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Robotics 2018, 7(2), 16;

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

Department of Mechanical Engineering, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Japan
Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma 630-0192, Japan
Author to whom correspondence should be addressed.
Received: 26 February 2018 / Revised: 29 March 2018 / Accepted: 3 April 2018 / Published: 4 April 2018
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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. View Full-Text
Keywords: prediction; signal shaping; automatic driving control prediction; signal shaping; automatic driving control

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Minami, Y.; Iwai, Y. Prediction Governors for Input-Affine Nonlinear Systems and Application to Automatic Driving Control. Robotics 2018, 7, 16.

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