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.

Adaptive S Surface Controller for Hover Control of Underwater Vehicles

“…Raw domain of inputs and outputs are related to working environments and motion capacity of underwater vehicles. It's necessary to make a preliminary estimation of the basic range before experiments, and modify gradually in the subsequent debugging [5].…”