This paper develops a data-based approach to the closed-loop output feedback control of nonlinear dynamical systems with a partial nonlinear observation model. We propose an information state based approach to rigorously transform the partially observed problem into a fully observed problem where the information state consists of the past several observations and control inputs. We further show the equivalence of the transformed and the initial partially observed optimal control problems and provide the conditions to solve for the deterministic optimal solution. We develop a data based generalization of the iterative Linear Quadratic Regulator (iLQR) to partially observed systems using a local linear time varying model of the information state dynamics approximated by an Autoregressive moving average (ARMA) model, that is generated using only the input-output data. This open-loop trajectory optimization solution is then used to design a local feedback control law, and the composite law then provides an optimum solution to the partially observed feedback design problem. The efficacy of the developed method is shown by controlling complex high dimensional nonlinear dynamical systems in the presence of model and sensing uncertainty.

翻译：本文提出了一种基于数据的闭环输出反馈控制方法，用于处理具有部分非线性观测模型的非线性动态系统。我们提出一种基于信息状态的方法，将部分观测问题严格转化为完全观测问题，其中信息状态由过去若干观测值与控制输入构成。进一步证明了转化后的问题与原始部分观测最优控制问题的等价性，并给出了确定性最优解的求解条件。我们开发了一种基于数据的迭代线性二次型调节器(iLQR)泛化方法，通过使用输入-输出数据生成的局部线性时变信息状态动态模型(由自回归滑动平均(ARMA)模型近似)，将其应用于部分观测系统。该开环轨迹优化解随后用于设计局部反馈控制律，复合控制律最终为部分观测反馈设计问题提供最优解。通过在存在模型与感知不确定性的复杂高维非线性动态系统控制中验证了所提方法的有效性。