A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight conditions.The accelerometer and...A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight conditions.The accelerometer and magnetometer are introduced to construct an error equation with the gyros,thus the drifting characteristics of gyroscope can be compensated by solving the error equation utilized by the gradient descent algorithm.Performance of the presented algorithm is evaluated using a self-proposed micro-electro-mechanical system(MEMS)based attitude heading reference system which is mounted on a tri-axis turntable.The on-ground,turntable and flight experiments indicate that the estimation attitude has a good accuracy.Also,the presented system is compared with an open-source flight control system which runs extended Kalman filter(EKF),and the results show that the attitude control system using the gradient descent method can estimate the attitudes for UAV effectively.展开更多
With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rej...With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.展开更多
The gradient descent approach is the key ingredient in variational quantum algorithms and machine learning tasks,which is an optimization algorithm for finding a local minimum of an objective function.The quantum vers...The gradient descent approach is the key ingredient in variational quantum algorithms and machine learning tasks,which is an optimization algorithm for finding a local minimum of an objective function.The quantum versions of gradient descent have been investigated and implemented in calculating molecular ground states and optimizing polynomial functions.Based on the quantum gradient descent algorithm and Choi-Jamiolkowski isomorphism,we present approaches to simulate efficiently the nonequilibrium steady states of Markovian open quantum many-body systems.Two strategies are developed to evaluate the expectation values of physical observables on the nonequilibrium steady states.Moreover,we adapt the quantum gradient descent algorithm to solve linear algebra problems including linear systems of equations and matrix-vector multiplications,by converting these algebraic problems into the simulations of closed quantum systems with well-defined Hamiltonians.Detailed examples are given to test numerically the effectiveness of the proposed algorithms for the dissipative quantum transverse Ising models and matrix-vector multiplications.展开更多
提出了一种基于有效性分析的自组织模糊神经网络(self-organizingfuzzyneural network based on effectiveness analysis, SOEFNN)建模方法。首先,提出了一种针对模糊规则的有效性评价指标,利用样本与规则层输出之间的映射关系进行网络...提出了一种基于有效性分析的自组织模糊神经网络(self-organizingfuzzyneural network based on effectiveness analysis, SOEFNN)建模方法。首先,提出了一种针对模糊规则的有效性评价指标,利用样本与规则层输出之间的映射关系进行网络模型的有效性分析,通过累积触发的方式实现相应模糊规则的增加或删减,使网络模型在能够处理复杂非线性问题的同时降低其冗余性,使模型更为紧凑。采用梯度下降算法对网络模型进行训练。然后,对所提出的SOEFNN模型进行非线性系统仿真实验和污水处理过程中的出水生化需氧量预测建模,并与其他自组织模糊神经网络模型进行对比。仿真结果表明,所提出的SOEFNN模型能够很好地实现结构和参数的自适应调整,并且具有较好的逼近能力。展开更多
针对航天嵌入式软件(aerospace embedded software,AES)时序需求复杂带来的时序需求定义不准确问题,提出一种基于MARTE(modeling and analysis of real-time and embedded systems)模型的数据流时序(data flow timing based on MARTE,DF...针对航天嵌入式软件(aerospace embedded software,AES)时序需求复杂带来的时序需求定义不准确问题,提出一种基于MARTE(modeling and analysis of real-time and embedded systems)模型的数据流时序(data flow timing based on MARTE,DFT-MARTE)模型,设计基于该模型的处理点缓存计算算法、时序偏离概率检测算法和时序序列分析算法。处理点缓存计算算法动态更新缓存空间,使后续时序检测正常执行;时序偏离概率检测算法利用多线程并发模拟时序特性,检测需求中时序偏离问题;时序序列分析算法是基于梯度下降算法,拟合时序序列,指导用户优化需求。该模型相比传统数据流模型更适用航天嵌入式软件,利于后续开发和维护,具有极高的应用价值。展开更多
基金supported by the Fundamental Research Funds for the Central Universities(No.56XAA17075)
文摘A gradient descent algorithm with adjustable parameter for attitude estimation is developed,aiming at the attitude measurement for small unmanned aerial vehicle(UAV)in real-time flight conditions.The accelerometer and magnetometer are introduced to construct an error equation with the gyros,thus the drifting characteristics of gyroscope can be compensated by solving the error equation utilized by the gradient descent algorithm.Performance of the presented algorithm is evaluated using a self-proposed micro-electro-mechanical system(MEMS)based attitude heading reference system which is mounted on a tri-axis turntable.The on-ground,turntable and flight experiments indicate that the estimation attitude has a good accuracy.Also,the presented system is compared with an open-source flight control system which runs extended Kalman filter(EKF),and the results show that the attitude control system using the gradient descent method can estimate the attitudes for UAV effectively.
