机翼颤振的鲁棒自适应切换控制

将机翼颤振问题对应的气动伺服弹性系统动态,看做沿飞行轨迹或特定飞行动作的若干模型的连续切换,引入切换系统描述其包线内的动态特性。进而,基于多Lyapunov函数方法和模型参考自适应控制(MRAC)理论,提出一种机翼颤振鲁棒自适应切换控制方案。该方案允许不同速度设计点的模型及对应控制器进行快速切换,以全面覆盖飞行包线内的系统动态,获得良好的机翼颤振主动抑制效果,有效规避自适应控制需要参数缓慢变化的缺陷。同时,构建Lure-Postnikov形式的Lyapunov函数,采用多Lyapunov函数方法,以线性矩阵不等式的形式,推导闭环切换系统为有界MRAC系统的充分条件,确保飞行包线内颤振抑制的全局稳定性。     

机器人在轨装配无标定视觉伺服对准方法

针对在轨装配过程中机器人"手眼"关系无法进行有效标定及机器人系统和被操作物惯性参数不定的情况,在传统的无标定视觉伺服基础上设计了深度估计器,基于机器人和图像运动的测量数据在线估计目标特征的深度值,并在机器人关节控制环中设计滑模控制器实时控制机器人关节运动,根据反馈图像信息纠正系统误差完成对准跟踪,通过仿真验证了方法的有效性。所提的无标定视觉伺服对准方法使机器人在装配过程中免去了复杂的"手眼"关系的标定程序,克服了机器人系统及被操作物惯性参数不确定性给装配精度造成的影响,提高了"手眼协调"的鲁棒性,保证机器人能够在复杂的太空环境下完成在轨装配任务。     

航空发动机滑动局部线性模型及控制

为了提高航空发动机的控制性能和控制系统的可靠性,提出一种基于粒子群算法的航空发动机局部小区域自适应滑动的线性模型建立方法和双闭环自适应PI控制方法。在辨识区间内,以发动机转子转速为状态变量,采用在线实测数据和粒子群算法的参数估计方法,使模型辨识参数按照设定区间大小自适应跟踪滑动,从而保证线性模型能够精确逼近发动机的非线性动态。通过分析发动机燃油调节器的工作特性,建立了燃油调节器计量活门和电液伺服阀的传递函数,并根据所构建的模型,设计了航空发动机转子转速和燃油流量双闭环自适应PI控制系统以实现对航空发动机的精确控制。结果表明：利用局部滑动自适应辨识计算机得到的数据与发动机稳态、动态试验数据相吻合,且双闭环仿真控制性能满足航空发动机工作性能要求,表明所提出的航空发动机局部线性建模方法和自适应PI控制器参数算法对提高发动机的控制性能是有效的。     

考虑舵机动力学的旋转弹自适应解耦控制

为解决由气动交联、控制交联引起的旋转弹俯仰和偏航通道间的耦合问题，提出了一种考虑气动不确定和执行机构动力学的自适应解耦控制方法。以一类鸭舵作用下的双通道控制旋转弹为研究对象，建立了非旋转弹体坐标系下考虑舵机动态响应过程以及气动参数不确定性的线性化动力学模型。利用模型参考自适应控制方法作为基础框架，将跟踪误差积分扩维至被控系统以改善闭环系统的跟踪性能。将舵机输入与输出之间的误差信号反馈到参考模型中实现俯仰和偏航通道之间的解耦。通过理论分析和数值仿真，验证了所提自适应解耦控制方法的有效性。仿真结果表明，设计的自适应解耦控制器能够保证闭环系统稳定并且实现俯仰和偏航通道之间的解耦。     

建模不确定性下变循环发动机自适应控制器设计

针对变循环航空发动机存在建模不确定情况下的多变量控制器设计问题,给出了一种最优律下的增广模型参考自适应跟踪补偿设计方法,阐明变循环发动机建模不确定性并引出LQR(Linear Quadratic Regulator)基准控制器动态跟踪不足的问题,进而在基准控制器反馈控制结构框架下,分别实现以下技术突破:确定期望闭环动态的参考模型、基于李亚普诺夫函数严格稳定证明下建立存在建模不确定性的自适应律等,最终以达到系统跟踪误差渐进为零的目标,改善系统不确定性的动态跟踪问题。对变循环航空发动机的控制器仿真结果表明:增广模型参考自适应控制方法改善了原LQR基准控制器对存在建模不确定性时的控制问题,有效地实现了控制指令跟踪,达到了所期望的动态响应,且自适应参数一致渐进稳定。同时,各标称点的稳态控制误差均小于0.3%,系统超调量小于0.8%,调节时间小于1s,符合航空发动机控制系统技术要求。     

