The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular sy...The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array(MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements,and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally,simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.展开更多
This paper introduces an observer-based adaptive optimal control method for unknown singularly perturbed nonlinear systems with input constraints. First, a multi-time scales dynamic neural network(MTSDNN) observer wit...This paper introduces an observer-based adaptive optimal control method for unknown singularly perturbed nonlinear systems with input constraints. First, a multi-time scales dynamic neural network(MTSDNN) observer with a novel updating law derived from a properly designed Lyapunov function is proposed to estimate the system states. Then, an adaptive learning rule driven by the critic NN weight error is presented for the critic NN, which is used to approximate the optimal cost function. Finally, the optimal control action is calculated by online solving the Hamilton-Jacobi-Bellman(HJB)equation associated with the MTSDNN observer and critic NN.The stability of the overall closed-loop system consisting of the MTSDNN observer, the critic NN and the optimal control action is proved. The proposed observer-based optimal control approach has an essential advantage that the system dynamics are not needed for implementation, and only the measured input/output data is needed. Moreover, the proposed optimal control design takes the input constraints into consideration and thus can overcome the restriction of actuator saturation.Simulation results are presented to confirm the validity of the investigated approach.展开更多
This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics.The requirement of the complete knowledg...This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics.The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values.An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network(NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman(HJB) equation,which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error.Stability of the whole system consisting of the identifier NN,the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.展开更多
基金supported by the National Nature Science Foundation of China (62063011,62273169, 61922037, 61873115)Yunnan Fundamental Research Projects(202001AV070001)+1 种基金Yunnan Major Scientific and Technological Projects(202202AG050002)partially supported by the Open Foundation of Key Laboratory in Software Engineering of Yunnan Province (2020SE502)。
文摘The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array(MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements,and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally,simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.
文摘目的:运用网络药理学方法分析康艾注射液抗肿瘤的作用机制。方法:利用中药系统药理学技术平台(TCMSP)筛选康艾注射液的组成中药的有效成分及对应靶蛋白。利用Cytoscape 3.6.1构建康艾注射液有效化合物-靶蛋白网络并进行网络拓扑学分析。借助STRING在线数据库进行蛋白互作(PPI)网络构建并进行分析。通过生物学信息注释数据库(DAVID)对靶点进行生物过程(Gene Ontology Biological Process,GO-BP)富集分析及基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)通路分析。结果:共获得83个候选化合物,包含了黄酮类、人参皂苷、苦参碱等有效组分;240个靶点,关键靶点是COX1、COX2、HSP90、TP53、AKT1、JUN等;生物过程条目共250条,涉及转录翻译、细胞信号转导、细胞增殖与凋亡过程、免疫反应、血管生成等生物过程;KEGG信号通路共127条,其中癌症通路、PI3K-Akt信号通路、淋巴瘤病毒感染、癌症中的蛋白聚糖、MAPK信号通路、TNF信号通路、前列腺癌、钙离子信号通路、癌症的mRNA等通路与肿瘤密切相关。结论:作用于COX1、COX2、HSP90、TP53、AKT1、JUN等靶点,康艾注射液中的黄酮类、人参皂苷、苦参碱等有效组分通过抑制肿瘤细胞增殖、促进其凋亡,调节肿瘤血管生成和免疫反应等生物过程,影响肿瘤微环境,从而发挥抗肿瘤作用,体现了多成分、多靶点、多通路的特点。
基金supported by the Natural Sciences and Engineering Research Council of Canada(N00892)in part by National Natural Science Foundation of China(51405436,51375452,61573174)
文摘This paper introduces an observer-based adaptive optimal control method for unknown singularly perturbed nonlinear systems with input constraints. First, a multi-time scales dynamic neural network(MTSDNN) observer with a novel updating law derived from a properly designed Lyapunov function is proposed to estimate the system states. Then, an adaptive learning rule driven by the critic NN weight error is presented for the critic NN, which is used to approximate the optimal cost function. Finally, the optimal control action is calculated by online solving the Hamilton-Jacobi-Bellman(HJB)equation associated with the MTSDNN observer and critic NN.The stability of the overall closed-loop system consisting of the MTSDNN observer, the critic NN and the optimal control action is proved. The proposed observer-based optimal control approach has an essential advantage that the system dynamics are not needed for implementation, and only the measured input/output data is needed. Moreover, the proposed optimal control design takes the input constraints into consideration and thus can overcome the restriction of actuator saturation.Simulation results are presented to confirm the validity of the investigated approach.
文摘This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics.The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values.An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network(NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman(HJB) equation,which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error.Stability of the whole system consisting of the identifier NN,the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method.
