Explicit stochastic predictive control of combustion plants based on Gaussian process models

By A. Grancharova, Jus Kocijan, T. A. Johansen. 2008

93 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering

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Citations in the corpus

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models (2018, background): “…such as Gaussian processes (GPs), are often the model of choice in MBRL, especially in low-dimensional problems where data efficiency is critical [Kocijan et al., 2004, Ko et al., 2007, Nguyen-Tuong et al., 2008, Grancharova et al., 2008, Deisenroth et al., 2014, Kamthe and Deisenroth, 2018].”
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control (2017, methodology): “MPC with GP transition models: GP-based predictive control was used for boiler and building control [13, 28], but the model uncertainty was discarded.”
Model Predictive Control — Status and Challenges (2013, unknown): “…” 策略 [1] , 包括输入 “ 分块化 (Blocking)” 技术 [60] 和预测函数控制算法 [61] 近年来预测控制的工业应用还体现出工业界对预测控制理论成果的关注和结合 , 如随机预测控制应用于工业油冷过程的温度控制 [118] , 显式随机预测控制在燃烧控制中的应用 [119] , 基于神经网络的预测控制方法在非线性工业系统中的应用 [120] , 针对具有周期重复特点的过程或设定 ( 扰动 ) 信号 , 把自学习控制 ( 或重复控制 ) 机理融入预测控制框架的自学习 ( 或重复 ) 预测控制方法 [121 − 123] 等 , 这些研究在一定…”
Cautious Model Predictive Control Using Gaussian Process Regression (2017, methodology): “In [3], a piecewise linear approximate explicit solution for the GP-based MPC problem of a combustion plant was presented.”
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research (2016, unknown): “Stochastic extensions of explicit MPC can potentially find many practical applications, in particular in safety-critical applications [128], [129].”