提出了一种包含在线采样、离线学习两个阶段的基于数据驱动的迭代自适应动态规划(ADP)算法，仅通过在线数据，解决输入约束的连续未知模型的非线性系统的H∞控制问题。通过策略迭代(PI）和迭代强化学习(IRL)方法推导出无模型(HJI)方程。构建3个神经网络，在线采集系统数据结束后，利用离线学习方法，近似求解无模型HJI方程，进而得到值函数、控制策略和扰动策略，神经网络的未知参数通过最小二乘方法求解。仿真结果验证了算法的可行性。

A data-driven iterative Adaptive Dynamic Programming (ADP) algorithm including online sampling and offline learning is proposed. The H∞ control problem of the nonlinear system with input constraints and unknown models is solved only by online data. The model-free Hamiton-Jacobi-Isaacs (HJI) equation is derived by using the methods of Policy Iteration (PI) and Iteration Reinforcement Learning (IRL).Three neural networks are constructed. After the online acquisition of system data is completed, the off-line learning method is used to approximately solve the model-free HJI equation, and then the value function, control strategy and disturbance strategy are obtained. The parameter of the neural network is solved by the least squares method. Simulation results verify the feasibility of the algorithm.