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.
Highlights
In recent years, automatic driving control has attracted attention, and various studies on this topic have been undertaken [1,2]
Many studies have been conducted on various prediction methods, such as Kalman filtering and machine learning, and automatic driving control systems based on such methods have been proposed [8,9,10,11,12,13]
This paper presents an extension of the authors’ previous study [18], with the addition of a proof of the optimality for the derived prediction governor and the results of an experiment using a radio controlled car
Summary
Automatic driving control has attracted attention, and various studies on this topic have been undertaken [1,2]. The prediction governor was applied to an automatic driving control system, and the usefulness of the proposed method was validated through numerical simulations and experiments. Based on this initiative, it is likely that the practical applicability of the prediction governor can be demonstrated. The prediction governor is based on the predicted low accuracy signal r (t), the past high accuracy signal r (t − 1), and the model information It minimizes the output difference between the system driven by the shaped signal v and that driven by r. The ∞-norm of discrete-time signal e := (e(0), e(1), . . .) is expressed by kek∞ := supt∈{0}∪N |e(t)|
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