Adaptive CMAC Neural Network Backstepping Control on a 2 DoF planar Manipulator in J-inverse Control Method
Date
2020-09-25
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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.
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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.