ASAP-MPC: An Asynchronous Update Scheme for Online Motion Planning with Nonlinear Model Predictive Control
CoRR(2024)
摘要
This paper presents a Nonlinear Model Predictive Control (NMPC) scheme
targeted at motion planning for mechatronic motion systems, such as drones and
mobile platforms. NMPC-based motion planning typically requires low computation
times to be able to provide control inputs at the required rate for system
stability, disturbance rejection, and overall performance. Although there exist
various ways in literature to reduce the solution times in NMPC, such times may
not be low enough to allow real-time implementations. This paper presents
ASAP-MPC, an approach to handle varying, sometimes restrictively large,
solution times with an asynchronous update scheme, always allowing for full
convergence and real-time execution. The NMPC algorithm is combined with a
linear state feedback controller tracking the optimised trajectories for
improved robustness against possible disturbances and plant-model mismatch.
ASAP-MPC seamlessly merges trajectories, resulting from subsequent NMPC
solutions, providing a smooth and continuous overall trajectory for the motion
system. This frameworks applicability to embedded applications is shown on two
different experiment setups where a state-of-the-art method fails: a quadcopter
flying through a cluttered environment in hardware-in-the-loop simulation and a
scale model truck-trailer manoeuvring in a structured lab environment.
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