基于辨识模型集的无人半潜水下机器人系统深度动态滑模控制切换策略研究

BQ-01无人半潜水下机器人深度运动模型参数未知，无法为基于模型的控制策略提供控制参数，导致该系统不易快速达到预期的性能指标要求，为此提出一种多辨识模型动态滑模控制方法。该控制方法采用平均拟合偏差方法，减少了系统辨识过程中出现过多冗余辨识模型参数组，以切换的方式为动态滑模控制方法选取最佳模型参数；同时采用状态反馈方法实现滑模面抖动指数衰减，从而解决了系统深度运动调节问题。BQ-01系统湖泊试验结果表明，所提辨识方法能够为滑模控制策略提供最佳控制参数，且提高了系统运动控制品质。

Switching Strategy of Dynamic Sliding Mode Control Based on Multiple Identification Model Set for Unmanned Semi-submersible Vehicle

The control parameters can not be provided for the model-based control algorithm,which leads to the undesirable control performances of the system since the depth motion model parameters regarding the unmanned semi-submersible vehicles (USVs) BQ-01 are unknown.For this purpose,a multi-identification model-based dynamic sliding mode control algorithm is presented for the investigation of system's control problem on the desired depth.In the proposed algorithm,the average-fitting-error method is used to reduce the excessive redundant model parameters,and thus the optimum model parameters is provided for the control algorithm by switching methods.And the state feedback method is used to bring about the chattering exponential decay of sliding mode controller so as to reduce the settling time.The experimental results of lake trials demonstrates that the proposed algorithm could offer the best model parameters for sliding mode control,and the dynamic sliding mode control with multi-identification models is capable of ensuring BQ-01 system to achieve the ideal control performance.