【摘要】 针对具有时延的非线性系统提出了一种基于神经网络模型直接优化的预测控制．该方法利用递阶遗传算法（ＨＧＡ）通过对一批实际输入输出数据训练，得到对象的离线神经网络模型（ＮＮ模型）；对该模型进行多步递推得到对象预测模型．在线控制时，将误差修正引入性能函数以减少静差及由于时变、模型失配对系统造成的影响．仿真实验表明由它所构成系统均具有很好的动态响应和较强的鲁棒性更多还原

【Abstract】 This paper addresses a kind of predictive control based on neural network(NN) for nonlinear systems with time-delay. The off-line NN model is obtained by using Hierarchical Genetic Algorithms(HGA) to train a sequence data of input and output . Multi-step output predictions are obtained by mapping recursively NN model .The error rectification term is introduced into a performance function directly for on-line control.And it can overcome the influences of mismatched model and disturbances etc. Simulations show systems have good dynamic responses and robustness.更多还原