3自由度旋翼飞行机械臂系统动力学建模与预测控制方法

旋翼飞行机械臂(rotorcraft aerial manipulator,RAM)系统是安装在飞行机器人上的可操作型机械臂,悬停模式下执行准确的空中操作时旋翼无人机与所加机械臂之间存在相对扰动,通过分离机械臂与飞行机器人进行动力学建模并不能有效消除这种扰动.本文基于对相互扰动力学作用的分析建立整体动力学模型,并在悬停飞行模式下将其简化为线性控制参考模型.进而对旋翼系统控制延时所引起的动力学扰动进行补偿,同时设计预测控制器来消除末端执行器的位置和姿态误差.最后,在存在内部和外部扰动的情况下,设定销钉插入操作任务进行控制方法的对比仿真.末端执行器位姿偏差的仿真结果表明了模型结构与控制方法的有效性.     

小型无人直升机横纵向模型参数辨识方法

针对小型无人直升机具有多变量、非线性和强耦合的特点,提出一种基于参数辨识的横纵向通道动力学建模方法.该方法根据直升机小扰动运动学方程和小型无人直升机空气动力学特点,推导了小型无人直升机横纵向通道动力学模型.在悬停条件下通过对模型进行简化,得到小型无人直升机横纵向通道待辨识线性耦合模型.根据飞行实验数据,通过采用多变量最小二乘方法估计出该耦合模型的未知参数.模型预测数据和实际飞行数据的比较结果表明,所建模型能充分反映该小型无人直升机在悬停状态下的横纵向通道动力学特性,具有较高精度且结构相对简单,可作为自主飞行控制器设计的参考模型.     

无人旋翼飞行器自适应飞行控制系统设计与仿真

为了设计出能适应不同飞行任务的无人旋翼飞行器飞行控制系统,讨论了模型逆控制器原理.提出了神经网络补偿控制器及其权系数在线算法,分析了综合控制器稳定性.导出了无人旋翼飞行器旋转动力学逆控制器和平移动力学逆控制器,设计了姿态内回路控制器和轨迹外回路控制器,确定了共轴旋翼转速驱动电机的控制分配策略.规划了组合机动飞行科目来模拟自动飞行任务.通过仿真验证了自适应飞行控制系统对无人旋翼飞行器水平垂直运动、悬停和航向运动的飞行控制能力.结果表明,所设计的飞行控制系统具有自适应性和鲁棒性,能实现姿态与轨迹的稳定和跟踪控制.     

倾转旋翼机平均驻留时间切换鲁棒H∞跟踪控制

针对倾转旋翼飞行器模态转换阶段的飞行控制问题,本文给出了倾转旋翼机纵向运动飞行控制系统模型和一种基于参考模型的鲁棒跟踪控制方法.为了保证闭环系统在切换过程中稳定并同时满足指定的鲁棒H∞性能指标,利用状态观测器对系统不可观测状态进行估计,结合模型依赖平均驻留时间方法提出了一种倾转旋翼机切换鲁棒H∞跟踪控制方法,通过求解线性矩阵不等式得到控制器增益,并分析了系统的鲁棒稳定性.仿真结果表明,所提出的方法能够使飞行器系统准确跟踪指令,且对于控制器切换具有鲁棒性.     

输入约束下多旋翼飞行机器人的大范围镇定控制

多旋翼飞行机器人具有良好的飞行稳定性,受到了越来越多的关注.而在某些特殊应用中,如:从较大飞行器上实施空投、瞬态失稳恢复等,往往需要多旋翼飞行机器人从一个高度不稳定的非零初始状态安全、快速地切换到稳定飞行模态,这就是所谓的大范围镇定控制问题.解决该问题面临的主要困难是如何在调节过程中避免飞行机器人进入到输入饱和区,从而引起周期旋转运动,导致系统完全失控.本文针对该问题,以四旋翼飞行机器人为例,详细分析了控制输入约束形式,并对6自由度模型进行了适当简化,构建了二维平面下考虑控制输入约束的非线性动力学模型;在此基础上,基于控制Lyapunov函数概念,提出了一种改进的广义逐点最小范数控制策略,构建了输入约束下的四旋翼飞行机器人大范围镇定控制器.该方法具有明确的解析控制结构,所设计的控制器满足四旋翼飞行机器人的全部控制输入约束.仿真结果表明,对比常规的线性化控制策略,该方法能在考虑控制约束的前提下避免控制器失效,实现四旋翼飞行机器人的大范围渐进稳定.     

