基于扰动观测器的机械臂自适应反演滑模控制

机械臂的动力学模型通常包含一定的结构不确定性,并受到外界未知干扰的影响。针对现有模型的不确定性特点,提出了一种基于非线性扰动观测器的自适应反演滑模控制方法,解决机械臂的轨迹跟踪控制问题。对于外界干扰,利用非线性扰动观测器进行观测补偿,无需上界先验知识;对于结构不确定性,引入反演滑模控制,同时设计自适应律,保证闭环系统的稳定性并增强系统的动态适应性。仿真结果证明,所提出的方法可以有效克服系统不确定性,降低控制输入信号的抖振,最终实现期望轨迹的快速精确跟踪。     

高超飞行器的再入非线性鲁棒控制

针对再入段高超飞行器非线性动力学模型存在不确定性和干扰,基于奇异摄动理论提出了鲁棒变结构+动态逆内外环解耦控制方法.为避免在线实时求逆,控制系统的外环基于简化的模型设计自适应滑模变结构控制律,通过反馈干扰观测器在线估计广义干扰量,实现角度的跟踪和闭环系统的稳定,抑止外来干扰.强耦合的姿态动力学内环采用动态逆跟踪角速度指...     

无人驾驶飞行器姿态的非线性扰动抑制控制

本文研究了无人驾驶飞行器(unmanned aerial vehicle,UAV)的姿态跟踪控制问题.针对在飞行器姿态跟踪时存在的系统模型不确定性和外界扰动,提出了一种基于四元数的姿态跟踪控制方法,基于UAV的姿态误差模型分别设计系统的观测器和控制器.首先,以四元数为姿态参数建立系统的非线性误差模型;在此基础之上,设计一种非线性干扰观测器(nonlinear disturbance observer,NDOB)用以在线估计误差模型中的复合扰动,并在控制输入端进行相应的补偿.然后通过设计非线性广义预测控制律设镇定误差系统,实现姿态跟踪.最后基于频域理论分析了非线性干扰观测器的扰动抑制性能.仿真与实验结果表明本文提出的方法在系统存在复合扰动的情况下能使系统姿态有效的跟踪期望值.     

基于广义扩张状态观测器的遥操作系统同步控制

针对机械臂遥操作系统中存在的时变时延问题,提出了基于广义扩张状态观测器的控制方法,实现了遥操作系统稳定并且主从机械臂关节角位置同步的控制目标。首先通过反馈线性化,将遥操作系统的主从机械臂动力学模型转化为一个关于位置跟踪误差和时延的状态空间模型。针对该多输入多输出的干扰不匹配模型,设计了广义扩张状态观测器和相应的控制律,从而消除了时变时延以及其它扰动引起的不确定性对系统的影响,并对系统进行稳定性和抗扰性分析。最后,通过仿真验证了所设计的控制方法的有效性。     

改进非线性干扰观测器的机械臂自适应反演滑模控制

针对传统滑模和传统干扰观测器在机械臂关节位置跟踪中存在的控制输入抖振、需要测量加速度项、应用模型受限等问题,提出一种改进非线性干扰观测器的机械臂自适应反演滑模控制算法。首先,设计改进的非线性干扰观测器进行在线测试,在滑模控制律中加入干扰估计值对可观测的干扰进行补偿;然后选择合适的设计参数,使观测误差指数型收敛;其次,引入反演自适应控制律,对不可观测的干扰进行估计,进一步改善控制系统的跟踪性能;最后,利用李雅普诺夫函数验证了闭环系统的渐近稳定性,并将其应用于机械臂关节位置跟踪。实验结果表明,与传统滑模算法比较,所提控制算法不但加快了系统的响应速度,而且能有效地削弱系统抖振、避免测量加速度项并扩大应用模型使用范围。     

基于干扰观测器的机械臂改进趋近律的滑模控制

针对机械臂滑模控制中存在抖振的问题以及对外界干扰较为敏感的特性,设计了一种基于干扰观测器的机械臂改进趋近律的滑模控制策略。该策略在指数趋近律中引入饱和函数,提出了一种改进趋近律;根据系统动力学模型,对系统的不确定性和外部干扰采用干扰观测器进行观测;在此基础上设计系统滑模控制律,控制律对未观测的干扰进行补偿。仿真结果表明,控制策略能有效地抑制轨迹跟踪中的抖振问题,较好地克服外界干扰,保证系统的控制性能。     

基于观测器的机器人动态面控制

针对带干扰项的刚性机械臂系统,提出一种基于观测器与动态面技术的控制器设计方案.该方案只需测量机械臂位置,而无需测量角速度.首先应用状态观测器估计不可测量的关节角速度,然后把这一观测量引入动态面设计过程中,从而得到了系统控制律.与基于后推的机器人控制比较,所设计的控制器结构更为简单.应用Lyapunov方法,分析了观测误差和系统跟踪误差的有界性与收敛性,并且证明了闭环系统所有信号一致终结有界.仿真结果表明该控制方案具有良好的跟踪性能.     

