Comparison of PD-Based Controllers for Robotic Manipulators

2004 ◽  
Author(s):  
P. R. Ouyang ◽  
W. J. Zhang
Keyword(s):  
Trajectory Tracking ◽  
Simple Structure ◽  
Feedforward Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Tracking Performance ◽  
Pd Control ◽  
Tracking Tasks ◽  
Control Schemes ◽  
Previous Iteration

PD control is widely used in industrial robotic manipulators because of its simple structure and acceptable performance. In this paper, the PD-based control schemes for the trajectory tracking of the robotic manipulators are addressed. The fixed gain PD control, the nonlinear gain PD (NPD) control, the adaptive PD learning control (PD-LC), and the adaptive NPD learning control (NPD-LC) are applied for the trajectory tracking of both serial and parallel robotic manipulators. The PD-LC and NPD-LC controllers can be used to improve the tracking performance for the repeatable tracking tasks in an iterative mode. The PD-LC and NPD-LC consists of a PD/NPD control as the basic feedback control and an additional feedforward control term directly inherited from the previous iteration of the same control task. A comparative study of four PD-based controllers is conducted to understand how different control schemes will affect the trajectory tracking performance, and the results are shown in this paper. Case studies are presented to demonstrate the validity of the PD-LC and NPD-LC algorithms.

scholarly journals Trajectory Tracking Control for Uncertain Robot Manipulators with Repetitive Motions in Task Space

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiutai Liu ◽  
Xiucheng Dong ◽  
Yong Yang ◽  
Hongyu Chen
Keyword(s):  
Trajectory Tracking ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Task Space ◽  
Learning Gain ◽  
Trajectory Tracking Control ◽  
Adaptive Iterative Learning Control ◽  
Tracking Tasks ◽  
Control Schemes ◽  
Composite Energy

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.

Controlled Synchronization of Robotic Manipulators in the Task Space

2009 ◽  
Author(s):  
YenChen Liu ◽  
Nikhil Chopra
Keyword(s):  
Trajectory Tracking ◽  
Control Algorithm ◽  
Robotic Manipulators ◽  
Joint Space ◽  
Task Space ◽  
Numerical Example ◽  
Redundant Robots ◽  
Control Schemes ◽  
Heterogeneous Robots ◽  
Dynamic Uncertainties

Due to its practical applicability, recently several algorithms for robot synchronization have been developed in the literature. However, the focus of these control schemes has primarily been on joint-space control and in the absence of communication unreliabilities between the agents. In this paper, we study the problem of task space synchronization and trajectory tracking for heterogeneous robots under dynamic uncertainties. Exploiting passivity based synchronization results developed previously, a new control algorithm is proposed to guarantee task space synchronization for a group of robotic manipulators. Both non-redundant and redundant robots are considered and the proposed scheme is validated by a numerical example.

scholarly journals Iterative learning control with sampled-data feedback for robot manipulators

2014 ◽  
Vol 24 (3) ◽  
pp. 299-319 ◽  
Author(s):  
Kamen Delchev ◽  
George Boiadjiev ◽  
Haruhisa Kawasaki ◽  
Tetsuya Mouri
Keyword(s):  
Feedback Control ◽  
Iterative Learning Control ◽  
Feedforward Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Iterative Learning ◽  
Control Input ◽  
Sampled Data ◽  
Model Based ◽  
Data Feedback

Abstract This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached

Trajectory Tracking of a One-Link Flexible Arm via Iterative Learning Control

2020 ◽  
Author(s):  
Michele Pierallini ◽  
Franco Angelini ◽  
Riccardo Mengacci ◽  
Alessandro Palleschi ◽  
Antonio Bicchi ◽  
...  
Keyword(s):  
Trajectory Tracking ◽  
Iterative Learning Control ◽  
Learning Control ◽  
Iterative Learning ◽  
Flexible Arm

scholarly journals Evaluation of Process Control Schemes for Sour Water Strippers in Petroleum Refining

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 363
Author(s):  
Chii-Dong Ho ◽  
Yih-Hang Chen ◽  
Chao-Min Chang ◽  
Hsuan Chang
Keyword(s):  
Feedback Control ◽  
Prediction Models ◽  
Feedforward Control ◽  
Absolute Error ◽  
Dynamic Responses ◽  
Control Schemes ◽  
Online Measurements ◽  
Measurement Delay ◽  
Water Feed ◽  
Sour Water

For the sour water strippers in petroleum refinery plants, three prediction models were developed first, including the estimators of sour water feed concentrations using convenient online measurements, the minimum reboiler duty and the corresponding internal temperature at a specific location (Tstage,29). Feedforward control schemes were developed based on these prediction models. Four categories of control schemes, including feedforward, feedback, feedback with external reset, and feedforward-feedback, were proposed and evaluated by the rigorous dynamic simulation model of the sour water stripper for their dynamic responses to the sour water feed stream disturbances. The comparison of control performance, in terms of the settling time, integrated absolute error (IAE) of the NH3 concentration of the stripped sour water and IAE of the specific reboiler duty, reveals that FFT (feedforward control of Tstage,29) and FBA-DT3 (feedback control with 3 min concentration measurement delay) are the best control schemes. The second-best control scheme is FBAT (cascade feedback control of concentration with temperature).

Sign in / Sign up

Export Citation Format

Share Document