Adaptive CMAC Neural Network Backstepping Control on a 2 DoF planar Manipulator in J-inverse Control Method
| dc.contributor.advisor | Pieper, Jeffery Kurt | |
| dc.contributor.advisor | MacNab, Chris J. B. | |
| dc.contributor.author | Bonakdar, Iman | |
| dc.contributor.committeemember | Goldsmith, Peter B. | |
| dc.contributor.committeemember | Westwick, David T. | |
| dc.date | 2020-11 | |
| dc.date.accessioned | 2020-09-29T22:48:51Z | |
| dc.date.available | 2020-09-29T22:48:51Z | |
| dc.date.issued | 2020-09-25 | |
| dc.description.abstract | Hybrid force/position control has been investigated in many studies in the presence of constraint compliance. Yet, the effect of compliance in the force sensor was left untouched. This study incorporates the force sensor model in the manipulator dynamic model and examines the impact of force sensor compliance on the system’s behavior. It has been shown in the literature that contact compliance and joint flexibility are the sources of instability in hybrid force/position J-inverse control. Nevertheless, using the hybrid force/position J-inverse control in the absence of these conditions causes instability in the system even far from the manipulator workspace singularities. This would lead us to suspect the compliance in the force sensor as a culprit. In this regard, we introduce an adaptive backstepping control that utilizes the Cerebellar Model Articulation Controller (CMAC) neural networks to handle model nonlinearities and parameter uncertainties. The backstepping method deals with force sensor compliance and constraint compliance by introducing virtual control signals. The model nonlinearities and unknown parameters are learned online by the controller with the neural networks’ help, and therefore, delivering an adaptive control design. By the employment of the Lyapunov theorem, we were able to derive the proposed controller and prove the stability of the system. The suggested controller has provided a better performance with respectively lower control effort in the J-inverse control law compared to the J-transpose control law. | en_US |
| dc.identifier.citation | Bonakdar, I. (2020). Adaptive CMAC Neural Network Backstepping Control on a 2 DoF planar Manipulator in J-inverse Control Method (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.doi | http://dx.doi.org/10.11575/PRISM/38308 | |
| dc.identifier.uri | http://hdl.handle.net/1880/112649 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Schulich School of Engineering | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject.classification | Engineering--Mechanical | en_US |
| dc.subject.classification | Robotics | en_US |
| dc.title | Adaptive CMAC Neural Network Backstepping Control on a 2 DoF planar Manipulator in J-inverse Control Method | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Engineering – Mechanical & Manufacturing | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |