Feedback Dual Controller Design and Its Application to Monocular Vision-Based Docking

A novel stochastic feedback control approach, which is capable of considering the presence of uncertainty during the controller design phase, is developed and applied to the automatic docking problem for an unmanned vehicle with a monocular vision sensor. A typical procedure for linear stochastic control employs a two-step approach that separates estimation and control: estimate the system state using noise corrupted measurements, and control the system based on the estimated state. However, for nonlinear stochastic control systems and adaptive control systems with uncertain parameters, control and estimation are often not separable because control inputs can affect not only the system state but also the quality of the state estimation. Dynamic programming and search-based methods are general solution techniques for solving these types of control problems, however, those solution techniques are often infeasible for real-time applications due to the huge computational requirements even for small problems. In this paper, a new systematic, suboptimal control algorithm is presented, which can be implemented as an online feedback controller for a stochastic control problem of moderate dimension. For an application example, automated docking of a nonholonomic vehicle with a monocular vision sensor is considered. The results from a series of numerical simulations and experimental tests are presented.