自抗扰实现飞翼布局无人机全包线飞行控制

本文以大展弦比飞翼布局无人机为研究对象,基于线性自抗扰控制(linear active disturbance rejection control,LADRC)理论设计了包含内环姿态控制和外环轨迹控制的全包线飞行控制器.在姿态控制中,提出一种抗时滞LADRC控制方法,可以有效解决控制延迟和执行机构动态特性引起的LADRC响应振荡;在轨迹控制中,考虑飞翼布局无人机的气动特性,分别设计了高度、航向、侧向偏离等常用飞行模态的跟踪控制器.仿真结果表明,在气动参数存在不确定性及强风干扰的全包线环境中连续飞行时,所设计的控制器具有较好的控制性能和较强的鲁棒特性.与常规全包线控制方案相比,本文设计的全包线飞行控制器待整定参数较少,参数整定过程相对简单,为进一步的工程应用提供了参考.     

面向抓取作业的飞行机械臂系统及其控制

面向飞行机械臂的飞行抓取作业,提出了一个由六旋翼飞行机器人和7自由度机械臂组成的飞行机械臂系统.系统采用分离式控制策略,即飞行机器人和机械臂各有一个控制器.机械臂运动所引起的系统质心和转动惯量的变化量及其导数被用来估计机械臂对飞行机器人的扰动力和力矩.为了减弱机械臂扰动对六旋翼飞行机器人的飞行控制性能的影响,提出了扰动补偿H∞鲁棒飞行控制器.实验结果表明,与没有扰动补偿的控制器相比,当机械臂运动时所提出的扰动补偿H∞鲁棒控制器对系统的飞行控制性能有明显的提升效果.最后,目标物抓取作业实验验证了所提出的飞行机械臂系统的可靠性.     

基于扰动补偿的无微分模型参考自适应控制系统设计

针对一类含有参数不确定性和未知非线性扰动的系统,本文提出一种基于扰动补偿的无微分模型参考自适应控制方法,实现系统输出对参考模型输出信号的高精度跟踪.首先,利用被控对象模型信息设计扰动估计器,对系统非线性扰动进行在线估计;其次,基于非线性扰动估计值设计参考模型和无微分参数更新律,构建无微分模型参考自适应控制器,建立基于扰动补偿和状态反馈的自适应控制律,以消除参数不确定性和非线性扰动对系统输出的影响,保证系统输出对参考模型输出的准确跟踪;然后,给出闭环系统误差信号收敛条件和控制器参数整定方法;最后,通过数值仿真验证所提方法的有效性和优越性.     

四旋翼无人直升机鲁棒飞行控制

讨论了四旋翼无人直升机的飞行控制问题,提出了一种鲁棒控制器设计方法.该控制器由内环姿态控制器和外环位置控制器两部分组成,姿态控制器采用基于信号补偿的鲁棒控制方法,位置控制器由经典的PD控制实现.将该控制器用于实验室自主研制的四旋翼无人直升机系统,实现了室内悬停飞行.实验结果验证了该控制方法的有效性.     

无人机全包线变增益调参控制律设计

中高空察打一体无人机具有长航时、多任务剖面的飞行特点,针对采用固定控制参数难以满足全包线飞行品质的问题,利用雅克比线性化方法建立了时变系统模型,研究了无人机空速、高度、质量变化对飞行品质的影响特性,设计了纵向和横向的短周期模态控制律,结合样例无人机在的典型工况得到了调参调度表,采用参数拟合的方法设计了基于参数摄动的全局调参控制器。仿真结果表明,所设计的变增益控制器能够有效改善无人机在全包线内的飞行品质,提升了无人机的使用性能。     

A model-free design of reduced-order controllers and application to a DC servomotor

