TY - CPAPER
AU - Jens Geisler
AU - K. Witting
AU - A. Trächtler
AU - M. Dellnitz
AB - Self-optimization refers to the ability of a mechatronic system to autonomously adapt the way it performs its functions to changing environmental and operational conditions or user demands. In this work we propose to use multiobjective optimal control to enable the self-optimization of the guidance of a rail-bound vehicle. We consider different strategies to reduce the computational cost of the optimization. Most importantly, a two-degree-of-freedom controller is used to separate optimal trajectory generation from disturbance compensation. Also, in order to solve the multiobjective optimization problem, an approximation of the entire set of optimal compromises of the objectives, the so-called Pareto set, is computed offline at design time. From this, we can derive a collection of weighting vectors that capture the best trade-off between the objectives for different situations. Given this set of preselected weights, for the online optimization, the objective function can be taken to be a weighted sum that best matches the situation at hand. For the guidance system we consider three objectives. Preliminary offline simulation results are presented.
BT - Proceedings of the 17th IFAC World Congress
CY - Seoul, Korea
DA - 07/2008
DO - 10.3182/20080706-5-KR-1001.00738
N2 - Self-optimization refers to the ability of a mechatronic system to autonomously adapt the way it performs its functions to changing environmental and operational conditions or user demands. In this work we propose to use multiobjective optimal control to enable the self-optimization of the guidance of a rail-bound vehicle. We consider different strategies to reduce the computational cost of the optimization. Most importantly, a two-degree-of-freedom controller is used to separate optimal trajectory generation from disturbance compensation. Also, in order to solve the multiobjective optimization problem, an approximation of the entire set of optimal compromises of the objectives, the so-called Pareto set, is computed offline at design time. From this, we can derive a collection of weighting vectors that capture the best trade-off between the objectives for different situations. Given this set of preselected weights, for the online optimization, the objective function can be taken to be a weighted sum that best matches the situation at hand. For the guidance system we consider three objectives. Preliminary offline simulation results are presented.
PP - Seoul, Korea
PY - 2008
T2 - Proceedings of the 17th IFAC World Congress
TI - Multiobjective Optimization of Control Trajectories for the Guidance of a Rail-Bound Vehicle
ER -