基于干扰精细估计与神经网络推力分配的载人潜水器控制

针对潜水器在水下运行时会受到洋流、参数摄动等多种干扰因素影响和潜水器的过驱动问题,设计一种基于干扰观测的反步控制器和基于神经网络二次规划的推力分配器的双层控制结构.首先,建立潜水器系统在洋流影响下的动力学模型;其次,将潜水器受到的干扰分为由洋流产生的干扰和由其他因素引起的干扰两部分,分别使用洋流观测器和非线性干扰观测器进行估计,并基于干扰观测信息利用反步法设计运动控制器;然后,针对潜水器的过驱动特性以及推进器的推力受限问题,提出一种基于神经网络二次规划的推力分配方法;最后,使用Matlab进行数值仿真,验证所提控制方法的有效性和优越性.结果表明,基于干扰精细估计与神经网络推力分配的潜水器运动控制系统具有干扰估计更加准确、推进系统的耗能最优,以及避免推进器的推力超限等优势.

Manned submersible vehicle control based on refined disturbance estimation and neural network thrust allocation

Aiming at the problem that the manned submersible vehicle is affected by ocean currents and over-actuated characteristics, a double-layer control structure is proposed for the manned submersible vehicle. First of all, a six-degree-of-freedoms dynamic model of the manned submersible vehicle system under the influence of ocean currents is established. Secondly, the disturbances of the submersible vehicle are divided into two parts: the disturbance caused by ocean currents and other disturbances. The ocean current observer and nonlinear disturbance observer are designed to estimate the ocean current disturbance and other disturbances, respectively. Based on the estimated disturbance information, a motion controller is designed using the back-stepping algorithm for the manned submersible vehicle. Then, a thrust allocation method based on neural network quadratic programming is proposed to address the over-actuated problem, as well as to meet the thrust constrains of the manned submersible vehicle. Finally, some numerical simulation results are given to demonstrate the effectiveness and superiority of the proposed control scheme. The superiorities of the proposed control scheme based on refined disturbance estimation and neural network thrust allocation are threefold: its capabilities to obtain more accurate disturbance estimation result, to achieve the optimal energy consumption, and to prevent the thrust exceeding the limit.