This paper presents a new model-free technique to design fixed-structure controllers for linear unknown systems. In the current control design approaches, measured data are used to first identify a model of the plant, then a controller is designed based on the identified model. Due to errors associated with the identification process, degradation in the controller performance is expected. Hence, we use the measured data to directly design the controller without the need for model identification. Our objective here is to design measurement-based controllers for stable and unstable systems, even when the closed-loop architecture is unknown. This proposed method can be very useful for many industrial applications. The proposed control methodology is a reference model design approach which aims at finding suitable parameter values of a fixed-order controller so that the closed-loop frequency response matches a desired frequency response. This reference model design problem is formulated as a nonlinear programming problem using the concept of bounded error, which can then be solved to find suitable values of the controller parameters. In addition to the well-known advantages of data-based control methods, the main features of our proposed approach are: (1) the error is guaranteed to be bounded, (2) it enables us to avoid issues related to the use of minimization methods, (3) it can be applied to stable and unstable plants and does not require any knowledge about the closed-loop architecture, and (4) the controller structure can be selected a priori, which means that low-order controllers can be designed. The proposed technique is experimentally validated through a real position control problem of a DC servomotor, where the results demonstrate the efficacy of the proposed method.     

一类随机摄动系统的鲁棒控制与仿真

在内模控制(IMC)结构下对一类随机摄动系统的鲁棒控制及其仿真研究作了探讨.首先针对一类随机模型误差的描述定义了一个实际敏感度和标称敏感度之间的加权敏感度误差,然后应用谱分解的方法调整标称控制器来最小化加权敏感度误差在整个频段上的方差,为一类随机摄动系统提供了一种鲁棒控制器设计方法,可使系统期望的标称性能对模型误差具有良好的鲁棒性.最后根据随机摄动系统的特点进行仿真研究,进一步说明了所得控制方法的有效性.     

Adaptive predictive path following control based on least squares support vector machines for underactuated autonomous vessels

Since vessel dynamics could vary during maneuvering because of load changes, speed changing, environmental disturbances, aging of mechanism, etc., the performance of model‐based path following control may be degraded if the controller uses the same motion model all the time. This article proposes an adaptive path following control method based on least squares support vector machines (LS‐SVM) to deal with parameter changes of the motion model. The path following controller consists of two components: the online identification of varying parameters and model predictive control (MPC) using the adaptively identified models. For the online parameter identification, an improved online LS‐SVM identification method is proposed based on weighted LS‐SVM. Specifically, the objective function of LS‐SVM is modified to decrease the errors of parameter estimation, an index is proposed to detect the possible model changes, which speeds up the rate of parameter convergence, and the sliding data window strategy is used to realize the online identification. MPC is combined with the line‐of‐sight guidance to track straight line reference paths. Finally, case studies are conducted to verify the effectiveness of the proposed path following adaptive controller. Typical parameter varying scenarios, such as rudder aging, current variations and changes of the maneuverability are considered. Simulation results show that the proposed method can handle the above situations effectively.     

System identification and improved internal model control for yaw of unmanned helicopter

The sea breeze is a low-frequency disturbance that severely damages the stability of small unmanned helicopters operating over the sea, especially for the yaw control, which is highly sensitive to disturbance. General internal model control is an appropriate method for dealing with this kind of operation conditions, whereas conventional internal model control cannot eliminate the tracking errors between a nominal model and a real model. In coping with unknown dynamics and low-frequency gust disturbances for small helicopters, this paper proposes a novel robust controller constructed with system identification and integrator-based improved general internal model. As a refinement of the conventional frame, the proposed control scheme extends the applicable scope of a controlled plant from a priori known dynamic to an unknown dynamic. Furthermore, under the proposed controller, it is guaranteed that the tracking error between the actual model and the nominal model converges to zero asymptotically. Finally, the effectiveness and advantage of the proposed control scheme are verified through comparative practical flight tests.     

