Motion Control of Rigid Robot Manipulators via First and Second Order Sliding Modes

A motion control strategy for rigid robot manipulators based on sliding mode control techniques and the compensated inverse dynamics method is presented in this paper. The motivation for using sliding mode mainly relies on its appreciable features, such as simplicity and robustness versus matched uncertainties and disturbances. Furthermore the proposed approach avoids the estimation of the time-varying inertia matrix. As a preliminary step a first order sliding mode control law is presented. Then a second order strategy is discussed. In both cases the problem of chattering, typical of sliding mode control, is suitably circumvented. Simulations results demonstrates the good tracking properties of the proposed control strategy.     

一种基于传感信息的机器人在线路径规划方法

针对非结构化环境下的多关节机器人实时避障问题,提出一种基于传感信息的机器人在线路径规划方法.由红外线传感器提供机器人手臂周围环境信息,通过计算C-空间内一些方向上的C-空间障碍距离,分阶段控制位姿点到达目标.避免了建立整个位姿点附近的C-空间,适合机器人在未知环境下的实时避障要求.仿真结果验证了该算法的有效性.     

考虑柔性关节的机械臂自适应运动控制策略研究

具有柔性关节的轻型机械臂因其自重轻、响应迅速、操作灵活等优点,取得了广泛应用;针对具有柔性关节的机械臂系统的关节空间轨迹跟踪控制系统动力学参数不精确的问题,提出一种结合滑模变结构设计的自适应控制器算法;通过自适应控制的思想对系统动力学参数进行在线辨识,并采用Lyapunov方法证明了闭环系统的稳定性;仿真结果表明,该控制策略保证了机械臂系统对期望轨迹的快速跟踪,具有良好的跟踪精度,系统具有稳定性。     

多机械手刚性协调动力学建模与混合控制

从力负载分析开始,阐述多手直辖市中对象的外/内力和外/内运动定义;考虑对象运动受限情况,提出一组描述协调任务的广义运动和力向量;以各机械手及对象的运动方程为基础,建立面向对象的运动学、静力学和动力学模型,该模型与单机械手操作空间的运动方程具有一致的表达形式,据此,该文探讨了多手协调情况下运动、内力和接触力的混合控制问题。     

Revising the Robust-Control Design for Rigid Robot Manipulators

Robust controllers for robot manipulators ensure stability of the closed-loop system, even if only partial knowledge of the dynamic model of the manipulator is available. Existing derivations of robust-control laws, while guaranteeing the stability result, present an undesired dependence of the robust-control term on the gains of the controller for the nominal system. This dependence forces larger robust-control terms when the nominal control gains are large. Based on a structured representation of the model uncertainty, this paper proposes a derivation of the robust-control law, where these limitations are removed. Experimental results on the COMAU SMART 3S industrial robot in a 3-degree-of-freedom (DOF) configuration confirm the advantages of the proposed controller.      

A Stable Neuro-Adaptive Controller for Rigid Robot Manipulators

In this paper a controller based on neural networks is proposed toachieve output trajectory tracking of rigid robot manipulators. Neuralnetworks used here are one hidden layer ones so that their outputs dependlinearly on the parameters. Our method uses a decomposed connectioniststructure. Each neural network approximate a separate element of thedynamical model. These approximations are used to perform an adaptive stablecontrol law. The controller is based on direct adaptive techniques and theLyapunov approach is used to derive the adaptation laws of the netsparameters. By using an intrinsic physical property of the manipulator, thesystem is proved to be stable. The performance of the controller depends onthe quality of the approximation, i.e. on the inherent reconstruction errorsof the exact functions.     

Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators

An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

Time-optimal and Smooth Trajectory Planning for Robot Manipulators

This paper presents a practical time-optimal and smooth trajectory planning algorithm and then applies it to robot manipulators. The proposed algorithm uses the time-optimal theory based on the dynamics model to plan the robot’s motion trajectory, constructs the trajectory optimization model under the constraints of the geometric path and joint torque, and dynamically selects the optimal trajectory parameters during the solving process to prominently improve the robot’s motion speed. Moreover, the proposed algorithm utilizes the input shaping algorithm instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The input shaping of trajectory parameters during postprocessing not only suppresses the residual vibration of the robot but also takes the signal delay caused by traditional input shaping into account. The combination of these algorithms makes the proposed time-optimal and smooth trajectory planning algorithm ensure absolute time optimality and achieve a smooth trajectory. The results of an experiment on a six-degree-of-freedom industrial robot indicate the validity of the proposed algorithm.

