基于不确定性机器人的跟踪控制策略研究

针对不确定刚体连杆机器人在动力学建模时参数不精确以及未被考虑的干扰因素可能引起的控制系统品质的恶化问题,提出一种轨迹跟踪控制方案。控制器由基于标称模型设计的计算力矩控制器和基于不确定性因素设计的鲁棒跟踪补偿控制器组成,结构比较简单。计算力矩控制器使标称系统跟踪期望轨迹;鲁棒补偿控制器则用于消除参数误差带来的不确定性影响。理论分析和仿真证明了控制方法的有效性。     

空间绳系机器人目标抓捕鲁棒自适应控制器设计

针对空间绳系机器人（Tethered space robot,TSR）目标抓捕过程中的稳定控制问题,建立空间绳系机器人系统模型,根据阻抗控制原理,设计基于位置的阻抗控制方法;针对空间绳系机器人系统的模型不确定性问题,利用神经网络对不确定性进行估计补偿,设计鲁棒项对空间系绳干扰和神经网络估计误差的影响进行抑制,在此基础上设计空间绳系机器人目标抓捕鲁棒自适应稳定控制器,并进行稳定性证明.最后对设计的控制器进行仿真验证.作为对比,对无鲁棒项自适应的稳定控制器进行仿真.仿真结果表明,设计的基于阻抗控制的鲁棒自适应控制可以实现对空间绳系机器人目标抓捕过程中的稳定控制,与无鲁棒项自适应的稳定控制器仿真结果相比,本文采用的鲁棒自适应控制方法可以有效地对不确定性进行补偿,控制过程中超调量更小,收敛时间更短,并且控制精度更高.     

机器人的鲁棒预测控制

本文提出基于误差预测的机器人鲁棒控制器。考虑到机器人的动力学建模误差影响其控制性能,本文建立机器人的误差模型,给出预测建模误差对运动轨迹偏差的作用的有效方法,并提出建模误差的鲁棒性补偿。本文分别在关节空间和直角空间针对冗余机器人和非冗余机器人提出鲁棒预测控制器设计,其有效性由仿真例子检验。     

基于Sigmoid函数的机器人鲁棒滑模跟踪控制系统设计

为了保证机器人能够在保持稳定的情况下,按照规划轨迹执行工作任务,从硬件和软件两个方面,设计了基于Sigmoid函数的机器人鲁棒滑模跟踪控制系统。装设机器人传感器与状态观测器,改装机器人鲁棒滑模跟踪控制器,完成系统硬件设计;综合机器人结构、运动机理和动力机制3个方面,构建机器人数学模型;根据状态数据采集结果与规划轨迹之间的偏差,计算机器人跟踪控制量;依据滑模运动与切换方程,利用Sigmoid函数生成机器人鲁棒滑模控制律,将生成控制指令作用在机器人执行元件上,实现系统的鲁棒滑模跟踪控制功能;在系统测试与分析中,所设计控制系统的平均位置跟踪控制误差为0.93 mm,与设定轨迹目标基本重合,机器人姿态角跟踪控制误差为0.06 mm,具有较好的鲁棒滑模跟踪控制效果,能够有效提高机器人鲁棒滑模跟踪控制精度。     

机器人轨迹跟踪的自适应模糊神经网络控制

研究机器人跟踪轨迹控制问题,针对模型未知的机器人系统,为提高跟踪精度和控制性能,提出了一种基于T-S型模糊RBF神经网络的H∞轨迹跟踪控制方法,用模糊神经网络为模型未知的机器人系统建模,克服了系统鲁棒性差,对机动目标跟踪性能差等缺点。然后设计自适应控制器,将H∞控制理论与模糊神经网络有机地结合起来,借助鲁棒补偿项将建模误差及外部干扰衰减到期望的程度以下,而控制器与改进Elman神经网络的结合,便于处理建模有界干扰以及非结构化的未建模的动力学,并进行仿真。仿真结果表明了所提出的控制算法的可行性。     

具有输入齿隙的一类非线性不确定系统自适应鲁棒控制

针对一类具有未知输入齿隙、参数不确定以及未建模动态和干扰的非线性系统,设计了自适应鲁棒控制器.将齿隙非线性模型等价表示为具有有界建模误差的全局线性化模型,在此基础上设计了包含自适应模型补偿、反馈稳定和鲁棒反馈3部分的自适应鲁棒控制器,并给出了系统动态跟踪误差和稳态误差指标.理论分析证明,闭环控制系统信号有界且跟踪误差在任意期望的精度范围内,仿真研究验证了所提出方法的有效性.     

