Nonlinear Model Predictive Control for Automatic Train Operation with Actuator Saturation and Speed Limit

2014 ◽  
Vol 678 ◽  
pp. 377-381
Author(s):  
Long Sheng Wang ◽  
Hong Ze Xu

This paper addresses a position and speed tracking problem for high-speed train automatic operation with actuator saturation and speed limit. A nonlinear model predictive control (NMPC) approach, which allows the explicit consideration of state and input constraints when formulating the problem and is shown to guarantee the stability of the closed-loop system by choosing a proper terminal cost and terminal constraints set, is proposed. In NMPC, a cost function penalizing both the train position and speed tracking error and the changes of tracking/braking forces will be minimized on-line. The effectiveness of the proposed approach is verified by numerical simulations.

Author(s):  
Thomas J. Stastny ◽  
Gonzalo A. Garcia ◽  
Shawn S. Keshmiri

This paper presents a novel approach to collision and obstacle avoidance in fixed-wing unmanned aerial systems (UASs), vehicles with high speed and high inertia, operating in proximal or congested settings. A unique reformulation of classical artificial potential field (APF) navigational approaches, adaptively morphing the functions' shape considering six-degrees-of-freedom (6DOF) dynamic characteristics and constraints of fixed-wing aircraft, is fitted to an online predictive and prioritized waypoint planning algorithm for generation of evasive paths during abrupt encounters. The time-varying waypoint horizons output from the navigation unit are integrated into a combined guidance and nonlinear model predictive control scheme. Real-time avoidance capabilities are demonstrated in full nonlinear 6DOF simulation of a large unmanned aircraft showcasing evasion efficiency with respect to classical methods and collision free operation in a congested urban scenario.


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