This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a...This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.展开更多
The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation...The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation systems and proposes an approach based on the efficiency formula widely documented in the literature. In the absence of input data, this method makes it possible to estimate the plant’s input power using data extracted from the site, in particular that provided by the Ministry of the Environment. The importance of this study lies in the need to accurately determine the input power in order to assess the overall performance of the energy system.展开更多
Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Insp...Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.展开更多
针对受扰移动机器人系统自触发模型预测控制(self-triggered model predictive control,STMPC)在虚假数据注入(false data injection,FDI)攻击下的安全控制问题,提出了一种基于输入重构的弹性STMPC方法。结合自触发机制非周期采样特性和...针对受扰移动机器人系统自触发模型预测控制(self-triggered model predictive control,STMPC)在虚假数据注入(false data injection,FDI)攻击下的安全控制问题,提出了一种基于输入重构的弹性STMPC方法。结合自触发机制非周期采样特性和FDI攻击模型,设计了一种基于关键数据的输入重构机制,以减弱FDI攻击对被控系统的影响。根据状态误差的最优控制问题,设计了重构参数的确定方法,以保证系统在应用重构控制输入后的控制性能。详细分析了所提出弹性STMPC算法的稳定性以及算法可行性。通过仿真和实验验证了所提出算法的有效性。展开更多
基金supported by National Natural Science Foundation of China(Nos.61603114,61673135)the Fundamental Research Funds for the Central Universities of China(No.HIT.NSRIF.201826)
文摘This study presents an improved data-driven Model-Free Adaptive Control(MFAC)strategy for attitude stabilization of a partially constrained combined spacecraft with external disturbances and input saturation. First, a novel dynamic linearization data model for the partially constrained combined spacecraft with external disturbances is established. The generalized disturbances composed of external disturbances and dynamic linearization errors are then reconstructed by a Discrete Extended State Observer(DESO). With the dynamic linearization data model and reconstructed information, a DESO-MFAC strategy for the combined spacecraft is proposed based only on input and output data. Next, the input saturation is overcome by introducing an antiwindup compensator. Finally, numerical simulations are carried out to demonstrate the effectiveness and feasibility of the proposed controller when the dynamic properties of the partially constrained combined spacecraft are completely unknown.
文摘The study focuses on estimating the input power of a power plant from available data, using the theoretical inverter efficiency as the key parameter. The paper addresses the problem of missing data in power generation systems and proposes an approach based on the efficiency formula widely documented in the literature. In the absence of input data, this method makes it possible to estimate the plant’s input power using data extracted from the site, in particular that provided by the Ministry of the Environment. The importance of this study lies in the need to accurately determine the input power in order to assess the overall performance of the energy system.
基金supported by the National Natural Science Foundation of China(No.12104396)the National Key R&D Program of China(Nos.2021YFF1200404 and 2021YFA1201200)+2 种基金the National Independent Innovation Demonstration Zone Shanghai Zhangjiang Major Projects(No.ZJZX2020014)the Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University(No.SN-ZJU-SIAS-003)the Shanghai Artificial Intelligence Lab(No.P22KN00272),China.
文摘Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.
文摘针对受扰移动机器人系统自触发模型预测控制(self-triggered model predictive control,STMPC)在虚假数据注入(false data injection,FDI)攻击下的安全控制问题,提出了一种基于输入重构的弹性STMPC方法。结合自触发机制非周期采样特性和FDI攻击模型,设计了一种基于关键数据的输入重构机制,以减弱FDI攻击对被控系统的影响。根据状态误差的最优控制问题,设计了重构参数的确定方法,以保证系统在应用重构控制输入后的控制性能。详细分析了所提出弹性STMPC算法的稳定性以及算法可行性。通过仿真和实验验证了所提出算法的有效性。