一款工业机器人上下料系统的研究

本文通过对基于双目视觉的机器人上下料系统的设计,引入视觉测量与处理技术准确获取工件轮廓、位置和位姿数据,实现对工件的自动、准确和快速定位。融合网络通讯技术实现对六自由度机器人的伺服控制,实时导引机器人完成上下料动作。试验结果表明该系统能自适应工件形状,快速准确地完成上下料,能满足自动化要求,具有重要的应用价值。     

非线性气动弹性模型参考自适应控制

 首先将含有前/后缘双控制面二元机翼的动态方程以状态空间形式描述,然后考虑俯仰方向的迟滞非线性模型存在参数不确定性的情况下,利用Lyapunov稳定性理论进行了结构化模型参考自适应控制律设计。仿真结果显示：所设计的控制律能够使开环不稳定的气动弹性系统快速地达到稳定状态。由于最大控制面偏转的存在,来流速度较高时闭环系统仍会发生颤振;根据控制面最大偏转的不同取值,文中给出了闭环临界颤振速度的变化曲线。     

月面车载机械臂的无标定视觉伺服控制方法

针对月面车载机械臂存在的鲁棒性和空间运动轨迹较差的问题,提出了月面车载机械臂的无标定视觉伺服控制方法。对无标定视觉伺服控制系统的控制原理和控制方法进行了分析,提出了双目双轴平行视觉配置方法,选取图像特征空间的点特征和线特征设计控制器,并基于卡尔曼滤波算法实时在线估计机械臂的手眼映射关系。通过仿真试验与基于六轴机械臂无标定视觉伺服平台的地面空间定位模拟实验,验证了该方法的有效性。     

交流伺服系统控制器在线参数自整定方法

伺服系统转动惯量的变化会影响伺服控制性能。为提高伺服性能需要对转动惯量进行在线辨识,实现伺服控制器参数的在线自整定。详细介绍了基于模型参考自适应的转动惯量在线辨识方法,提出了动态调整自适应增益和滤波器时间的方法,有效解决了自适应算法辨识速度和辨识精度的矛盾。根据转动惯量的辨识结果,利用对称优化法则,实时调整伺服控制器参数,以保证控制器动态性能的一致性和鲁棒性。仿真和试验验证了方法的有效性。     

基于射影算子的变循环发动机鲁棒自适应控制器设计

针对变循环航空发动机大范围工作包线下的复杂外部扰动现象引起的多变量鲁棒自适应控制器设计问题,给出基于射影算子的自适应律设计方法并将其应用至模型参考自适应控制器设计中。通过理论分析与变循环航空发动机某一状态点的仿真分析,阐明系统存在外部干扰时传统自适应律的设计方法对于指令跟踪不足等问题,进而在射影算子的理论框架基础上设计自适应律,将存在干扰时的状态误差动态方程的解限制在设定的凸集范围内,从而实现系统状态误差动态方程不大于零的稳定性要求,最终在LQR基准控制器框架上,实现基于射影算子的多变量鲁棒自适应控制器设计,以改善自适应控制系统的鲁棒性。基于该方法对变循环航空发动机进行控制器设计和仿真。结果表明:基于射影算子的自适应律设计方法改善了传统自适应律设计方法的鲁棒性问题,实现了控制系统对外部干扰的有效抑制,当系统各控制回路加入不同外部随机噪声信号时,均达到了期望的控制效果;变循环航空发动机各个状态点均能够跟踪期望的闭环参考指令,各状态点稳态控制误差均小于0.5%,系统超调量小于1%,调节时间小于1.2s,动态跟踪误差不大于0.5%,符合发动机控制系统技术要求。     

Unified Voltage and Pitch Angle Controller for Wind-Driven Induction Generator System

