Long, Y., Xie, L. (2021). Unconstrained tracking MPC for continuous-time nonlinear systems. Automatica, 129, 109680. doi:10.1016/j.automatica.2021.109680
Abstract:
In this paper, we extend unconstrained model predictive control (MPC) from setpoint stabilization to dynamic reference tracking for continuous-time nonlinear systems. In particular, we focus on the case when the reference cannot be perfectly tracked by the system due to dynamics and/or constraints. Under the incremental stabilizability assumption and an additional dissipativity assumption, the practical stability of tracking the unknown optimal reachable reference trajectory is proved even though the controller does not know such a reference explicitly.
License type:
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - Singapore Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) Program
Grant Reference no. :
This research / project is supported by the Agency for Science, Technology and Research - A*ccelerate Gap-funded project
Grant Reference no. : ACCL200013