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For optimal control problems that involve planning and following atrajectory, two degree of freedom (2DOF) controllers are a ubiquitously usedcontrol architecture that decomposes the problem into a trajectory generationlayer and a feedback control layer. However, despite the broad use andpractical success of this layered control architecture, it remains a designchoice that must be imposed $a\ priori$ on the control policy. To address thisgap, this paper seeks to initiate a principled study of the design of layeredcontrol architectures, with an initial focus on the 2DOF controller. We showthat applying the Alternating Direction Method of Multipliers (ADMM) algorithmto solve a strategically rewritten optimal control problem results in solutionsthat are naturally layered, and composed of a trajectory generation layer and afeedback control layer. Furthermore, these layers are coupled via Lagrangemultipliers that ensure dynamic feasibility of the planned trajectory. Weinstantiate this framework in the context of deterministic and stochasticlinear optimal control problems, and show how our approach automatically yieldsa feedforward/feedback-based control policy that exactly solves the originalproblem. We then show that the simplicity of the resulting controller structuresuggests natural heuristic algorithms for approximately solving nonlinearoptimal control problems. We empirically demonstrate improved performance ofthese layered nonlinear optimal controllers as compared to iLQR, and highlighttheir flexibility by incorporating both convex and nonconvex constraints.

Augmented Lagrangian Methods as Layered Control Architectures