A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output（MIMO） aeroelastic system in the presence of wind gust,system uncertainties,and input nonlinearities consisting of i...A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output（MIMO） aeroelastic system in the presence of wind gust,system uncertainties,and input nonlinearities consisting of input saturation and dead-zone.In regard to the input nonlinearities,the right inverse function block of the dead-zone is added before the input nonlinearities,which simplifies the input nonlinearities into an equivalent input saturation.To deal with the equivalent input saturation,an auxiliary error system is designed to compensate for the impact of the input saturation.Meanwhile,uncertainties in pitch stiffness,plunge stiffness,and pitch damping are all considered,and radial basis function neural networks（RBFNNs） are applied to approximate the system uncertainties.In combination with the designed auxiliary error system and the backstepping control technique,a constrained adaptive neural network controller is designed,and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method.Finally,extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust,system uncertainties,and input nonlinearities.展开更多
Recently, flutter active control using linear parameter varying（LPV） framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroela...Recently, flutter active control using linear parameter varying（LPV） framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant（LTI） models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency（p-LSCF） algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification,the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequencydomain maximum likelihood（ML） estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.展开更多
气弹控制系统的驱动器、闭环信号回路在实际中会存在时滞环节,由于气弹敏感性和环境复杂性,时滞会引起控制信号迟延并导致气弹控制极速恶化、甚至造成系统失稳,该问题以往研究较少。针对翼型时滞气弹控制问题,设计了一种基于BRGWO算法...气弹控制系统的驱动器、闭环信号回路在实际中会存在时滞环节,由于气弹敏感性和环境复杂性,时滞会引起控制信号迟延并导致气弹控制极速恶化、甚至造成系统失稳,该问题以往研究较少。针对翼型时滞气弹控制问题,设计了一种基于BRGWO算法和改进型滤波Smith的最优气弹控制方法。首先,引入二阶滤波器改进Smith预估器,设计了翼型气弹控制器;然后,创新设计了一种双向随机灰狼优化算法(bidirectional random grey wolf optimization, BGWO),提高了时滞下气弹控制参数的全局寻优能力,该算法改进了不同等级灰狼的狩猎策略,提高跳出非理想值机率、避免陷入局部最优。利用最小增益原理,在理论上证明了闭环系统稳定性。仿真结果表明,对比传统智能优化算法(如遗传算法、灰狼优化算法)和多种已有控制器(经典Smith、PI-PD型Smith和传统滤波Smith预估器),该方法具有更强的时滞补偿能力和更优的气弹控制性能,在不确定时滞、不确定风速、刚度变化和驱动干扰等算例下,保持了优良的时滞气弹控制效果,具有较强的鲁棒性。展开更多
Based on modified Leishman-Beddoes （L-B） state space model at low Mach number （lower than 0.3）, the airfoil aeroelastic system is presented in this paper. The main modifications for L-B model include a new dynamic...Based on modified Leishman-Beddoes （L-B） state space model at low Mach number （lower than 0.3）, the airfoil aeroelastic system is presented in this paper. The main modifications for L-B model include a new dynamic stall criterion and revisions of normal force and pitching moment coefficient. The bifurcation diagrams, the limit cycle oscillation （LCO） phase plane plots and the time domain response figures are applied to investigating the stall flutter bifurcation behavior of airfoil aeroelastic systems with symmetry or asymmetry. It is shown that the symmetric periodical oscillation happens after subcritical bifurcation caused by dynamic stall, and the asymmetric periodical oscillation, which is caused by the interaction of dynamic stall and static divergence, only happens in the airfoil aeroelastic system with asymmetry. Validations of the modified L-B model and the airfoil aeroelastic system are presented with the experimental airload data of NACA0012 and OA207 and experimental stall flutter data of NACA0012 respectively. Results demonstrate that the airfoil aeroelastic system presented in this paper is effective and accurate, which can be applied to the investigation of airfoil stall flutter at low Mach number.展开更多
Structural nonlinearities such as freeplay will affect the stability and even flight safety of the fin-actuator system.There is a lack of a practical method for designing Active Flutter Suppression (AFS) control laws ...Structural nonlinearities such as freeplay will affect the stability and even flight safety of the fin-actuator system.There is a lack of a practical method for designing Active Flutter Suppression (AFS) control laws for nonlinear fin-actuator systems.A design method for the AFS controller of the nonlinear all-movable fin-electromechanical actuator system is established by combining the inverse system and the Immersion and Invariance (I&I) theory.First,the composite control law combining the inverse system principle and internal model control is used to offset the nonlinearity and dynamics of the actuator,so that its driving torque can follow the ideal signal.Then,the ideal torque of the actuator is designed employing the I&I theory.The unfavorable oscillation of the fin is suppressed by making the output torque of the actuator track the ideal signal.The simulation results reveal that the proposed AFS method can increase the flutter speed of the nonlinear finactuator system with freeplay,and a set of controller parameters is also applicable for wider freeplay within a certain range.The power required for the actuator does not exceed the power that can be provided by the commonly used aviation actuator.This method can also resist a certain level of noise and external disturbance.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61473307 and 61304120)the Aeronautical Science Foundation of China(No. 20155896026)
文摘A constrained adaptive neural network control scheme is proposed for a multi-input and multi-output（MIMO） aeroelastic system in the presence of wind gust,system uncertainties,and input nonlinearities consisting of input saturation and dead-zone.In regard to the input nonlinearities,the right inverse function block of the dead-zone is added before the input nonlinearities,which simplifies the input nonlinearities into an equivalent input saturation.To deal with the equivalent input saturation,an auxiliary error system is designed to compensate for the impact of the input saturation.Meanwhile,uncertainties in pitch stiffness,plunge stiffness,and pitch damping are all considered,and radial basis function neural networks（RBFNNs） are applied to approximate the system uncertainties.In combination with the designed auxiliary error system and the backstepping control technique,a constrained adaptive neural network controller is designed,and it is proven that all the signals in the closed-loop system are semi-globally uniformly bounded via the Lyapunov stability analysis method.Finally,extensive digital simulation results demonstrate the effectiveness of the proposed control scheme towards flutter suppression in spite of the integrated effects of wind gust,system uncertainties,and input nonlinearities.
