基于神经网络的自治水下机器人广义预测控制

针对自治式水下机器人高度非线性和时变性的特点，提出了一种基于神经网络的水下机器人广义预测控制策略．利用改进型Elman网络作为多步预测模型，在对网络学习算法进行改进的基础上，实现了Elman网络的在线学习，并提出了用于求解神经广义预测控制律的灵敏度公式．进行了具有神经网络在线学习功能和不具有在线学习功能的水下机器人的速度控制实验，并就预测控制效果进行了对比分析．实验结果表明，具有自适应学习功能的水下机器人速度控制法的精度要优于不具有在线学习功能的速度控制法，且当水下机器人动态特性发生变化时具有较强的自适应能力．

Neural-Network-Based Generalized Predictive Control for Autonomous Underwater Vehicles

Considering the highly nonlinear and time-variant dynamics of autonomous underwater vehicles(AUVs),a generalized predictive control strategy based on neural network is presented.The modified Elman neural network is used as the multi-step predictive model and the neural network learning algorithm is improved.Online learning of the modified Elman network is realized,and the sensitivity formula to solve the neural generalized predictive control law is presented.Experiments are made on the velocity predictive control of AUVs with on-line and off-line neural network learning,and the predictive control results are analyzed contrastively.Experiment results show that the velocity control accuracy of the AUV with on-line learning is more accurate than that of the AUV with off-line learning,and the former has stronger adaptability when the AUV dynamics changes.