Adaptive Backstepping Hybrid Force Position Control Free-Space and On Contact Operations

Doctolero, Samuel

Adaptive Backstepping Hybrid Force Position Control Free-Space and On Contact Operations

dc.contributor.advisor	MacNab, Chris J. B.
dc.contributor.author	Doctolero, Samuel
dc.contributor.committeemember	Westwick, David T.
dc.contributor.committeemember	Goldsmith, Peter B.
dc.date	2020-11
dc.date.accessioned	2020-06-01T13:26:44Z
dc.date.available	2020-06-01T13:26:44Z
dc.date.issued	2020-05-27
dc.description.abstract	A set of adaptive hybrid force-position controllers are designed with the intent of operating in free space and on surface-contact without relying on a switching scheme. The ﬁrst set of controllers create the framework for a backstepping adaptive hybrid force-position controller, and the second set take the framework further by designing it to be fully adaptive. On both sets, controllers were originally designed with a fully rigid robot manipulator but it is taken a step further by incorporating ﬂexible joints in the system. In other words, a total of four hybrid force-position controllers are created. Controllers were designed using a Lyapunov stable backstepping approach, to ensure a Uniformly Ultimately Bounded (UUB) system. The proposed controllers utilize Cerebellar Model Articulation Controllers (CMACs) to adapt to unknown functions or dynamics, and do not require knowledge of the surface model, nor location. The controllers for the rigid case are veriﬁed via experimentation on a Quanser 2DOF serial manipulator, with an attached OptoForce 3 directional force sensor at the end-effector, and a foam as the surface to interact with. The controllers for the ﬂexible jointed case are veriﬁed via simulations with a similar model as the Quanser manipulator and a nonlinear stiffness surface to interact with. The experiments and simulations test the effectiveness of the controllers by comparing them against traditional hybrid force-position schemes (stiffness or resolved acceleration). In terms of its robustness, controllers undergo additional payload to change the system dynamics and to asses how the robot behaves. Finally, adaptability is tested by letting the robot follow the same trajectory over multiple cycles and record its errors and neural network weights over time.	en_US
dc.identifier.citation	Doctolero, S. (2020). Adaptive Backstepping Hybrid Force Position Control Free-Space and On Contact Operations (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/37884
dc.identifier.uri	http://hdl.handle.net/1880/112133
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	Hybrid	en_US
dc.subject	Force Control	en_US
dc.subject	Position Control	en_US
dc.subject	Machine Learning	en_US
dc.subject	Cerebellar Model Articulation Controller	en_US
dc.subject	Lyapunov	en_US
dc.subject	Backstepping	en_US
dc.subject	Control System	en_US
dc.subject	Adaptive	en_US
dc.subject.classification	Artificial Intelligence	en_US
dc.subject.classification	Engineering--Electronics and Electrical	en_US
dc.subject.classification	Engineering--Mechanical	en_US
dc.subject.classification	Robotics	en_US
dc.title	Adaptive Backstepping Hybrid Force Position Control Free-Space and On Contact Operations	en_US
dc.type	master thesis	en_US
thesis.degree.discipline	Engineering – Electrical & Computer	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