机器人系统非线性分散重复学习轨迹跟踪控制

采用一类具有"小误差放大、大误差饱和"功能的非线性饱和函数来改进传统重复学习控制(Repetitive control, RC)机器人系统动力学控制, 形成一类新的非线性分散重复学习控制(Nonlinear decentralized repetitive control, NRC),使得在不增加驱动力矩的条件下获得了更快的响应速度和更高的轨迹跟踪精度. 应用Lyapunov直接稳定性理论和LaSalle不变性原理证明了闭环系统的全局渐近稳定性. 三自由度机器人系统数值仿真结果表明了所提出的非线性分散重复学习控制具有良好的控制品质.     

机器人系统终端滑模重复学习轨迹跟踪控制

针对非线性机器人系统的轨迹跟踪问题, 提出一种终端滑模重复学习混合控制方案. 该方案综合了重复学习控制和终端滑模技术的特性, 能够有效跟踪周期性参考信号, 抑制周期性和非周期性动态的干扰, 具有较强的鲁棒性和良好的轨迹跟踪性能, 且算法的实现不需要完全已知系统模型信息. 应用Lyapunov 稳定性理论证明了闭环系统的全局渐近稳定性. 三自由度机器人系统数值仿真结果验证了所提出的终端滑模重复学习控制的有效性.

     

机器人系统全局渐近稳定非线性PD+轨迹跟踪控制

采用一类具有“小误差放大、大误差饱和”功能的非线性饱和函数来改进常用的线性比例微分加（ＰＤ＋）机器人系统动力学控制,以形成非线性ＰＤ＋（ＮＰＤ＋）控制,从而获得更快的响应速度和轨迹跟踪精度．应用Ｌｙａｐｕｎｏｖ直接稳定性理论和ＬａＳａｌｌｅ不变性原理证明了闭环系统的全局渐近稳定性．两自由度机器人系统数值仿真结果表明了所提出的ＮＰＤ＋控制具有良好的控制品质．     

机器人轨迹跟踪自适应控制

本文提出一种新的自适应控制算法,用于补偿机器人动力学方程中的非线性项和消除关节间的耦合。这种简单有效的控制算法,保证了系统在一定的参数变化范围内具有渐近稳定性和良好的控制精度。     

模糊学习控制在SCARA机器人轨迹跟踪中的应用

模糊学习控制以模糊控制提供反馈机制为主体,辅以迭代学习控制提供前馈补偿机制,来实现对期望轨迹的完全跟踪.把模糊学习控制应用于SCARA机器人的轨迹跟踪.仿真试验表明,该方法具有简单实用、跟踪精度高、学习速度快等优点.     

轮式移动机器人的位置量测输出反馈轨迹跟踪控制

针对机器人的姿态角难以精确测量的困难,本文研究基于位置测量的轮式移动机器人的轨迹跟踪问题.首先提出一种利用机器人的位置信息估计其姿态角的降维状态观测器,当机器人的线速度严格大于零时,可保证姿态角观测误差的指数收敛.然后给出一种新的状态反馈轨迹跟踪控制律,当参考轨迹满足一定的激励条件时,可以保证机器人的线速度严格大于零且跟踪误差全局渐近收敛.进一步结合姿态角观测器和状态反馈控制律,得到一种输出反馈轨迹跟踪控制算法.理论分析表明,当参考轨迹满足一定的激励条件时,所提出的输出反馈控制算法可以保证跟踪误差的全局渐近收敛.最后对所提出的姿态角观测器、状态反馈和输出反馈轨迹跟踪控制算法进行了仿真验证,证实了算法的有效性,并且当存在位置测量误差时,所提出的输出反馈轨迹跟踪控制算法仍可以保证机器人对参考轨迹的实际跟踪.     

基于神经网络的机器人轨迹跟踪控制

针对机器人模型未知情况，讨论了用神经网络和反馈控制实现机械手的跟踪控制。提出一种基于参考误差的投影算法来训练网络权值，训练后网络输出能逼近期望的前馈力矩，并从理论上证明跟踪误差的收敛性。仿真结果表明方案具有较好的跟踪性能和较强的抗干扰能力。     

全局指数收敛的机器人PD 自适应轨迹跟踪

针对机器人轨迹跟踪,介绍一种新的ＰＤ自适应控制算法。该算法是全局按指数收敛的,直接利用期望轨迹,不需要定义虚拟参考轨迹,结构简单,计算量小;并能完全消除模型误差产生的轨迹误差,使有界不确定性干扰产生的轨迹误差任意小。两关节直接驱动机器人的实验研究证明了所提出算法的有效性。     

基于预测型间接迭代学习的SCARA机器人轨迹跟踪控制

SCARA机器人是一个强耦合、多输入多输出的非线性系统,运行时较易受外界干扰的影响,而且传统比例-积分-微分(PID)反馈控制器的轨迹跟踪精度较低。针对上述问题,设计具有前馈作用的预测型迭代学习控制器(A-ILC)。利用运行批次在采样时刻t+Δ处的误差输出信息,优化调整下次运行在采样时刻t处双闭环PID反馈控制器的角度。仿真结果表明,与仅采用双闭环PID反馈控制器相比,采用所设计的控制器能明显减小机器人末端的轨迹跟踪误差。     

Global trajectory tracking through output feedback for robot manipulators with bounded inputs

