This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties...
详细信息
ISBN:
(纸本)9781538629185
This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties and the frequency of the vibration feeder. First, a two dimensional（2D） weighing model was established based on the analysis of the dynamic characteristics of alloy weighing process. Second, a control scheme is proposed for the 2D model of alloy weighing ***, a robust iterative learning controller is developed and a stability condition of the 2D system is derived through linear matrix inequality（LMI） obtained by a 2D Lyapunov-Krasovskii function. Finally, the simulation results show that the proposed method can sufficiently improve the control precision of the alloy weighing process.
Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. ...
详细信息
Many technological systems consist of similar or identical subsystems, which are directly interconnected with their nearest neighbors. Communication networks enable an information exchange among subsystems, when a coo...
详细信息
This paper deals with the synchronization problem for a class of linear heterogeneous multi-agent systems. The agents are assumed to be similar in the sense that a part of the dynamics is common and the remaining part...
详细信息
Data augmentation methods for neural machine translation are particularly useful when limited amount of training data is available, which is often the case when dealing with low-resource languages. We introduce a nove...
详细信息
A new approach to the synthesis of linear virtual actuators for the purpose of reconfigurable control after actuator faults is described. A synthesis approach is provided that recovers the nominal closed-loop tracking...
详细信息
This paper investigates the synchronization problem of multi-agent systems with eventbased communication. A communication between the agents is invoked only after an event condition has been fulfilled, which can be ch...
详细信息
The paper presents the tuning and testing of an LQ optimal minimax tracking controller which is capable of attenuating low frequency deterministic, and all frequency stochastic disturbances. The controller - based on ...
详细信息
Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient t...
详细信息
Accurate forecasting for photovoltaic power generation is one of the key enablers for the integration of solar photovoltaic systems into power *** deep-learning-based methods can perform well if there are sufficient training data and enough computational ***,there are challenges in building models through centralized shared data due to data privacy concerns and industry *** learning is a new distributed machine learning approach which enables training models across edge devices while data reside *** this paper,we propose an efficient semi-asynchronous federated learning framework for short-term solar power forecasting and evaluate the framework performance using a CNN-LSTM *** design a personalization technique and a semi-asynchronous aggregation strategy to improve the efficiency of the proposed federated forecasting *** evaluations using a real-world dataset demonstrate that the federated models can achieve significantly higher forecasting performance than fully local models while protecting data privacy,and the proposed semi-asynchronous aggregation and the personalization technique can make the forecasting framework more robust in real-world scenarios.
暂无评论