卫星姿态直接自适应模糊预测控制

对具有模型不确定性和未知外干扰的卫星姿态系统提出了多输入多输出直接自适应模糊预测跟踪控制设计方法. 此方法先基于卫星姿态动力学模型设计出非线性广义预测控制律, 再构造直接自适应模糊控制器逼近预测控制律中因模型不确定性引起的未知项. 文中证明了所设计的控制律能使卫星跟踪给定的期望姿态轨迹, 跟踪误差收敛到原点的小邻域内. 仿真结果验证了此方法的有效性.     

基于不确定逼近的机械手自适应鲁棒预测控制

针对具有参数不确定性和未知外部干扰的机械手轨迹跟踪问题提出了一种多输入多输出自适应鲁棒预测控制方法. 首先根据机械手模型设计非线性鲁棒预测控制律, 并在控制律中引入监督控制项; 然后利用函数逼近的方法逼近控制律中因模型不确定性以及外部干扰引起的未知项. 理论证明了所设计的控制律能够使机械手无静差跟踪期望的关节角轨迹. 仿真验证了本文设计方法的有效性.     

空间柔性机械臂高阶滑模干扰观测器轨迹跟踪

在空间漂浮状态时机械臂载体姿态均不受控的条件下,针对机械臂在关节空间内轨迹跟踪及振动抑制等问题,提出了一种基于空间机械臂改进高阶滑模干扰观测器控制策略。利用拉格朗日方程及动量守恒定律建立机械臂运动学数学模型,对误差估计值进行修正,设计干扰观测器,根据机械臂末端轨迹设计合理的高阶滑模面及滑模函数。针对设计的观测器进行模拟仿真实验,并改变理想轨迹,使其在复杂曲线中运行。仿真结果表明:高阶滑模干扰观测器可应用于更为复杂的工作空间中,并更有效、精确地实现了轨迹跟踪任务。     

Visual servoing tracking control of uncalibrated manipulators with a moving feature point

The paper is concerned with the problem of uncalibrated visual servoing robots tracking a dynamic feature point along with the desired trajectory. A nonlinear observer and a nonlinear controller are proposed, which allow the considered uncalibrated visual servoing robotic system to fulfil the desired tracking task. Based on this novel control method, a dynamic feature point with unknown motion parameters can be tracked effectively along with the desired trajectory, even with multiple uncertainties existing in the camera, the kinematics and the manipulator dynamics. By the Lyapunov theory, asymptotic convergence of the image errors to zero with the proposed control scheme is rigorously proven. Simulations have been conducted to verify the performance of the proposed control scheme. The results demonstrated good convergence of the image errors.     

Neural network-based adaptive controller design of roboticmanipulators with an observer

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies     

力矩受限的柔性空间机器人模糊神经网络自适应跟踪控制及振动抑制

针对力矩受限和存在参数不确定情况下,自由漂浮柔性空间机器人（FFFSR）关节轨迹跟踪控制与柔性振动抑制的问题,利用奇异摄动法将系统分解为关节轨迹跟踪的慢变子系统和描述柔性振动的快变子系统,进而提出含慢、快变控制项的组合控制器。对于慢变子系统,设计一种无需模型的模糊径向基函数（RBF）神经网络（FRBFNN）自适应跟踪控制方案,利用神经网络观测器估计关节角速度信息,并对系统的未知非线性函数进行逼近;对于快变子系统,采用扩张状态观测器（ESO）对不易测量的柔性模态坐标导数和不确定扰动进行估计,并结合线性二次调节器（LQR）方法抑制柔性振动。数值仿真结果表明,当控制力矩限制在±20 N·m和±10 N·m范围内时,该组合控制器能够在2.5 s实现稳定的关节轨迹跟踪,并将柔性振动幅值限制在±1×10^-3 m内。     

