Partially-Observed Decoupled Data-based Control (POD2C) for Complex Robotic Systems
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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.
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