基金the 2021 Key Project of Natural Science and Technology of Yangzhou Polytechnic Institute,Active Disturbance Rejection and Fault-Tolerant Control of Multi-Rotor Plant ProtectionUAV Based on QBall-X4(Grant Number 2021xjzk002).
文摘With the increasing prevalence of high-order systems in engineering applications, these systems often exhibitsignificant disturbances and can be challenging to model accurately. As a result, the active disturbance rejectioncontroller (ADRC) has been widely applied in various fields. However, in controlling plant protection unmannedaerial vehicles (UAVs), which are typically large and subject to significant disturbances, load disturbances andthe possibility of multiple actuator faults during pesticide spraying pose significant challenges. To address theseissues, this paper proposes a novel fault-tolerant control method that combines a radial basis function neuralnetwork (RBFNN) with a second-order ADRC and leverages a fractional gradient descent (FGD) algorithm.We integrate the plant protection UAV model’s uncertain parameters, load disturbance parameters, and actuatorfault parameters and utilize the RBFNN for system parameter identification. The resulting ADRC exhibits loaddisturbance suppression and fault tolerance capabilities, and our proposed active fault-tolerant control law hasLyapunov stability implications. Experimental results obtained using a multi-rotor fault-tolerant test platformdemonstrate that the proposed method outperforms other control strategies regarding load disturbance suppressionand fault-tolerant performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.12075159,12171044,and 12005015)Beijing Natural Science Foundation(Grant No.Z190005)Academy for Multidisciplinary Studies,Capital Normal University,Academician Innovation Platform of Hainan Province,and Shenzhen Institute for Quantum Science and Engineering,Southern University of Science and Technology(Grant No.SIQSE202001)。
文摘The gradient descent approach is the key ingredient in variational quantum algorithms and machine learning tasks,which is an optimization algorithm for finding a local minimum of an objective function.The quantum versions of gradient descent have been investigated and implemented in calculating molecular ground states and optimizing polynomial functions.Based on the quantum gradient descent algorithm and Choi-Jamiolkowski isomorphism,we present approaches to simulate efficiently the nonequilibrium steady states of Markovian open quantum many-body systems.Two strategies are developed to evaluate the expectation values of physical observables on the nonequilibrium steady states.Moreover,we adapt the quantum gradient descent algorithm to solve linear algebra problems including linear systems of equations and matrix-vector multiplications,by converting these algebraic problems into the simulations of closed quantum systems with well-defined Hamiltonians.Detailed examples are given to test numerically the effectiveness of the proposed algorithms for the dissipative quantum transverse Ising models and matrix-vector multiplications.
文摘提出了一种基于有效性分析的自组织模糊神经网络(self-organizingfuzzyneural network based on effectiveness analysis, SOEFNN)建模方法。首先,提出了一种针对模糊规则的有效性评价指标,利用样本与规则层输出之间的映射关系进行网络模型的有效性分析,通过累积触发的方式实现相应模糊规则的增加或删减,使网络模型在能够处理复杂非线性问题的同时降低其冗余性,使模型更为紧凑。采用梯度下降算法对网络模型进行训练。然后,对所提出的SOEFNN模型进行非线性系统仿真实验和污水处理过程中的出水生化需氧量预测建模,并与其他自组织模糊神经网络模型进行对比。仿真结果表明,所提出的SOEFNN模型能够很好地实现结构和参数的自适应调整,并且具有较好的逼近能力。
基金Supported by the Science and Technology Innovation 2030 New Generation Artificial Intelligence Major Project(2018AAA0100902)the National Key Research and Development Program of China(2019YFB1705800)the National Natural Science Foundation of China(61973270)。
文摘针对航天嵌入式软件(aerospace embedded software,AES)时序需求复杂带来的时序需求定义不准确问题,提出一种基于MARTE(modeling and analysis of real-time and embedded systems)模型的数据流时序(data flow timing based on MARTE,DFT-MARTE)模型,设计基于该模型的处理点缓存计算算法、时序偏离概率检测算法和时序序列分析算法。处理点缓存计算算法动态更新缓存空间,使后续时序检测正常执行;时序偏离概率检测算法利用多线程并发模拟时序特性,检测需求中时序偏离问题;时序序列分析算法是基于梯度下降算法,拟合时序序列,指导用户优化需求。该模型相比传统数据流模型更适用航天嵌入式软件,利于后续开发和维护,具有极高的应用价值。