In this work, a globally stabilizing output feedback scheme for the trajectory tracking of robot manipulators with bounded inputs is proposed. It achieves the motion control objective avoiding input saturation and excluding velocity measurements. Moreover, it is not defined using a specific sigmoidal function, but any one on a set of saturation functions. Consequently, the proposed scheme actually constitutes a family of globally stabilizing output feedback bounded controllers. Furthermore, the control gains are not tied to satisfy any saturation‐avoidance inequality and may consequently take any positive value, which may be considered beneficial for performance adjustment/improvement purposes. Further, a class of desired trajectories that may be globally tracked avoiding input saturation and excluding velocity measurements is completely characterized. Global asymptotic stabilization of the closed‐loop system solutions towards the pre‐specified desired trajectory is proved through a strict Lyapunov function. The efficiency of the proposed scheme is corroborated through experimental results. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Adaptive output feedback tracking control of robot manipulators using position measurements only

In this paper, a new adaptive neuro controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of joint positions only. The system uncertainty, which may include payload variation, unknown nonlinearities and torque disturbances is estimated by a Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to generate pseudo filtered tracking error signals (for the elimination of velocity measurements) and the theory of function approximation using CNN. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload variation but also to unstructured one such as disturbances. Moreover the computational complexity of the proposed controller is reduced as compared to the multilayered neural network controller. The validity of the control scheme is shown by simulation results of a two-link robot manipulator. Simulation results are also provided to compare the proposed controller with a controller where velocity is estimated by finite difference methods using position measurements only.     

多自由度机械臂的饱和输出反馈有限时间同步位置控制EI北大核心CSCD

针对输入受限的多自由度机械臂高精度位置控制问题,本文充分考虑驱动器饱和非线性的影响,提出了多自由度机械臂输出反馈饱和有限时间比例–微分(PD)+同步位置控制策略,应用Lyapunov稳定性理论和几何齐次性技术证明了闭环系统的全局有限时间稳定性.非线性饱和函数的恰当引入,使得所提出的控制器具有清晰明确的上界,可以通过预先选择满足特定条件的控制器参数有效避免驱动器饱和问题;同步控制项的恰当引入,使得所提出的控制器兼顾了多自由度机械臂各轴间的同步协调性,从而获得更快的收敛速度和更好的系统整体性能,满足工程实际对机械臂的高精度要求.本文的数值仿真结果验证了所提出的控制方法的有效性和可行性.     

Global finite-time inverse tracking control of robot manipulators

This paper addresses the global finite-time tracking of robot manipulators. By replacing with the nonlinear exponential-like errors, the commonly used inverse dynamics control for robot manipulators is modified to produce global finite-time tracking. Using this method, the controlled robotic system is transformed into a nonlinear and decoupled one, and thus the tracking performance is very convenient to quantify. A Lyapunov-like argument along with finite-time stability analysis is employed to prove global finite-time stability. Simulations performed on a two degree-of-freedom (DOF) manipulator are provided to illustrate the effectiveness and the improved performance of the formulated algorithm.     

机器人自适应PID饱和输出反馈控制

针对机器人系统在仅有位置传感、驱动器饱和、存在建模不确定性及干扰等条件下的轨迹跟踪控制问题,提出了一种新的自适应PID控制方案。采用高精度滤波器估计机器人关节速度,采用带饱和函数的控制器限制输出力矩,采用自适应PID控制器补偿建模不确定性和干扰。通过Lyapunov直接法,证明系统的稳定性。最后以两关节机器人为例,给出仿真实验结果,验证了算法的有效性。     

Global asymptotic stability of a tracking sectorial fuzzy controller for robot manipulators.

This paper shows that fuzzy control systems satisfying sectorial properties are effective for motion tracking control of robot manipulators. We propose a controller whose structure is composed by a sectorial fuzzy controller plus a full nonlinear robot dynamics compensation, in such a way that this structure leads to a very simple closed-loop system represented by an autonomous nonlinear differential equation. We demonstrate via Lyapunov theory, that the closed-loop system is globally asymptotically stable. Experimental results show the feasibility of the proposed controller.     

Global adaptive output feedback tracking control of robotmanipulators

This paper presents a solution to the problem of global, output feedback, tracking control of uncertain robot manipulators, specifically, a desired compensation adaptation law plus a nonlinear feedback term coupled to a dynamic nonlinear filter is designed to produce global asymptotic link position tracking while compensating for parametric uncertainty and requiring only link position measurements     

Adaptive output feedback tracking control of a nonholonomic mobile robot

An adaptive output feedback tracking controller for nonholonomic mobile robots is proposed to guarantee that the tracking errors are confined to an arbitrarily small ball. The major difficulties are caused by simultaneous existence of nonholonomic constraints, unknown system parameters and a quadratic term of unmeasurable states in the mobile robot dynamic system as well as their couplings. To overcome these difficulties, we propose a new adaptive control scheme including designing a new adaptive state feedback controller and two high-gain observers to estimate the unknown linear and angular velocities respectively. It is shown that the closed loop adaptive system is stable and the tracking errors are guaranteed to be within the pre-specified bounds which can be arbitrarily small. Simulation results also verify the effectiveness of the proposed scheme.     

Perfomance-based adaptive tracking control of robot manipulators

This article presents two new adaptive schemes for the motion control of robot manipulators. The proposed controllers are very general and computationally efficient because they do not require knowledge of either the mathematical model or the parameter values of the manipulator dynamics, and are implemented without calculation of the robot inverse dynamics or inverse kinematic transformation. It is shown that the control strategies are globally stable in the presence of bounded disturbances, and that in the absence of disturbances the ultimate bound on the size of the tracking errors can be made arbitrarily small. Computer simulation results are given for a PUMA 560 manipulator, and demonstrate that accurate and robust trajectory tracking can be achieved by using the proposed controllers. Experimental results are presented for an IMI Zebra Zero manipulator and confirm that the control schemes provide a simple and effective means of obtaining high-performance trajectory tracking. © 1995 John Wiley & Sons, Inc.