[HTML][HTML] A quantitative analysis of model predictive control as energy management strategy for hybrid electric vehicles: A review
Q Zhou, C Du - Energy Reports, 2021 - Elsevier
This paper innovatively applies fuzzy comprehensive evaluation (FCE) method with analytic
hierarchy process methodology (AHP) to quantitatively explore the academic value of …
hierarchy process methodology (AHP) to quantitatively explore the academic value of …
[PDF][PDF] Current status and prospects for model predictive energy management in hybrid electric vehicles
张风奇, 胡晓松, 许康辉, 唐小林… - Journal of Mechanical …, 2019 - qikan.cmes.org
Energy management strategies are a core technology in hybrid electric vehicles and plug-in
hybrid electric vehicles (HEVs/PHEVs), which directly determines fuel economy, power …
hybrid electric vehicles (HEVs/PHEVs), which directly determines fuel economy, power …
Computationally efficient model predictive control algorithms
M Ławryńczuk - A Neural Network Approach, Studies in Systems …, 2014 - Springer
In the Proportional-Integral-Derivative (PID) controllers the control signal is a linear function
of: the current control error (the proportional part), the past errors (the integral part) and the …
of: the current control error (the proportional part), the past errors (the integral part) and the …
[PDF][PDF] 混合动力汽车模型预测能量管理研究现状与展望
张风奇, 胡晓松, 许康辉, 唐小林, 崔亚辉 - 机械工程学报, 2019 - qikan.cmes.org
能量管理策略是混合动力汽车的核心技术, 其直接决定了整车燃油经济性, 动力性及驾驶性.
然而, 实际工况的不确定性和扰动性极大地增加了能量管理算法的设计难度. 为此, 开发高效 …
然而, 实际工况的不确定性和扰动性极大地增加了能量管理算法的设计难度. 为此, 开发高效 …
Neural predictive control for a car-like mobile robot
This paper presents a new path tracking scheme for a car-like mobile robot based on neural
predictive control. A multi-layer back-propagation neural network is employed to model non …
predictive control. A multi-layer back-propagation neural network is employed to model non …
Neural networks for modeling and control of particle accelerators
AL Edelen, SG Biedron, BE Chase… - … on Nuclear Science, 2016 - ieeexplore.ieee.org
Particle accelerators are host to myriad nonlinear and complex physical phenomena. They
often involve a multitude of interacting systems, are subject to tight performance demands …
often involve a multitude of interacting systems, are subject to tight performance demands …
A new TS fuzzy model predictive control for nonlinear processes
In this paper, a novel fuzzy Generalized Predictive Control (GPC) is proposed for discrete-
time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS …
time nonlinear systems via Takagi-Sugeno system based Kernel Ridge Regression (TS …
Generalized predictive control using recurrent fuzzy neural networks for industrial processes
CH Lu, CC Tsai - Journal of process control, 2007 - Elsevier
This paper presents a design methodology for predictive control of industrial processes via
recurrent fuzzy neural networks (RFNNs). A discrete-time mathematical model using RFNN …
recurrent fuzzy neural networks (RFNNs). A discrete-time mathematical model using RFNN …
Identification and control of dynamical systems using the self-organizing map
GA Barreto, AFR Araujo - IEEE Transactions on Neural …, 2004 - ieeexplore.ieee.org
In this paper, we introduce a general modeling technique, called vector-quantized temporal
associative memory (VQTAM), which uses Kohonen's self-organizing map (SOM) as an …
associative memory (VQTAM), which uses Kohonen's self-organizing map (SOM) as an …
Model predictive control of nonlinear systems with unmodeled dynamics based on feedforward and recurrent neural networks
This paper presents new results on a neural network approach to nonlinear model predictive
control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of …
control. At first, a nonlinear system with unmodeled dynamics is decomposed by means of …