机器人鲁棒控制器参数演化设计

论文将讨论具有控制输入幅值限制的机器人轨迹跟踪控制问题。将利用基于信号补偿的鲁棒控制方法设计机器人的子关节系统控制器。该控制器由标称控制器和鲁棒补偿器组成。标称控制器对于一标称受控对象实现所希望的轨迹跟踪特性,鲁棒补偿器则用于减小实际受控对象和标称受控对象之间的差异对跟踪特性的影响。当输入存在饱和约束的情况下,对鲁棒补偿器进行了修改,并且基于演化寻优的方法求取鲁棒补偿器参数。     

考虑状态约束与执行器饱和的水下机器人轨迹跟踪控制

针对执行器饱和、模型参数不确定以及海流干扰等因素影响下的水下机器人,提出一种考虑状态约束以及执行器饱和的轨迹跟踪控制器.首先,构建水下机器人水平面轨迹跟踪误差方程;然后,对载体模型参数不确定性产生的模型误差以及海流干扰,设计一个非线性观测器进行估计并用于对控制器进行扰动补偿;接着,引入执行器饱和补偿系统、二阶滤波器以及滤波器误差补偿系统,设计命令滤波反步滑模控制器来控制水下机器人的水平面轨迹跟踪;最后,严格验证命令滤波反步滑模控制器的稳定性并进行数值仿真,验证所提出控制器的有效性.     

基于耗散理论的机器人神经网络鲁棒控制

为了更好地解决机器人系统中存在的参数不确定和外部干扰的鲁棒控制问题,提出一种基于耗散性理论的神经网络自适应鲁棒控制器,首先应用无源性理论对名义模型设计镇定控制器,然后利用RBF神经网络自适应学习系统的不确定部分,将神经网络逼近误差作为外部干扰,基于H∞控制理论使干扰对系统输出的影响抑制到所要求的最小程度,并用Lyapunov稳定性理论推导出RBF神经网络的权重矩阵调节律以及相关的鲁棒控制器,证明了系统的全局稳定性.仿真结果表明,这种控制器对机器人系统可能受到的干扰具有较好的抑制能力,提高了系统的鲁棒性,实现了系统轨迹的快速准确跟踪,又能很好地消除控制器的抖振,进而提高机器人工作性能.     

欠驱动AUV模糊神经网络L2增益鲁棒跟踪控制

提出基于模糊神经网络欠驱动水下自主机器人(AUV)的L2增益鲁棒跟踪控制方法,该方法通过在线学习逼近动力学模型的不确定项.控制器克服了由于缺少横向推力对跟踪误差的影响,在考虑未知海流干扰情况下,实现了系统对模糊神经网络逼近误差的L2增益小于γ.利用Lyapunov稳定性理论证明了闭环控制系统误差信号一致最终有界.最后,通过精确模型参数和参数扰动仿真实验验证了该控制方法具有很好的跟踪效果和较强的鲁棒性.     

基于神经网络的自由漂浮空间机器人关节空间鲁棒跟踪控制

针对存在不确定性以及干扰的自由漂浮空间机器人关节空间轨迹跟踪问题,提出了一种基于鲁棒控制思想的神经网络鲁棒控制方法.对于控制器中由系统惯性参数不确定性引起的非线性不确定项,利用径向基函数(RBF)神经网络进行逼近,并且利用鲁棒控制器使系统镇定并保证从干扰到跟踪误差的增益小于或等于给定的指标.最后,对本文提出的控制方案进...     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

基于光流的四旋翼直升机鲁棒自主着陆控制

针对小型四旋翼无人机自主着陆问题,提出了一种基于光流的高度估计方法和基于信号补偿的高度鲁棒控制器设计方法.首先从通用光流运动模型出发,采用奇异值分解方法求解无人机线速度和深度的比值,通过对垂直速度和高度比值积分获得高度数据.其次,将考虑地效影响和其它不确定性的高度通道非线性模型分解为标称线性模型和等效扰动两部分,并设计基于信号补偿的高度鲁棒控制器,该控制器由标称控制器和鲁棒补偿器组成,其中标称控制器使得标称闭环系统达到期望的高度跟踪特性,鲁棒补偿器用于抑制等效扰动的影响.最后从理论上证明了该控制器可以保证高度跟踪误差在有限时间内收敛至指定的原点邻域内.四旋翼无人机自主着陆的实验结果验证了所提出的基于光流的高度估计和鲁棒控制方法的有效性.     

含有摩擦补偿的全方位移动机器人自抗扰控制

本文针对全方位移动机器人轨迹追踪中的摩擦补偿问题,提出了一种改进的非线性自抗扰控制器.首先建立了含有经典静态摩擦模型的全方位移动机器人动力学模型.其次,基于该模型设计非线性控制器和线性扩张状态观测器并给出了系统的稳定性分析.通过将模型已知项加入线性扩张状态观测器中得到摩擦力的估计值,并将估计值用于非线性控制器中摩擦补偿部分.为减小摩擦力对机器人低速运动轨迹追踪控制的影响,非线性控制器采用变增益控制器进行轨迹追踪控制.最后通过仿真结果验证本文提出控制器的有效性.     

