Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control

Abstract

In recent years, automatic driving control has attracted attention. To achieve a satisfactory driving control performance, the prediction accuracy of the traveling route is important. If a highly accurate prediction method can be used, an accurate traveling route can be obtained. Despite the considerable efforts that have been invested in improving prediction methods, prediction errors do occur in general. Thus, a method to minimize the influence of prediction errors on automatic driving control systems is required. This need motivated us to focus on the design of a mechanism for shaping prediction signals, which is called a prediction governor. In this study, we first extended our previous study to the input-affine nonlinear system case. Then, we analytically derived a solution to an optimal design problem of prediction governors. Finally, we applied the solution to an automatic driving control system, and demonstrated its usefulness through a numerical example and an experiment using a radio controlled car.

Prediction governors: Optimal solutions and application to electric power balancing control

“…Moreover, an optimal prediction governor is analytically derived in this paper, although the conference paper [22] focuses on a numerical design approach. Furthermore, although our result for nonlinear‐affine systems has been published in [23], the result is limited to the case that single‐input single‐output (SISO) system and one step time delay are considered.…”