Nonlinear disturbance observer design for robotic manipulators

Robotic manipulators are highly nonlinear and coupled systems that are subject to different types of disturbances such as joint frictions, unknown payloads, varying contact points, and unmodeled dynamics. These disturbances, when unaccounted for, adversely affect the performance of the manipulator. Employing a disturbance observer is a common method to reject such disturbances. In addition to disturbance rejection, disturbance observers can be used in force control applications. Recently, research has been done regarding the design of nonlinear disturbance observers (NLDOs) for robotic manipulators. In spite of good results in terms of disturbance tracking, the previously designed nonlinear disturbance observers can merely be used for planar serial manipulators with revolute joints [Chen, W. H., Ballance, D. J., Gawthorp, P. J., O'Reilly, J. (2000). A nonlinear disturbance observer for robotic manipulators. IEEE Transactions on Industrial Electronics, 47 (August (4)), 932–938; Nikoobin, A., Haghighi, R. (2009). Lyapunov-based nonlinear disturbance observer for serial n-link manipulators. Journal of Intelligent & Robotic Systems, 55 (July (2–3)), 135–153]. In this paper, a general systematic approach is proposed to solve the disturbance observer design problem for robotic manipulators without restrictions on the number of degrees-of-freedom (DOFs), the types of joints, or the manipulator configuration. Moreover, this design method does not need the exact dynamic model of the serial robotic manipulator. This method also unifies the previously proposed linear and nonlinear disturbance observers in a general framework. Simulations are presented for a 4-DOF SCARA manipulator to show the effectiveness of the proposed disturbance observer design method. Experimental results using a PHANToM Omni haptic device further illustrate the effectiveness of the design method.     

Stability and control aspects of flexible link robot manipulators during constrained motion tasks

The effect of robotic manipulator structural compliance on system stability and trajectory tracking performance and the compensation of this structural compliance has been the subject of a number of publications for the case of robotic manipulator noncontact task execution. The subject of this article is the examination of dynamics and stability issues of a robotic manipulator modeled with link structural flexibility during execution of a task that requires the robot tip to contact fixed rigid objects in the work environment. The dynamic behavior of a general n degree of freedom flexible link manipulator is investigated with a previously proposed nonlinear computed torque constrained motion control applied, computed based on the rigid link equations of motion. Through the use of techniques from the theory of singular perturbations, the analysis of the system stability is investigated by examining the stability of the “slow” and “fast” subsystem dynamics. The conditions under which the fast subsystem dynamics exhibit a stable response are examined. It is shown that if certain conditions are satisfied a control based on only the rigid link equations of motion will lead to asymptotic trajectory tracking of the desired generalized position and force trajectories during constrained motion. Experiments reported here have been carried out to investigate the performance of the nonlinear computed torque control law during constrained motion of the manipulator. While based only on the rigid link equations of motion, experimental results confirm that high-frequency structural link modes, exhibited in the response of the robot, are asymptotically stable and do not destabilize the slow subsystem dynamics, leading to asymptotic trajectory tracking of the overall system. © 1992 John Wiley & Sons, Inc.     

基于观测器的机械手神经网络自适应控制

提出了一种基于观测器的机械手神经网络自适应轨迹跟随控制器设计方法,这里机 械手的动力学非线性假设是未知的,并且假设机械手仅有关节角位置测量.文中采用一个线 性观测器重构机械手的关节角速度,用神经网络逼近修正的机械手动力学非线性,改进系统 的跟随性能.基于观测器的神经网络自适应控制器能够保证机械手角跟随误差和观测误差的 一致终结有界性以及神经网络权值的有界性,最后给出了机械手神经网络自适应控制器-观 测器设计的主要理论结果,并通过数字仿真验证了所提方法的性能.     

基于不确定逼近的机械手间接自适应鲁棒预测控制

为了使机械手系统在含有模型不确定项时具有良好的跟踪性能和较强的抗干扰能力,提出了一种间接自适应鲁棒预测控制。首先,针对机械手模型设计出非线性鲁棒预测控制器;然后,基于三次样条函数逼近控制律中因模型不确定性产生的未知项,并在控制律中引入一个D-控制项抑制外部干扰。理论证明了所设计的控制器能够使跟踪误差收敛到原点。仿真验证了所提方法的有效性。     

Observer-Based Adaptive Controller Design of Flexible Manipulators Using Time-Delay Neuro-Fuzzy Networks

In this paper, a dynamical time-delay neuro-fuzzy controller is proposed for the adaptive control of a flexible manipulator. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity. For a perfect tracking control of the robot, the output redefinition approach is used in the adaptive controller design using time-delay neuro-fuzzy networks. The time-delay neuro-fuzzy networks with the rule representation of the TSK type fuzzy system have better learning ability for complex dynamics as compared with existing neural networks. The novel control structure and learning algorithm are given, and a simulation for the trajectory tracking of a flexible manipulator illustrates the control performance of the proposed control approach.