Abstract
In this paper, a novel adaptive sliding mode control scheme with RBF (radial basis function) neural network-based tuning method is proposed for the trajectory tracking of a 6-RSS (Revolute-Spherical-Spherical) parallel robot in Cartesian space. Parallel robot is a highly nonlinear system with closed-chain mechanisms, which poses the major challenges to the controller design. The robust sliding mode controller is developed to deal with system uncertainties such as modeling errors, frictions, and disturbances. With strong adaptation and learning ability, RBF neural network is adopted to identify the parallel robot dynamics, and then the adaptive self-tuning of the control gains in the controller is realized, which is more flexible than manual tuning method and can guarantee the desired results of the changing system. The stability of the controller has been validated using Lyapunov theorem. Simulation results demonstrate that the proposed controller can achieve better tracking performance than the sliding mode controller with fixed control gains.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