Robust dynamic surface control of flexible joint robots using recurrent neural networks

A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H-infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dynamic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally, simulation results verify the effectiveness of the proposed control scheme.     

On the Robust Nonlinear Motion Position and Force Control of Flexible Joints Robot Manipulators

The design of a robust nonlinear position and force controller for a flexible joints robot manipulator interacting with a rigid environment is presented. The controller is designed using the concept of feedback linearization, sliding mode techniques, and LQE estimation methodologies. It is shown that the nonlinear robot manipulator model is feedback linearizable. A robust performance of the proposed control approach is achieved by accounting for the system parameters uncertainties in the derivation of the nonlinear control law. An upper bound of the error introduced by parametric uncertainties in the system is computed. Then, the feedback linearizing control law is modified by adding a switching action to compensate the errors and to guarantee the achievement of the desired tracking performance. The relationship between the minimum achievable boundary layer thickness and the parametric uncertainties is derived. The proposed controller is tested using an experimental flexible joints robot manipulator, and the results demonstrate its potential benefits in reducing the number of sensors required and the complexity of the design. This is achieved by eliminating the need for nonlinear observers. A robust performance is obtained with minimum control effort by taking into account the effect of system parameter uncertainties and measurement noise.     

Robust chattering-free sliding mode control of space robot in task space

This paper studies the tracking control problem of a free-floating space robot in a task space. Considering the model uncertainties and external disturbance, a robust sliding mode controller is proposed using the Lyapunov direct method and dissipative theory. To eliminate the chattering phenomenon, an radial basis function （RBF） neural network is applied to replace the discontinuous part of the control signal. A novel on-line learning method of the weights and parameters of the RBF neural network established using Lyapunov function assures the stability of the system. It is proved that the proposed controller can guarantee that the L2 gain from disturbance to tracking error is lower than the given index y. Simulation results show that the control method is valid.     

Robust finite-time tracking control of nonholonomic mobile robots without velocity measurements

The problem of robust finite-time trajectory tracking of nonholonomic mobile robots with unmeasurable velocities is studied. The contributions of the paper are that: first, in the case that the angular velocity of the mobile robot is unmeasurable, a composite controller including the observer-based partial state feedback control and the disturbance feed-forward compensation is designed, which guarantees that the tracking errors converge to zero in finite time. Second, if the linear velocity as well as the angular velocity of mobile robot is unmeasurable, with a stronger constraint, the finite-time trajectory tracking control of nonholonomic mobile robot is also addressed. Finally, the effectiveness of the proposed control laws is demonstrated by simulation.     

基于干扰观测器的一类不确定仿射非线性系统有限时间收敛backstepping控制

针对一类不确定仿射非线性系统的跟踪控制问题,提出一种基于干扰观测器的有限时间收敛backstepping控制方法.为增强小脑模型(CMAC)泛化和学习能力,将非对称高斯函数和模糊理论相结合,给出非对称模糊CMAC结构,设计干扰观测器实现系统未知复合干扰在线准确逼近;基于非对称模糊CMAC干扰观测器,给出有限时间收敛backstepping控制器设计步骤,利用Lyapunov稳定理论证明闭环系统稳定性,其中采用非线性微分器获取虚拟控制量滤波和微分信息以避免backstepping设计中的微分“膨胀问题”,设计辅助系统修正因微分器带来的误差对系统跟踪性能影响,引入基于障碍型函数的自适应滑模鲁棒项抑制复合干扰估计偏差对跟踪误差的影响;将所提方法应用于无人机飞行控制仿真实验,结果表明所提方法的有效性.     

Internal model based robust inversion feedforward and feedback 2DOF control for LPV system with disturbance

To reduce the adverse effects on the control performance and disturbance rejection caused by system uncertainty, a novel internal model based robust inversion feedforward and feedback 2DOF control approach was proposed for LPV system with disturbance. The proposed control approach combines the internal model control and robust inversion based 2DOF control, it utilizes internal model based control to reject external disturbance, utilizes robust inversion 2DOF control to enhance the control resolution and guarantee the system control performance. At first, a LMI synthesis approach for LPV system model identification and a disturbance compensator optimization design method which could minimize H_∞ norm of output error caused by disturbance are presented. Then, combined with internal loop for disturbance compensation, a robust inversion feedforward controller is designed by robust inversion approach and the feedback controller which could render the requirements of reference signal tracking performance and robustness satisfied is obtained by the H_∞ mixed sensitivity synthesis approach. Finally, atomic force microscopy (AFM) vertical positioning simulation experiments are conducted and the experiment results showed that the proposed control approach could achieve better output performance and disturbance rejection compared with conventional internal model based control and robust inversion based 2DOF control approach.