一种基于信息状态的非线性系统最优输出反馈控制方法
An Information-state based Approach to the Optimal Output Feedback Control of Nonlinear Systems
AI总结：
针对带部分非线性观测的非线性系统，提出基于信息状态的输出反馈控制法，将部分观测问题转化为完全观测问题，结合ARMA模型与iLQR实现数据驱动的最优控制，在模型和传感不确定下验证了有效性。
摘要：
本文针对具有部分非线性观测模型的非线性动力系统，提出了一种基于数据的闭环输出反馈控制方法。我们提出了一种基于信息状态的方法，可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和控制输入组成。我们进一步证明了转化后的问题与初始部分观测最优控制问题的等价性，并给出了求解确定性最优解的条件。我们利用由自回归滑动平均（ARMA）模型逼近的信息状态动力学局部线性时变模型，将迭代线性二次调节器（iLQR）基于数据的推广形式拓展到部分观测系统，该模型仅利用输入输出数据生成。随后，这种开环轨迹优化解被用于设计局部反馈控制律，而复合控制律则为部分观测反馈设计问题提供了最优解。通过在存在模型和传感不确定性的情况下控制复杂高维非线性动力系统，验证了所提方法的有效性。
英文摘要：
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.
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