A unified voltage and pitch angle controller (UVPC) for a wind-driven induction generator system is presented to reach effective voltage control and to stabilize generator speed and system frequency. The proposed UVPC comprises a linear optimal controller (LOC), a supplementary voltage regulator (SVR), and a supplementary pitch angle controller (SPC). An ac voltage regulator and a dc voltage regulator are designed in the SVR to generate the desired voltage magnitude and phase angle for the voltage-sourced inverter (VSI). On the other hand, the SPC is designed based on fuzzy logic inference rules in order to generate the desired pitch angle command for the variable blade pitch such that the mechanical power can be controlled in a very efficient manner. When the system is operated in the islanding condition, an approach based on the concept of relative rotating speed has been developed for the measurement of system frequency deviation. Simulation and experimental results reveal that both bus voltage and mechanical power can be effectively controlled by the proposed UVPC for the wind energy conversion system (WECS) either connected with or disconnected from the power grid when there is sufficient wind energy.     

Battery charger design for the Columbus MTFF power system

A novel pulsewidth-modulated (PWM) dc-dc converter topology is proposed for the battery charge regulator (BCR) of the Columbus Man-Tended Free-Flyer (MTFF) power system. The system is a regulated bus system. Bus voltage control is implemented at the input of the BCR. The use of a conventional buck topology with PWM conductance control at the input results in a second-order behavior. A study of new PWM dc-dc converter topologies has been made to attain a suitable topology. The proposed converter topology is designed and a breadboard including the control loop has been built and tested. The experimental results show that the converter operates according to the design constraints.     

RFNN control for PMLSM drive via backstepping technique

A robust fuzzy neural network (RFNN) control system is proposed in this study to control the position of the mover of a permanent magnet linear synchronous motor (PMLSM) drive system to track periodic reference trajectories. First, an ideal feedback linearization control law is designed based on the backstepping technique. Then, a fuzzy neural network (FNN) controller is designed to be the main tracking controller of the proposed RFNN control system to mimic an ideal feedback linearization control law, and a robust controller is proposed to confront the shortcoming of the FNN controller. Moreover, to relax the requirement for the bound of uncertainty term, which comprises a minimum approximation error, optimal parameter vectors and higher order terms in Taylor series, an adaptive bound estimation is investigated where a simple adaptive algorithm is utilized to estimate the bound of uncertainty. Furthermore, the simulated and experimental results due to periodic reference trajectories demonstrate that the dynamic behaviors of the proposed control systems are robust with regard to uncertainties.     

基于模糊控制的永磁同步电机直接转矩控制

设计了基于模糊控制的永磁同步电机(PMSM)直接转矩控制(DTC)系统,采用模糊控制器取代了传统DTC系统中的磁链和转矩滞环比较器及开关表。仿真结果表明:模糊控制PMSM DTC系统运行良好,可实现四象限运行,与传统开关表相比,可有效减小转矩脉动和平均开关频率。研究了转矩误差论域和零电压矢量对模糊控制性能的影响,并基于转矩角对电压矢量作用的影响规律,提出了考虑转矩角的PMSM模糊直接转矩控制。仿真结果表明:在转矩角较大时,考虑转矩角的PMSM模糊DTC系统可有效减小转矩和磁链脉动,改善控制性能。     

基于TS模型的模糊神经网络光伏MPPT控制

为了克服传统最大功率点跟踪(MPPT)方法的一些缺点,使光伏系统更加快速准确地工作在最大功率输出点,提出了基于模糊控制和神经网络控制相结合的自适应控制方法。该方法充分利用模糊神经网络处理非线性问题的优点,通过模糊控制来改变步长,利用神经网络的自学习能力来快速达到平衡,使光伏MPPT在跟踪速度和稳定性之间达到一个较优的平衡。仿真和试验结果表明,基于模糊神经网络自适应控制的MPPT方法具有较强的鲁棒性和自适应能力。     

模糊内模控制的永磁无刷直流电机控制研究

永磁无刷直流电机在日常生活和工业领域中有着广泛的应用。随着控制要求的不断提升,传统双闭环永磁无刷电机控制系统已逐渐无法满足实际需求。针对控制要求,提出了内模控制与模糊控制相结合的驱动方式。在内模控制与双闭环控制相结合的基础上加入了模糊控制,电流环采用内模PI控制,转速环采用内模PI与模糊的共同控制。试验结果表明:基于模糊内模控制的驱动系统在响应速度、超调量、转矩波动等性能方面与传统双闭环控制系统相比,都有明显优势。     

