This paper proposes a data-driven iterative feedback control method to efficiently solve finite time horizon, nonlinear, input constrained optimal control problems. The proposed method introduces a novel approach to combine an inexact system model with measured state information to reduce the cost and provide near-optimal control by approximately solving the optimal control problem along the trajectory of the real system, as opposed to solving it along the trajectory predicted by the inexact model. We present a new algorithm that implements the proposed method, establish the convergence and optimality properties of the proposed algorithm, and compare it to optimal feedback control and model-predictive control that solve the same optimal control problem along the trajectory predicted by the inexact model. Finally, we illustrate the generality of the proposed algorithm by approximately solving a challenging optimal control problem with unknown and changing dynamics.

中文翻译：

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迭代在线最优反馈控制

本文提出了一种数据驱动的迭代反馈控制方法，以有效解决有限时间范围、非线性、输入约束的最优控制问题。所提出的方法引入了一种新方法，将不精确的系统模型与测量的状态信息相结合，以通过沿真实系统的轨迹近似求解最优控制问题来降低成本并提供接近最优的控制，而不是沿轨迹求解。由不精确的模型预测。我们提出了一种新的算法来实现所提出的方法，建立所提出算法的收敛性和最优性，并将其与最优反馈控制和模型预测控制进行比较，后者沿着不精确模型预测的轨迹解决相同的最优控制问题。最后，