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.     

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.

     

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.     

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。     

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.     

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.     

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.     

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。     

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.     

Decentralized PD and Robust Nonlinear Control for Robot Manipulators

A decentralized PD and robust nonlinear feedback law for robot motioncontrol is proposed. The control system structure is based on thegeneralized tracking error proposed by Slotine and Li. Using this systemstructure, a simple and comprehensive result on the local stabilityconditions of PD control is obtained. A decentralized robust nonlinearfeedback term is then added to it to improves the performance of trackingerrors from local convergence to global convergence. Since the approachkeeps the simplicity of the independent joint controller structure it can beeasily implemented in most robot systems without hardware alteration.     

A Fast Approach for Robot Motion Planning

This paper describes a new approach to robot motion planning that combines the end-point motion planning with joint trajectory planning for collision avoidance of the links. Local and global methods are proposed for end-point motion planning. The joint trajectory planning is achieved through a pseudoinverse kinematic formulation of the problem. This approach enables collision avoidance of the links by a fast null-space vector computation. The power of the proposed planner derives from: its speed; the good properties of the potential function for end-point motion planning; and from the simultaneous avoidance of the links collision, kinematic singularities, and local minima of the potential function. The planner is not defined over computationally expensive configuration space and can be applied for real-time applications. The planner shows to be faster than many previous planners and can be applied to robots with many degrees of freedom. The effectiveness of the proposed local and global planning methods as well as the general robot motion planning approach have been experimented using the computer-simulated robots. Some of the simulation results are included in this paper.     

Homotopic Roadmap Generation for Robot Motion Planning

Two robot paths are said to be in the same homotopic group if one can be obtained from the other by multiple small deformations. Knowledge of robot homotopic groups gives information regarding the obstacle structure and enables timely computation of optimal path. Making a roadmap which misses out on a single homotopic group in such approaches may lead to sub-optimal decisions. E.g. one may prefer to go through a very narrow corridor if that reduces the path length significantly, but not if the resulting path has too many such narrow segments. Similarly knowledge of homotopic groups may enable distribution and scheduling of robots across homotopic groups for decentralized planning of multiple robots. For an unstructured robot environment, sampling based approaches give an insight into homotopic groups. The aim of the work is to make a homotopy conscious Probabilistic Roadmap such that the roadmap is capable of generating paths corresponding to as many homotopic groups as possible. Experimental results confirm that the proposed approach gives the best results as compared to the other sampling techniques subject to the test scenarios run.     

基于粒子群模糊的除冰机器人越障规划

由于除冰机器人多在天气恶劣,覆冰较厚的输电线路上工作,现有的基于视觉伺服越障策略存在图像质量差,冰、线区分难等不足。根据模糊逻辑和粒子群优化原理,提出了一种除冰机器人在线越障和路径规划方法。该方法通过模糊规划器实现除冰机器人机械臂的无障跟踪和平稳越障。在此基础上,以机械臂末端经过路径长度和与目标点距离的综合最小为目标,利用粒子群算法对模糊规划器输出角度进行在线优化。仿真结果表明:与传统的模糊越障规划相比,该方法不仅满足除冰机器人实时规划和自主越障的要求,缩短了机械臂经过轨迹的长度,提高了除冰机器人的工作效率和续航能力,为实际工程应用中除冰机器人的能源短缺问题,提供了一种节约使用方案,具有一定的工程应用价值。