scholarly journals Robotic arm joint position control using iterative learning and mixed sensitivity H∞ robust controller

2021 ◽  
Vol 10 (4) ◽  
pp. 1864-1873
Author(s):  
Petrus Sutyasadi ◽  
Martinus Bagus Wicaksono
Keyword(s):  
Embedded System ◽  
Trajectory Tracking ◽  
Simple Structure ◽  
Position Control ◽  
Low Cost ◽  
Iterative Learning ◽  
Robotic Arm ◽  
Joint Position ◽  
Robust Controller ◽  
Sinusoidal Input

This paper proposes an improved control strategy of a robotic arm joint using hybrid controller consist of H∞ robust controller and iterative learning controller. The main advantage of this controller is the simple structure that made it possible to be implemented on a small embedded system for frugal innovation in industrial robotic arm development. Although it has a simple structure, it is a robust H∞ controller that has robust stability and robust performance. The iterative learning controller makes the trajectory tracking even better. To test the effectiveness of the proposed method, computer simulations using Matlab and hardware experiments were conducted. Variation of load was applied to both of the processes to present the uncertainties. The superiority of the proposed controller over the proportional integral derivative (PID) controller that usually being used in a low-cost robotic arm development is confirmed that it has better trajectory tracking. The error tracking along the slope of sinusoidal trajectory input was suppressed to zero. The biggest error along the trajectory that happened on every peak of the sinusoidal input, or when the direction is changed has been improved from 15 degrees to 4 degrees. This can be conceived that the proposed controller can be applied to control a low-cost robotic arm joint position which is applicable for small industries or educational purpose.

Integrated Direct/Indirect Adaptive Robust Control (DIARC) of Hydraulic Robotics Arm With Accurate Parameter Estimates

2006 ◽  
Author(s):  
Amit Mohanty ◽  
Bin Yao
Keyword(s):  
Parameter Estimation ◽  
Robust Control ◽  
Trajectory Tracking ◽  
Adaptive Robust Control ◽  
Tracking Performance ◽  
Robotic Arm ◽  
Parameter Estimates ◽  
Robust Controller ◽  
Seamless Integration ◽  
Indirect Adaptive Robust Control

In a general Adaptive Robust Control (ARC) framework, the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in the presence of uncertain nonlinearity and parametric uncertainties. However, when secondary purposes such as system health monitoring and prognosis are of equal importance, intelligent integration of output tracking performance oriented direct adaptive robust control (DARC) and the recently proposed accurate parameter estimation-based indirect adaptive robust control (IARC) is required. In this paper, we will consider such a seamless integration for a hydraulic robotic arm. The newly developed IARC design is first applied to the trajectory tracking for the robotic arm but with an improved estimation model, in which accurate parameter estimates are obtained through a parameter estimation algorithm that is based on physical dynamics rather than the tracking error dynamics. An integrated direct/indirect adaptive robust controller (DIARC) is then presented that preserves the excellent transient tracking performance of the direct ARC designs as well as the better parameter estimation process of the IARC design. The proposed Integrated Direct/Indirect Adaptive Robust Controller (DIARC) achieves the controller-identifier separation, thus enabling certain modularity in the controller design.

Design of Laptop Power Adapter Circuit

2013 ◽  
Vol 427-429 ◽  
pp. 909-912
Author(s):  
Shu Hai Wang ◽  
Shu Wang Chen ◽  
Yue Su
Keyword(s):  
Power Factor ◽  
Simple Structure ◽  
Low Cost ◽  
Input Current ◽  
Current Harmonics ◽  
High Power Factor ◽  
Sinusoidal Input ◽  
Long Time ◽  
Conduction Mode ◽  
Control Part

In the design of laptop power adapter, the former stage is the power factor correction PFC converter; the after stage is DC/DC converter. The control part controls chip through an integrated PFC and PFM control integrated. In this two structures, the former stage PFC often using traditional inductor current critical conduction mode Boost converter to achieve sinusoidal input current to the whole form, thus reducing input current harmonics with a high power factor, keep a long time , simple structure and low cost.

Embedded System for Front Differential Drive of Rotational and Translational Vehicle Position Control

2017 ◽  
Vol 10 (4) ◽  
pp. 325
Author(s):  
Angie Julieth Valencia Castañeda ◽  
Mauricio Felipe Mauledoux Monroy ◽  
Oscar Fernando Avilés Sánchez ◽  
Paola Andrea Niño Suarez ◽  
Edgar Alfredo Portilla Flores
Keyword(s):  
Embedded System ◽  
Position Control ◽  
Differential Drive ◽  
Vehicle Position

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

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
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