Multiobjective Optimization of Control Trajectories for the Guidance of a Rail-bound Vehicle

摘要: Self-optimization refers to the ability of a mechatronic system autonomously adapt way it performs its functions changing environmental and operational conditions or user demands. In this work we propose use multiobjective optimal control enable self-optimization guidance rail-bound vehicle. We consider different strategies reduce computational cost optimization. Most importantly, two-degree-of-freedom controller is used separate trajectory generation from disturbance compensation. Also, in order solve optimization problem, an approximation entire set compromises objectives, so-called Pareto set, computed offline at design time. From this, can derive collection weighting vectors that capture best trade-off between objectives for situations. Given preselected weights, online optimization, objective function be taken weighted sum matches situation hand. For three objectives. Preliminary simulation results are presented.