MacNab, Chris J. B.Doctolero, Samuel2020-06-012020-06-012020-05-27Doctolero, 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.http://hdl.handle.net/1880/112133A 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.engUniversity 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.HybridForce ControlPosition ControlMachine LearningCerebellar Model Articulation ControllerLyapunovBacksteppingControl SystemAdaptiveArtificial IntelligenceEngineering--Electronics and ElectricalEngineering--MechanicalRoboticsmaster thesis10.11575/PRISM/37884