不确定性机器人的神经网络补偿控制

本文研究具有不确定性的机器人的轨迹跟踪控制问题。提出了一种由计算力矩控制器和神经网络补偿控制器构成的控制方案。探讨了一种用神经网络估计机器人系统不确定性的途径。给出了神经补偿控制器的设计方法，并证明了闭环系统的收敛性。仿真结构表明所提方案具有很好的鲁棒性和抗干扰能力。     

基于外环速度补偿的封闭机器人确定学习控制

针对未开放力矩控制接口的一类封闭机器人系统,提出一种基于外环速度补偿的确定学习控制方案.该控制方案考虑机器人受到未知动力学影响,且具有未知内环比例积分(Proportional-integral, PI)速度控制器.首先,利用宽度径向基函数(Radial basis function, RBF)神经网络对封闭机器人的内部未知动态进行逼近,设计外环自适应神经网络速度控制指令.在实现封闭机器人稳定控制的基础上,结合确定学习理论证明了宽度RBF神经网络的学习能力,提出基于确定学习的高精度速度控制指令.该控制方案能够保证被控封闭机器人系统的所有信号最终一致有界且跟踪误差收敛于零的小邻域内.在所提控制方案中,通过引入外环补偿控制思想和宽度神经网络动态增量节点方式,减小了设备计算负荷,提高了速度控制下机器人的运动性能,解决了市场上封闭机器人系统难以设计力矩控制的难题,实现了不同工作任务下的高精度控制.最后数值系统仿真结果和UR5机器人实验结果验证了该方案的有效性.     

自适应神经变结构的机器人轨迹跟踪控制

提出一种神经网络与变结构融合的控制策略用于非线性机器人控制,该方案利用神经网络来自适应补偿不确定模型,并通过变结构控制器消除逼近误差.考虑到局部泛化网络的不足,根据其状态空间的划分,分别对3个区间采用神经网络与变结构的分级与集成控制.该方案能在控制阶段初期及网络逼近区域外使两种控制器共同起作用以保持系统的强鲁棒性,基于Lyapunov理论证明了闭环系统的全局稳定性.仿真结果进一步表明了该方法的优越性.     

不确定机器人的神经网络轨迹控制

针对不确定机器人的轨迹跟踪问题,提出了一种基于自适应神经网络的控制方案.对于系统中的各种未知非线性,通过RBF神经网络和变结构光滑集成的控制器来自适应学习并且补偿,这种控制器克服了局部泛化网络的不足,提高了控制精度及其收敛速度.而且在考虑神经网络失效的情况下,仍能保证系统具有良好的鲁棒性.网络权重的自适应修正规则基于Lyapunov函数方法得到,它保证了跟踪误差的全局渐进稳定性.试验结果证明了这种控制算法的有效性.     

基于滑模变结构的空间机器人神经网络跟踪控制

研究了在无需模型估计值的情况下不确定空间机器人轨迹跟踪问题,提出了滑模变结构的神经网络控制方案.首先基于Lyapunov理论设计了一种径向基函数（RBF）神经网络控制器来补偿系统中的未知非线性,该神经控制器能够保证闭环系统的稳定性,而通过利用饱和函数把神经网络和滑模控制结合起来的控制器来不仅可以进一步削弱滑模控制输入的抖振,且当神经网络控制器无效时仍能保证系统鲁棒性.仿真结果证明了该控制器能在初期及强干扰情况下均能达到较好的控制效果.     

基于神经网络的无人机负载模拟器的复合控制

衡量负载模拟器系统性能的关键指标是多余力矩的抑制。针对无人机负载模拟器系统的非线性及多余力矩强扰动的特点,依据神经网络的非线性逼近和自学习特性,提出了一种基于神经网络和前馈相结合的复合控制器,用来提高系统的性能。复合控制器利用前馈来补偿定常多余力,利用神经网络进行在线辨识、控制来补偿系统的非线性部分,很好地抑制了多余力矩。该文给出了具体的控制结构和算法。仿真结果还表明该方法极大地改善了系统动态加载性能,有很强的鲁棒性。     

基于连杆力矩传感器动态补偿的机器人灵巧手笛卡儿阻抗控制

为了对连杆空间力矩传感器进行动态补偿,提出了适用于求取串联机器人任意连杆中任意一点处所受的内力和内力矩的算法.该算法采用连杆假想截断原理利用牛顿-欧拉方程推导而出.推导过程综合考虑了串联机器人是否处于静态以及末端是否受外力作用的情况,以及串联机器人的关节是否是回转关节的情况.然后利用该算法计算动态补偿值,构建了基于连杆力矩传感器动态补偿的笛卡儿阻抗控制器.最后在HIT/DLR Hand II五指灵巧手上进行了实验验证.实验结果一方面验证了该算法的有效性,另一方面也验证了本文所构建的笛卡儿阻抗控制器的有效性.     

