Modeling and controlling the descent operation of a fish robot using neural networks

• Published in: 2015 15th International Conference on Control, Automation and Systems (ICCAS) pp. 1920 - 1923
• Main Authors: Phi Luan Nguyen, Byung Ryong Lee, Kyung Kwan Ahn
• Format: Conference Proceeding
• Language: English
• Published: Institute of Control, Robotics and Systems - ICROS 01.10.2015
• Subjects: Biomimetics; descent; dynamic modeling; fish robot; flexible tail; pectoral fin; Propulsion; Robots
• DOI: 10.1109/ICCAS.2015.7364679
• ISSN: 2093-7121

This paper presents a neural networks model (NNM) and for modeling and identifying the nonlinear behavior of a fish robot. Firstly, a set of driving moment signals were applied to the fish robot in order to investigate the fish robot operation. Consequently, a neural networks model was constructed and an identification scheme based on Genetic Algorithm was developed. Validation results proved the ability of proposed scheme to tracking the descent operation of the fish robot. The combination of PID controller and NNM was implemented and successfully control fish robot follow given trajectories.