Abstract
This paper presents the adaptive model predictive control approach for a two-wheeled robot manipulator with varying mass. The mass variation corresponds to the robot picking and placing objects or loads from one place to another. A linear parameter varying model of the system is derived consisting of local linear models of the system at different values of the varying parameter. An adaptive model predictive control controller is designed to control the fast-varying center of gravity angle in the inner loop. The reference for the inner loop is generated by a slower outer loop controlling the linear position using a linear quadratic Gaussian regulator. This adaptive model predictive control/linear quadratic Gaussian control system is simulated on the nonlinear model of the robot, and the closed-loop performance of the proposed scheme is compared with a system having inner/outer loop controllers as proportional integral derivative/proportional integral derivative, feedback linearization/linear quadratic Gaussian, and linear quadratic Gaussian/linear quadratic Gaussian. It is seen that adaptive model predictive control shows mostly superior and otherwise very good performance when compared to these benchmarks in terms of reference tracking and robustness to mass parameter variations.
Highlights
Mobile robot applications are growing in significance in research and daily-life implementations
Sliding mode control (SMC) is possible, a method known for its powerful capabilities and robustness against system uncertainties and perturbations.[26]
Another possibility is feedback linearization (FBL; i.e. dynamic inversion), where the system nonlinearities are cancelled through feedback, after which the problem reduces to linear control.[28,29]
Summary
Mobile robot applications are growing in significance in research and daily-life implementations. Sliding mode control (SMC) is possible, a method known for its powerful capabilities and robustness against system uncertainties and perturbations.[26] SMC drives the system to a predefined hypersurface and ensures exponential convergence to origin, while rejecting disturbances and perturbations.[27] Another possibility is feedback linearization (FBL; i.e. dynamic inversion), where the system nonlinearities are cancelled through feedback, after which the problem reduces to linear control.[28,29] Numerous other nonlinear control approaches were tested on TWBR systems, including fuzzy PID with satisfactory results.[30]. TWBR includes highly coupled nonlinear dynamics, limiting the ability of MPC control to achieve satisfactory performance and stability. After merging a TWBR with a robot manipulator the system becomes underactuated, and from the controller’s perspective it is similar to an inverted pendulum on a cart.[46] In this study the setup investigated consists of four manipulator links, which are considered as one virtual link The use of this equivalence allows simpler dynamical modeling.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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.