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。     

直接驱动机器人控制算法的试验研究和比较

本文针对直接驱动机器人轨线跟踪进行了控制方法实验比较研究，为了解决实时控制的运算量问题，首先建立了一个基于Ｔｒａｎｓｐｕｔｅｒ的机器人并行控制器，实现前馈补偿、计算力矩、滑动模以及知适应等几种典型的控制方案，据此给出一些比较结果。     

神经网络自适应滑模控制的不确定机器人轨迹跟踪控制

提出一种针对机器人跟踪控制的神经网络自适应滑模控制策略。该控制方案将神经网络的非线性映射能力与滑模变结构和自适应控制相结合。对于机器人中不确定项,通过RBF网络分别进行自适应补偿,并通过滑模变结构控制器和自适应控制器消除逼近误差。同时基于Lyapunov理论保证机器手轨迹跟踪误差渐进收敛于零。仿真结果表明了该方法的优越性和有效性。     

采用神经元补偿器的机械手计算转矩控制算法

在本文中,我们提出了一种神经网络控制方法,以增 强在机械手控制中普遍使用的计算转矩控制结构．本算法将计算转矩控制结构,和神经网络 补偿结构有效地结合起来,使得在不增加控制结构复杂性的基础上,大大增强了控制的鲁棒 性．由于神经元补偿器具有很强的自适应性,因此在整个控制算法中无需事先精确了解机器 人的动力学参数和结构,而且在操作中变化的参数也能得到很好的补偿．这种算法还有一个 突出的优点就是,神经元补偿器作为前馈控制回路中的一个独立部分,使得整个控制系统能 够实现多速率采样控制结构．     

Decoupling and tracking control for elastic joint robots with coupled joint structure

The paper deals with the modeling, identification, and control of a flexible joint robot developed for medical applications at the German Aerospace Center (DLR). In order to design anthropomorphic kinematics, the robot uses a coupled joint structure realized by a differential gearbox, which however leads to strong mechanical couplings inside the coupled joints and must be taken into account. Therefore, a regulation MIMO state feedback controller based on modal analysis is developed for each coupled joint pair, which consists of full state feedback (motor position, link side torque, as well as their derivatives). Furthermore, in order to improve position accuracy and simultaneously keep good dynamic behavior of the MIMO state feedback controller, a cascaded tracking control scheme is proposed, based on the MIMO state feedback controller with additional feedforward terms (desired motor velocity, desired motor acceleration, derivative of the desired torque), which are computed in a computed torque controller and take the whole rigid body dynamics into account. Stability analysis is shown for the complete controlled robot. Finally, experimental results with the DLR medical robot are presented to validate the practical efficiency of the approaches.     

Robust control of robotic manipulators based on integral sliding mode

For rigid body robot manipulators, the computed torque approach provides asymptotic stability for tracking control tasks. However, the state dependent matrices needed to complete the computed torque algorithm are normally unknown and possibly too complex for a real-time implementation. This paper proposes a simple controller with computed-torque-like structure enhanced by integral sliding mode, having pole-placement capability. For the reduction of the chattering effect generated by the sliding mode part, the integral sliding mode is posed as a perturbation estimator with quasi-continuous control action provided by an additional low-pass filter. The time-constant of the latter tunes the controller functionality between the perturbation compensation and a pure integral sliding mode control, as well as between chattering reduction and system robustness. A comparative simulation study between conventional sliding mode control, integral sliding mode control, and integral sliding mode in form of a perturbation estimator for a two-link robot arm validates the proposed design.     

机械臂的位置与力的混合控制方法

本文研究关于机械臂的位置与力的混合控制方法.为了实现混合控制,首先应用一对被 称为"任务规范投影算子"建立了机械臂混合控制的动态方程.在此基础上,提出了两种控制器 的设计方法,一种是计算力矩方法控制器,另一种是动态补偿变结构控制器.后者不但具有更 好的鲁棒性,并且可以分别调整运动与约束力的跟踪精度.     

A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm

A novel robust adaptive control algorithm is proposed and implemented in real-time on two degrees-of-freedom (DOF) of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) arm in joint-space. In addition to having a significant robustness property for the tracking, the algorithm also features a sliding-mode term based adaptive law that captures directly the parameter estimation error. An auxiliary filtered regression vector and filtered computed torque is introduced. This allows the definition of another auxiliary matrix, a filtered regression matrix, which facilitates the introduction of a sliding mode term into the adaptation law. Parameter error convergence to zero can be guaranteed within finite-time with a Persistent-Excitation (PE) condition or Sufficient Richness condition for the demand. The proposed scheme also exhibits robustness both in the tracking and parameter estimation errors to any bounded additive disturbance. This theoretical result is then exemplified for the BERT II robot arm in simulation and for experiments.     

A comparison of model-based path control algorithms for direct-drive SCARA robots

Various advanced control strategies are applied to a direct-drive SCARA robot and studied in computer simulations. Besides computed torque control and direct adaptive control, heuristic optimal control, a new path control scheme for robotic manipulators, is included in the comparison study. PD control, the traditional robot control method, is used for generating a comparing baseline. While all schemes are applied for the same tracking task, the effect of modelling errors and measurement noise is considered in robot performance evaluation. Simulation results show that (1) without model errors, all advanced control schemes can achieve higher tracking accuracy than PD control; (2) with a random measurement error of 1%, computed torque and direct adaptive control methods are inferior to PD control; (3) heuristic control proves to be the most robust control scheme in case of mixed model and measurement errors.     

A C5 parallel robot identification and control

The paper proposes a controller scheme based on a priori identification for a C5 parallel robot. First we realize the identification of dynamic parameters of the robot using the Least Squares technique. Different data are used for this step of identification. The cross validation permitted to select and confirm the identified parameters. After, a control scheme (computed torque) is applied to control the C5 parallel robot. The functions of this control scheme are based on precedent identified parameters. In order to reduce the effect of the identification error, we have added a robustness term based on sliding mode technique. The stability of the system in closed loop is presented using the Lyapunov principle. Experimental results of identification and control are presented and show the effectiveness of our methodology.     

一种基于操作空间的操作器分散自适应控制方法

本文提出了一种操作器分散自适应阻力控制方法．这种方法不要求知道操作器动态模型的结构和参数，采用分散控制的形式，可以对各自由度单独进行控制，因此计算简单有效，具有一定的容错能力，控制系统有较好的暂态性能，由于控制律以操作空间坐标形式描述，适合于具有冗余自由度的操作器的控制．计算机仿真表明了该方法的良好的控制效果