The practical usefulness of the method is examined through its application to the modeling and control of an engine airpath system.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningmodelModel Predictive ControlSimilar Papers 제목 키워드 기반
Gaussian Process-Based Prediction and Control of Hammerstein-Wiener Systems
This work investigates data-driven prediction and control of Hammerstein-Wiener systems using physics-informed Gaussian process models. Data-driven prediction algorithms have been developed for structured nonlinear syste…
LEMMAPredictionLow-rank tensor recovery for Jacobian-based Volterra identification of parallel Wiener-Hammerstein systems
We consider the problem of identifying a parallel Wiener-Hammerstein structure from Volterra kernels. Methods based on Volterra kernels typically resort to coupled tensor decompositions of the kernels. However, in the ca…
Identification of Black-Box Inverter-Based Resource Control Using Hammerstein-Wiener Models
The development of more complex inverter-based resources (IBRs) control is becoming essential as a result of the growing share of renewable energy sources in power systems. Given the diverse range of control schemes, gri…
Wiener-Hammerstein model and its learning for nonlinear digital pre-distortion of optical transmitters
We present a simple nonlinear digital pre-distortion (DPD) of optical transmitter components, which consists of concatenated blocks of a finite impulse response (FIR) filter, a memoryless nonlinear function and another F…
Adaptive Output-Feedback Model Predictive Control of Hammerstein Systems with Unknown Linear Dynamics
This paper considers model predictive control of Hammerstein systems, where the linear dynamics are a priori unknown and the input nonlinearity is known. Predictive cost adaptive control (PCAC) is applied to this system …
Model Predictive Control