Zhao, Kehao
(2019)
Human-Machine Co-Learning Design in Controlling a Double Inverted Pendulum.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Effective human-machine interaction is an essential goal of the design of human-machine systems. This, however, is often constrained by the fundamental limitation of the human neural control and inability of the machine’s control system in adapting to the time-varying characteristics of the human operator. It is desirable that the control system of the machine can learn to optimize its performance under the behavior change of the human operator. This thesis is aimed at enhancing the machine’s control system with learning capabilities. Specifically, an adaptive control framework is proposed that enables human-machine co-learning through the interaction between the machine and the human operator. A dual inverted pendulum system is introduced as an experimental platform. Simulations are performed to implement the control of the two-joint inverted pendulum using the human-machine co-learning controller. The results are compared with those using a controller without learning ability. The parameters of the two controllers are adjusted to explore the effect of the value changing of each parameter on the control performance. Simulation results indicate the superior performance of the proposed adaptive controller design framework.
Share
| Citation/Export: |
|
| Social Networking: |
|
Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
|
| Date: |
10 September 2019 |
| Date Type: |
Publication |
| Defense Date: |
10 July 2019 |
| Approval Date: |
10 September 2019 |
| Submission Date: |
15 July 2019 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
62 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Swanson School of Engineering > Mechanical Engineering and Materials Science |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Human-machine systems, Co-Learning, Control |
| Date Deposited: |
10 Sep 2019 15:23 |
| Last Modified: |
10 Sep 2019 15:23 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/37103 |
Metrics
Monthly Views for the past 3 years
Plum Analytics
Actions (login required)
 |
View Item |
![[feed]](/images/feed-icon-32x32.png){kind=icon}