Xuchao Lin

Christos. G. Cassandras, Xuchao Lin, Xu Chu Ding

We propose an optimal control framework for persistent monitoring problems where the objective is to control the movement of mobile nodes to minimize an uncertainty metric in a given mission space. For multi agent in a one-dimensional mission space, we show that the optimal solution is obtained in terms of a sequence of switching locations and waiting time on these switching points, thus reducing it to a parametric optimization problem. Using Infinitesimal Perturbation Analysis (IPA) we obtain a complete solution through a gradient-based algorithm. We also discuss a receding horizon controller which is capable of obtaining a near-optimal solution on-the-fly.

Christos G. Cassandras, Xu Chu Ding, Xuchao Lin

We propose an optimal control framework for persistent monitoring problems where the objective is to control the movement of mobile agents to minimize an uncertainty metric in a given mission space. For a single agent in a one-dimensional space, we show that the optimal solution is obtained in terms of a sequence of switching locations, thus reducing it to a parametric optimization problem. Using Infinitesimal Perturbation Analysis (IPA) we obtain a complete solution through a gradient-based algorithm. We also discuss a receding horizon controller which is capable of obtaining a near-optimal solution on-the-fly. We illustrate our approach with numerical examples.