Publications       Rudy Negenborn
Associate Professor
Team & Themes

Title:Predictive path following with arrival time awareness for waterborne AGVs
Authors:H. Zheng, R.R. Negenborn, G. Lodewijks

Journal:Transportation Research Part C

Abstract:Large ports are seeking innovative logistical ways to improve their competitiveness worldwide. This article proposes waterborne AGVs, inspired by conventional AGVs and Autonomous Surface Vessels, for transport over water. A predictive path following with arrival time awareness controller is proposed for such waterborne AGVs. The controller is able to achieve smooth tracking and energy efficiency with arrival time awareness for transport oriented applications. Tracking errors are conveniently formulated with vessel dynamics modeled in connected reference path coordinate systems and a coordinate transformation at switching coordinate systems. Binary decision variables and logic constraints based on an along-track state are proposed for modeling switches in the framework of Model Predictive Control (MPC) so that overshoots are avoided. Moreover, timing-aware along-track references are generated by a two-level double integrator scheme. The lower level is embedded in online MPC optimizations for smooth tracking. The higher level solves a mixed-integer quadratic programming problem considering distance-to-go and time-to-go before each MPC optimization. References over the next prediction horizon are generated being aware of the requirements on arrival time. Furthermore, successive linearizations of nonlinear vessel dynamics about a shifted previous optimal system trajectory are implemented to maintain a trade-off between computational complexity and optimality. Simulation results of two industrially relevant Inter Terminal Transport case studies illustrate the effectiveness of the proposed modeling and control design for waterborne AGVs.

Reference:H. Zheng, R.R. Negenborn, G. Lodewijks. Predictive path following with arrival time awareness for waterborne AGVs. Transportation Research Part C, vol. 70, pp. 214-237, September 2016.
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