Research on Typical Methods of S Surface Controller Parameter Self-Tuning for Underwater Vehicles

Abstract:

S surface control is a simple and operative motion control algorithm for underwater vehicles, but it has two parameters requiring to be adjusted manually. In order to enhance the adaptability of S surface controller, the research of S surface controller parameter self-tuning methods based on rules and models is carried out. Firstly, combined with fuzzy control, parameter self-tuning method based on fuzzy rules is presented. Then by means of predictive control theory, model-based parameter self-tuning method is proposed. By introducing the nonlinear autoregressive moving average model, the prediction model of underwater vehicles is established using parallel Elman neural network, and the optimal parameters of S surface controller is obtained by constructing quadratic performance index function. The results of simulation experiments show that the response speed of S surface controller with parameter self-tuning modules is improved, and the parameter self-tuning methods is demonstrated feasible and effective.

• Periodical: Applied Mechanics and Materials (Volumes 365-366)
• Pages: 897-904
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.365-366.897
• Online since: August 2013
• Authors: Bin He, Da Peng Jiang, Guo Cheng Zhang, Ying Hao Zhang
• Keywords: Fuzzy Control, Parameter Self-Tuning, Predictive Control, Underwater Vehicle

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