Adaptive Fuzzy Torque Control of Passive Torque Servo Systems Based on Small Gain Theorem and Input-to-state Stability

Passive torque servo system (PTSS) simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturbance is called extra torque. The most important issue for PTSS controller design is how to eliminate the influence of extra torque. Using backstepping technique, adaptive fuzzy torque control (AFTC) algorithm is proposed for PTSS in this paper, which reflects the essential characteristics of PTSS and guarantees transient tracking performance as well as final tracking accuracy. Takagi-Sugeno (T-S) fuzzy logic system is utilized to compensate parametric uncertainties and unstructured uncertainties. The output velocity of actuator identified model is introduced into AFTC aiming to eliminate extra torque. The closed-loop stability is studied using small gain theorem and the control system is proved to be semiglobally uniformly ultimately bounded. The proposed AFTC algorithm is applied to an electric load simulator (ELS), and the comparative experimental results indicate that AFTC controller is effective for PTSS.     

Two-stage open-loop velocity compensating method applied to multi-mass elastic transmission system

In this paper, a novel vibration-suppression open-loop control method for multi-mass system is proposed, which uses two-stage velocity compensating algorithm and fuzzy I ＋ P control- ler. This compensating method is based on model-based control theory in order to provide a damp- ing effect on the system mechanical part. The mathematical model of multi-mass system is built and reduced to estimate the velocities of masses. The velocity difference between adjacent masses is cal- culated dynamically. A 3-mass system is regarded as the composition of two 2-mass systems in order to realize the two-stage compensating algorithm. Instead of using a typical PI controller in the velocity compensating loop, a fuzzy I ＋ P controller is designed and its input variables are decided according to their impact on the system, which is different from the conventional fuzzy PID controller designing rules. Simulations and experimental results show that the proposed veloc- ity compensating method is effective in suppressing vibration on a 3-mass system and it has a better performance when the designed fuzzy I ＋ P controller is utilized in the control system.     

On-line gain-tuning IP controller using RFNN

In this study an integral-proportional (IP) controller with on-line gain-tuning using a recurrent fuzzy neural network (RFNN) is proposed to control the mover position of a permanent magnet linear synchronous motor (PMLSM) servo drive system. The structure and operating principle of the PMLSM are first described in detail. A field-oriented control PMLSM servo drive is then introduced. After that, an IP controller with on-line gain tuning using an RFNN is proposed to control the mover of the PMLSM for achieving high-precision position control with robustness. The backpropagation algorithm is used to train the RFNN on line. Moreover to guarantee the convergence of tracking error for the periodic step-command tracking, analytical methods based on a discrete-type Lyapunov function are proposed to determine the varied learning rates of the RFNN. Furthermore, the proposed control system is implemented in a PC-based computer control system, Finally, the effectiveness of the proposed PMLSM servo drive system is demonstrated by some simulated and experimental results. Accurate tracking response and superior dynamic performance can be obtained due to the powerful on-line learning capability of the RFNN. In addition, the proposed on-line gain-tuning servo drive system is robust with regard to parameter variations and external disturbances     

基于自适应滑模与模糊控制的导弹直接力/气动力复合控制系统优化设计

 针对导弹直接力/气动力复合控制问题,提出了一种基于自适应滑模控制(ASMC)与模糊逻辑的自动驾驶仪设计方法。该方法将整个导弹控制系统分为气动力控制子系统(ACS)和直接力控制子系统(RCS)两部分。前者采用自适应滑模控制理论进行设计,利用其所具有的强鲁棒性优点,克服了包括参数摄动与外界扰动在内的各类不确定性因素的影响。后者通过基于规则的模糊推理来确定不同条件下直接力作用的大小,以辅助提高气动力子系统的性能。在控制系统结构确定的条件下,利用遗传算法(GA)对各参数进行优化,实现了两个子系统之间的协调工作。仿真结果表明,所提出的控制方案对机动指令具有较好的跟踪效果,适用于直接力/气动力复合控制导弹的控制系统设计。