scholarly journals Robust Task Space Trajectory Tracking Control of Robotic Manipulators

2016 ◽  
Vol 21 (3) ◽  
pp. 547-568 ◽  
Author(s):  
M. Galicki

Abstract This work deals with the problem of the accurate task space trajectory tracking subject to finite-time convergence. Kinematic and dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the end-effector. Furthermore, the movement is to be accomplished in such a way as to reduce both the manipulator torques and their oscillations thus eliminating the potential robot vibrations. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we propose a class of chattering-free robust controllers, based on the estimation of transpose Jacobian, which seem to be effective in counteracting both uncertain kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a SCARA type consisting of the three revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers as well as comparisons with other well known control schemes.

2017 ◽  
Vol 22 (4) ◽  
pp. 839-865
Author(s):  
M. Galicki

Abstract This work deals with the problem of the robust optimal task space trajectory tracking subject to finite-time convergence. Kinematic and dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the endeffector. Furthermore, the movement is to be accomplished in such a way as to minimize both the manipulator torques and their oscillations thus eliminating the potential robot vibrations. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we derive a class of chattering-free robust kinematically optimal controllers, based on the estimation of transpose Jacobian, which seem to be effective in counteracting both uncertain kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a SCARA type consisting of the three revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers as well as comparisons with other well known control schemes.


2016 ◽  
Vol 85 (3-4) ◽  
pp. 471-489 ◽  
Author(s):  
Mirosław Galicki

AbstractThis work deals with the problem of the accurate task space control subject to finite-time convergence. Kinematic and dynamic equations of a rigid robotic manipulator are assumed to be uncertain. Moreover, unbounded disturbances, i.e., such structures of the modelling functions that are generally not bounded by construction, are allowed to act on the manipulator when tracking the trajectory by the end-effector. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we derive a class of absolutely continuous (chattering-free) robust controllers based on the estimation of a Jacobian transpose matrix, which seem to be effective in counteracting uncertain both kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a 2DOF robotic manipulator with two revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiutai Liu ◽  
Xiucheng Dong ◽  
Yong Yang ◽  
Hongyu Chen

This paper aims at the trajectory tracking problem of robot manipulators performing repetitive tasks in task space. Two control schemes are presented to conduct trajectory tracking tasks under uncertain conditions including unmodeled dynamics of robot and additional disturbances. The first controller, pure adaptive iterative learning control (AILC), is based upon the use of a proportional-derivative-like (PD-like) feedback structure, and its design seems very simple in the sense that the only requirement on the learning gain and control parameters is the positive definiteness condition. The second controller is designed with a combination of AILC and neural networks (NNs) where the AILC is adopted to learn the periodic uncertainties that attribute to the repetitive motion of robot manipulators while the add-on NNs are used to approximate and compensate all nonperiodic ones. Moreover, a combined error factor (CEF), which is composed of the weighted sum of tracking error and its derivative, is designed for network updating law to improve the learning speed as well as tracking accuracy of the system. Stabilities of the controllers and convergence are proved rigorously by a Lyapunov-like composite energy function. The simulations performed on two-link manipulator are provided to verify the effectiveness of the proposed controllers. The results of compared simulations illustrate that our proposed control schemes can significantly conduct trajectory tracking tasks.


Author(s):  
Kun Yu ◽  
Leng-Feng Lee ◽  
Venkat N. Krovi

Cable-actuated parallel manipulators combine benefits of large workspaces, significant payload capacities and high stiffness by virtue of the cable actuation. However, redundant/surplus cables are required to overcome the unidirectional nature of forces exertable by cables. This leads to actuation redundancy which needs to be resolved in order to realize some of the benefits. We study the implication of using actuation redundancy to tailor the workspace (task space) stiffness of the cable robot system. Suitable trajectory tracking control schemes are developed that additionally achieve secondary goal of active stiffness control to improve disturbance rejection, under positive control input constraint We demonstrate the performance of these control schemes using a point-mass cable robot system modeled within a virtual prototyping (VP) implementation framework.


Author(s):  
Q Li ◽  
S K Tso ◽  
W J Zhang

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.


2012 ◽  
Vol 457-458 ◽  
pp. 1089-1095
Author(s):  
Da Yong Lu ◽  
Zhen Hua Luo ◽  
Jian Lu Tian

Trajectory tracking control is a major control problem in the application of the wheeled mobile robots (WMRs). However, many of the WMRs are autonomous which are equipped with several kinds of sensor to detect the position and orientation by itself, such as ultrasonic, laser, infrared and visual. This paper studies the implementation for a distributed network feedback control system and the trajectory tracking control algorithm for a non-autonomous WMR. The control system consists of a wireless sensor network (WSN) and a monitoring base station for measurement and feedback, and a non-autonomous WMR as controlled plant. To cope with the distributed and asynchronous measurements and slow response of the feedback channel, the sectional error amplitude limited algorithm is studied and the results show the system can effectively track a reference trajectory.


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