This paper presents a Fault Mode Probability Factor（FMPF） based Fault-Tolerant Control（FTC） strategy for multiple faults of Dissimilar Redundant Actuation System（DRAS）composed of Hydraulic Actuator（HA） and Ele...This paper presents a Fault Mode Probability Factor（FMPF） based Fault-Tolerant Control（FTC） strategy for multiple faults of Dissimilar Redundant Actuation System（DRAS）composed of Hydraulic Actuator（HA） and Electro-Hydrostatic Actuator（EHA）. The long-term service and severe working conditions can result in multiple gradual faults which can ultimately degrade the system performance, resulting in the system model drift into the fault state characterized with parameter uncertainty. The paper proposes to address this problem by using the historical statistics of the multiple gradual faults and the proposed FMPF to amend the system model with parameter uncertainty. To balance the system model precision and computation time, a Moving Window（MW） method is used to determine the applied historical statistics. The FMPF based FTC strategy is developed for the amended system model where the system estimation and Linear Quadratic Regulator（LQR） are updated at the end of system sampling period. The simulations of DRAS system subjected to multiple faults have been performed and the results indicate the effectiveness of the proposed approach.展开更多
This paper presents a bio-inspired backstepping adaptive sliding mode control strategy for a novel 3 degree of freedom(3-DOF) parallel mechanism with actuation redundancy. Based on the kinematic model and the dynamic ...This paper presents a bio-inspired backstepping adaptive sliding mode control strategy for a novel 3 degree of freedom(3-DOF) parallel mechanism with actuation redundancy. Based on the kinematic model and the dynamic model, a sliding mode controller is designed to assure the tracking performance, and an adaptive law is introduced to approximate the system uncertainty including parameters variation, external disturbances and un-modeled part. Furthermore, a bio-inspired model is introduced to solve the inherent chattering problem of sliding mode control and provide a chattering free control. The simulation and experimental results testify that the proposed bio-inspired backstepping adaptive sliding mode control can achieve better performance(the tracking accuracy,robustness, response speed, etc.) than the conventional slide mode control.展开更多
针对冗余直接驱动阀伺服系统中由于余度降级所造成的性能降低问题,提出一种神经网络自适应滑模余度控制策略.利用径向基函数神经网络RBFNN(Radial Basis Function Neural Network)的在线学习功能,对系统发生的变化进行快速自适应补偿,...针对冗余直接驱动阀伺服系统中由于余度降级所造成的性能降低问题,提出一种神经网络自适应滑模余度控制策略.利用径向基函数神经网络RBFNN(Radial Basis Function Neural Network)的在线学习功能,对系统发生的变化进行快速自适应补偿,使系统状态趋近于滑模面,提高跟踪精度和鲁棒性;并通过与比例微分PD(Proportional-Derivative)算法的并行控制,促进RBFNN的收敛,增强系统的稳定性.通过与PID(Proportional-Integral-Deriva-tive)切换控制策略的对比研究,表明RBFNN自适应滑模余度控制方法不但设计简单,而且能够有效克服余度降级带来的系统性能下降的问题,极大地改善了系统的品质.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.51620105010,51675019 and 51575019)the National Basic Research Program of China(No.2014CB046402)+1 种基金the Fundamental Research Funds for the Central Universities of China(YWF-17-BJ-Y-105)the "111" Project of China
文摘This paper presents a Fault Mode Probability Factor（FMPF） based Fault-Tolerant Control（FTC） strategy for multiple faults of Dissimilar Redundant Actuation System（DRAS）composed of Hydraulic Actuator（HA） and Electro-Hydrostatic Actuator（EHA）. The long-term service and severe working conditions can result in multiple gradual faults which can ultimately degrade the system performance, resulting in the system model drift into the fault state characterized with parameter uncertainty. The paper proposes to address this problem by using the historical statistics of the multiple gradual faults and the proposed FMPF to amend the system model with parameter uncertainty. To balance the system model precision and computation time, a Moving Window（MW） method is used to determine the applied historical statistics. The FMPF based FTC strategy is developed for the amended system model where the system estimation and Linear Quadratic Regulator（LQR） are updated at the end of system sampling period. The simulations of DRAS system subjected to multiple faults have been performed and the results indicate the effectiveness of the proposed approach.
基金supported by National Natural Science Foundation of China(No.51375210)Priority Academic Program Development of Jiangsu Higher Education Institutions(No.6,2011)+1 种基金Postgraduate Research and Innovation Program of Jiangsu Higher Education Institutions(No.CXLX11-0598)Jiangsu University Senior Professionals Scientific Research Foundation(No.13JDG047)
文摘This paper presents a bio-inspired backstepping adaptive sliding mode control strategy for a novel 3 degree of freedom(3-DOF) parallel mechanism with actuation redundancy. Based on the kinematic model and the dynamic model, a sliding mode controller is designed to assure the tracking performance, and an adaptive law is introduced to approximate the system uncertainty including parameters variation, external disturbances and un-modeled part. Furthermore, a bio-inspired model is introduced to solve the inherent chattering problem of sliding mode control and provide a chattering free control. The simulation and experimental results testify that the proposed bio-inspired backstepping adaptive sliding mode control can achieve better performance(the tracking accuracy,robustness, response speed, etc.) than the conventional slide mode control.
文摘针对冗余直接驱动阀伺服系统中由于余度降级所造成的性能降低问题,提出一种神经网络自适应滑模余度控制策略.利用径向基函数神经网络RBFNN(Radial Basis Function Neural Network)的在线学习功能,对系统发生的变化进行快速自适应补偿,使系统状态趋近于滑模面,提高跟踪精度和鲁棒性;并通过与比例微分PD(Proportional-Derivative)算法的并行控制,促进RBFNN的收敛,增强系统的稳定性.通过与PID(Proportional-Integral-Deriva-tive)切换控制策略的对比研究,表明RBFNN自适应滑模余度控制方法不但设计简单,而且能够有效克服余度降级带来的系统性能下降的问题,极大地改善了系统的品质.
