Efficient Train Operation via Shrinking Horizon Parametrized Predictive Control. Issue 20 (2018)

• Record Type: Journal Article
• Authors: Farooqi, Hafsa; Fagiano, Lorenzo; Colaneri, Patrizio
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 20(2018)
• Page Start: 203
• Page End: 208
• Publication Date: 2018
• Subjects: Model Predictive Control -- Input Parametrization -- Nonlinear control systems -- Train control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.11.014
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The problem of driver assistance for the energy-efficient operation of trains is considered. The goal is to control the traction/braking forces applied to the train, while satisfying speed limits and reaching the next station at the prescribed arrival time. Moreover, the control input has to belong to a discrete set of values and/or operating modes, which a human driver has to implement. A nonlinear model predictive control (MPC) approach is proposed, featuring a shrinking horizon and an input-parametrization strategy to retain a continuous optimization problem. Theoretical convergence guarantees are derived, and the approach is tested in realistic simulations.