This paper develops a systematic data-based approach to the closed-loop feedback control of high-dimensional robotic systems using only partial state observation. We first develop a model-free generalization of the iterative Linear Quadratic Regulator (iLQR) to partially-observed systems using an Autoregressive Moving Average (ARMA) model, that is generated using only the input-output data. The ARMA model results in an information state, which has dimension less than or equal to the underlying actual state dimension. This open-loop trajectory optimization solution is then used to design a local feedback control law, and the composite law then provides a solution to the partially observed feedback design problem. The efficacy of the developed method is shown by controlling complex high dimensional nonlinear robotic systems in the presence of model and sensing uncertainty and for which analytical models are either unavailable or inaccurate.

翻译：本文仅使用部分状态观测,为高维机器人系统的闭环反馈控制开发了系统化的数据基控制方法。我们首先开发了对使用仅使用输入-输出数据生成的自动递减平均移动模型(ARMA)进行部分观察的系统,对迭代线性二次曲线调节器(iLQR)进行无模型化的常规化系统。ARMA模型的结果是一个信息状态,其尺寸小于或等于潜在的实际状态层面。这种开放通道轨迹优化解决方案随后用于设计地方反馈控制法,而综合法则则则为部分观测到的反馈设计问题提供了解决办法。在模型和感测不确定性存在的情况下控制复杂的高维非线性非线性机器人系统,并且分析模型要么不可用,要么不准确,要么不准确。