     

基于传感器和模糊规则的机器人在动态障碍环境中的智能运动控制

提出了一种基于传感器和模糊规则的智能机器人运动规划方法,该方法运用了基于调和函数分析的人工势能场原理。采用模糊规则可减少推导势能函数所必须的计算,同时给机器人伺服系统发出指令,使它能够自动地寻找通向目标的路径。提出的方法具有简单、快速的特点,而且能对n自由度机械手的整个手臂实现避碰,建立在非线性机器人动力学之上的整个闭环系统和模糊控制器的稳定性由李雅普诺夫原理保证。仿真结果证明了该方法的有效性,通过比较分析显示出文中所提出的避障算法的优越性。     

Modelling of Bound Estimation Laws and Robust Controllers for Robustness to Parametric Uncertainty for Control of Robot Manipulators

In this paper, first, a new approach is proposed for derivation of bound estimation laws for robust control of robot manipulators. In this approach, functions depending on robot kinematics and control parameters and integration techniques can be used for derivation of the bound estimation laws based on the Lyapunov theory, thus, stability of the uncertain system is guaranteed. Five new bound estimation laws are proposed, and in this derivations, five novel functions depending on robot kinematics and control parameters and proper integration techniques, such as substitution method, integration by part and integration by partial fractions are used. Then, four new robust control inputs are proposed based on each derived bound estimation law. Lyapunov theory based on Corless and Leitmann (IEEE Trans Automat Contr 26:1139–1144, 1981) approach is used for designing the robust control input achieving uniform boundedness error convergence. This study also allows derivation of other bound estimation laws for robust controllers provided that appropriate novel functions and proper integration techniques are chosen.     

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。     

一种工业机器手的自适应阻抗控制方法

提出一种基于任务空间的直接自适应阻抗方法，它不要求辨识机器手动态模型结构和参数，不需要计算机器手的运动学逆变换，因此，避免了基于机器手模型线性参数辨识的控制方法的缺点。     

移动机器人运动规划与计算机控制实现

论文针对移动机器人的链式系统提出了一种实时运动规划方法,通过在线更新复合分段常值与多项式输入控制律的系数,来驱使系统到达预定的目标,并将其应用于两伺服直流电机独立控制轮式移动机器人,采用计算机系统以及伺服放大器实现其控制,控制的结果达到了预期的效果。     

A Frequency-Dependent Direct Adaptive Scheme for Robot Manipulators: A Case of Model-Following Motion Control

This paper presents a new frequency-dependent direct adaptive scheme for the optimal and/or suboptimal tracking of the motion of a prescribed model. The idea is based on a closed-loop control scheme in which an _{2 H} optimal/suboptimal controller is applied in parallel with a direct adaptive technique to guide a robot manipulator to follow a certain prescribed trajectory. The H ₂ compensators have to be proper and positively bounded with respect to all dynamic frequencies. Robustness issues are addresses in the paper by lumping all the nonlinear dynamic terms such as the centrifugal and Coriolis effects as well as the mechanical and electrical friction forces of the robot arm, into a general unstructured uncertainty term.     

Flexible Links Manipulators: from Modelling to Control

This paper relates recent results obtained in the field of modelling and control of flexible link manipulators and proposes an investigation of the problem raised by this type of systems (at least in the planar case). First, adopting the modal floating frame approach and the Newton–Euler formalism, we propose an extension of the models for control to the case of fast dynamics and finite deformations. This dynamic model is based on a nonlinear generalisation of the standard Euler–Bernoulli kinematics. Then, based on the models recalled we treat the end-effector tracking problem for the one-link case as well as for the planar multi-link case. For the one-link system, we propose two methods, the first one is based on causal stable inversion of linear non-minimum phase model via output trajectory planning. The other one is an algebraic scheme, based on the parametrization of linear differential operators. For the planar multi-link case the control law proposed is based on causal stable inversion over a bounded time domain of nonlinear non-minimum phase systems. Numerical tests are presented together with experimental results, displaying the well behaved of these approaches.     

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.     

机器人运动规划中的姿态空间建模方法研究

提出机器人运动规划中的一种姿态空间建模方法,其基本思想是在工作空间中定义具有某种特征的点为基本碰撞体,可用于机械臂在复杂环境中的运动规划建模。实验表明该建模方法是有效可行的。     

An Adaptive Fault-tolerant Control Method for Robot Manipulators

International Journal of Control, Automation and Systems - This paper presents a new method for adaptive continuous nonsingular fast terminal sliding mode control (ACNFTSMC) based on a novel...     

An Approach of Adaptive Control for Robot Manipulators

This paper presents the study of an adaptive control which tracks a desired time-based trajectory as closely as possible for all times over a wide range of manipulator motion and payloads both in joint-variable coordinates and Cartesian coordinates. The proposed adaptive control is based on the linearized perturbation equations in the vicinity of a nominal trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component computes the nominal torques from the Newton-Euler equations of motion either using the resolved joint information or the joint information from the trajectory planning program. This computation can be completed in O(n) time. The feedback component consisting of recursive least square identification and one-step optimal control algorithms for the linearized system computes the perturbation torques in O(n³) time. Because of the parallel structure, the computations of the adaptive control may be implemented in low-cost microprocessors. A computer simulation study Was conducted to evaluate the performance of the adaptive control in joint-variable coordinates for a three-joint robot arm. The feasibility of implementing the adaptive control in Cartesian coordinates using present day low-cost microprocessors is discussed.