基于故障观测器的多无人机姿态一致性控制

在多个固定翼无人机姿态主从式一致性控制过程中,给出单个固定翼无人机在理想情况下的姿态动力学模型,即名义模型。考虑到无人机在实际运行过程中存在的外部干扰、状态测量误差、控制器微小故障以及无人机实际模型与名义模型之间的偏移,提出一种基于观测器和神经网络的故障检测方法,以实时检测出无人机中存在的故障、模型不确定以及干扰情况。基于无人机名义模型和检测出的故障及干扰,设计主从式多无人机姿态一致性控制器,以实现多无人机姿态的一致性准确跟踪。仿真结果表明,在外部干扰、状态测量误差与控制器微小故障下,与基于神经网络的直接姿态一致性控制器相比,该控制器能够使得无人机的姿态运动状态更接近于期望状态。     

一类不确定系统的最优鲁棒控制

运用最优方法探讨了一类模型不确定系统的鲁棒控制问题。定义了一个实际敏感度和标称敏感度之间的加权敏感度误差,并通过调整标称控制器最小化加权敏感度误差在整个频段上的方差,从而为一类不确定系统提供了一种最优的鲁棒控制器设计方法,可使系统性能对模型误差具有良好的鲁棒性。针对控制工程领域典型的一阶时滞系统进行仿真研究,其结果说明了该方法的有效性。     

Robust output containment control of multi-agent systems with unknown heterogeneous nonlinear uncertainties in directed networks

Output containment problem for high-order nonlinear time-invariant multi-agent systems in directed networks is investigated in this paper. The output is related with the observation matrix. The dimensions of observation matrix are extended so that it is non-singular. Then the containment problem is transformed into stability problem. The model of each agent is constructed by a nominal system combined with uncertainties. A robust controller, which includes a nominal controller and a robust compensator, is proposed to achieve output containment and restrain external uncertainties. The nominal controller is based on the output feedback and the nominal system constructed by the nominal controller contains desired containment properties. The robust compensator design is based on robust signal compensation technology for restraining the effects of external disturbances. A sufficient condition on the output containment is proposed and the containment errors can be made as small as desired with the expected convergence rate. Finally, numerical simulation is presented to demonstrate the effectiveness of the control method.     

A measurement-based approach for designing reduced-order controllers with guaranteed bounded error

The objective of this paper is to present a measurement-based control-design approach for single-input single-output linear systems with guaranteed bounded error. A wide range of control-design approaches available in the literature are based on parametric models. These models can be obtained analytically using physical laws or via system identification using a set of measured data. However, due to the complex properties of real systems, an identified model is only an approximation of the plant based on simplifying assumptions. Thus, the controller designed based on a simplified model can seriously degrade the closed-loop performance of the system. In this paper, an alternative approach is proposed to develop fixed-order controllers based on measured data without the need for model identification. The proposed control technique is based on computing a suitable set of fixed-order controller parameters for which the closed-loop frequency response fits a desired frequency response that meets the desired closed-loop performance specifications. The control-design problem is formulated as a nonlinear programming problem using the concept of bounded error. The main advantages of our proposed approach are: (1) it guarantees that the error between the computed and the desired frequency responses is less than a small value; (2) the difficulty of finding the globally optimal solution in the error minimisation problem is avoided; (3) the controller can be designed without the use of any analytical model to avoid errors associated with the identification process; and (4) low-order controllers can be designed by selecting a fixed low-order controller structure. To experimentally validate and illustrate the efficacy of the proposed approach, proportional-integral measurement-based controllers are designed for a DC (direct current) servomotor.     

多输入多输出变量带误差模型的最坏情况频域辨识

本文将单输入单输出(SISO)变量带误差(EIV)模型的频域最坏情况辨识方法推广应用于多输入多输出 (MIMO)情况. 类似于SISO情况, 多输入多输出变量带误差(MIMO EIV)模型的辨识模型集合由估计的系统名义模型及 其最坏情况误差界描述. 所估计的系统名义模型表征为正规右图符号, 其最坏情况误差界具有可能的更少保守性, 可利 用EIV 模型的先验信息和后验信息由v-gap度量量化得到. 因此, 这种模型集合非常适合于后期利用Vinnicombe提出 的H1回路成形法设计鲁棒控制器. 最后, 利用一数值仿真实例验证所提出辨识方法的有效性.     

Modelling and uncertainties characterization for robust control

In this work, multi-input multi-output (MIMO) process identification is studied, where the model identification is dedicated to the control design goal. An ad hoc identification procedure is presented which allows estimating not only a nominal parametric process model, but also a bound of the model uncertainty (i.e. modelling errors). The model structure is defined in a way that the identified nominal model and the uncertainties can readily be used for the analysis and design of a robust control system by means of many of the techniques available in the literature. Simulation examples are given to illustrate the method.