基金co-supported by the National Natural Science Foundation of China (Nos. 61134004 and 61573289)Aeronautical Science Foundation of China (No. 20140753010)the Fundamental Research Funds for the Central Universities (No. 3102015BJ004)
文摘Recently, flutter active control using linear parameter varying（LPV） framework has attracted a lot of attention. LPV control synthesis usually generates controllers that are at least of the same order as the aeroelastic models. Therefore, the reduced-order model is required by synthesis for avoidance of large computation cost and high-order controller. This paper proposes a new procedure for generation of accurate reduced-order linear time-invariant（LTI） models by using system identification from flutter testing data. The proposed approach is in two steps. The well-known poly-reference least squares complex frequency（p-LSCF） algorithm is firstly employed for modal parameter identification from frequency response measurement. After parameter identification,the dominant physical modes are determined by clear stabilization diagrams and clustering technique. In the second step, with prior knowledge of physical poles, the improved frequencydomain maximum likelihood（ML） estimator is presented for building accurate reduced-order model. Before ML estimation, an improved subspace identification considering the poles constraint is also proposed for initializing the iterative procedure. Finally, the performance of the proposed procedure is validated by real flight flutter test data.
文摘气弹控制系统的驱动器、闭环信号回路在实际中会存在时滞环节,由于气弹敏感性和环境复杂性,时滞会引起控制信号迟延并导致气弹控制极速恶化、甚至造成系统失稳,该问题以往研究较少。针对翼型时滞气弹控制问题,设计了一种基于BRGWO算法和改进型滤波Smith的最优气弹控制方法。首先,引入二阶滤波器改进Smith预估器,设计了翼型气弹控制器;然后,创新设计了一种双向随机灰狼优化算法(bidirectional random grey wolf optimization, BGWO),提高了时滞下气弹控制参数的全局寻优能力,该算法改进了不同等级灰狼的狩猎策略,提高跳出非理想值机率、避免陷入局部最优。利用最小增益原理,在理论上证明了闭环系统稳定性。仿真结果表明,对比传统智能优化算法(如遗传算法、灰狼优化算法)和多种已有控制器(经典Smith、PI-PD型Smith和传统滤波Smith预估器),该方法具有更强的时滞补偿能力和更优的气弹控制性能,在不确定时滞、不确定风速、刚度变化和驱动干扰等算例下,保持了优良的时滞气弹控制效果,具有较强的鲁棒性。
基金Aeronautical Science Foundation of China (08A52003)Science and Technology Foundation of Rotorcraft Aeromechanics Laboratory (9140C4001010901)
文摘Based on modified Leishman-Beddoes （L-B） state space model at low Mach number （lower than 0.3）, the airfoil aeroelastic system is presented in this paper. The main modifications for L-B model include a new dynamic stall criterion and revisions of normal force and pitching moment coefficient. The bifurcation diagrams, the limit cycle oscillation （LCO） phase plane plots and the time domain response figures are applied to investigating the stall flutter bifurcation behavior of airfoil aeroelastic systems with symmetry or asymmetry. It is shown that the symmetric periodical oscillation happens after subcritical bifurcation caused by dynamic stall, and the asymmetric periodical oscillation, which is caused by the interaction of dynamic stall and static divergence, only happens in the airfoil aeroelastic system with asymmetry. Validations of the modified L-B model and the airfoil aeroelastic system are presented with the experimental airload data of NACA0012 and OA207 and experimental stall flutter data of NACA0012 respectively. Results demonstrate that the airfoil aeroelastic system presented in this paper is effective and accurate, which can be applied to the investigation of airfoil stall flutter at low Mach number.
文摘Structural nonlinearities such as freeplay will affect the stability and even flight safety of the fin-actuator system.There is a lack of a practical method for designing Active Flutter Suppression (AFS) control laws for nonlinear fin-actuator systems.A design method for the AFS controller of the nonlinear all-movable fin-electromechanical actuator system is established by combining the inverse system and the Immersion and Invariance (I&I) theory.First,the composite control law combining the inverse system principle and internal model control is used to offset the nonlinearity and dynamics of the actuator,so that its driving torque can follow the ideal signal.Then,the ideal torque of the actuator is designed employing the I&I theory.The unfavorable oscillation of the fin is suppressed by making the output torque of the actuator track the ideal signal.The simulation results reveal that the proposed AFS method can increase the flutter speed of the nonlinear finactuator system with freeplay,and a set of controller parameters is also applicable for wider freeplay within a certain range.The power required for the actuator does not exceed the power that can be provided by the commonly used aviation actuator.This method can also resist a certain level of noise and external disturbance.
