CPC: Cascaded Predictive Control

We present Cascaded Predictive Control (CPC), a framework that decomposes a model predictive control horizon into multiple stages, each solved with the model and method best suited to its accuracy requirements. CPC transfers information across stages through the value function: the terminal cost produced by a downstream stage acts as a boundary condition for the preceding one, enabling heterogeneous combinations of different techniques and solvers. Two proof-of-concept studies illustrate the approach. First, a humanoid walking controller uses a double DDP cascade that schedules single rigid body and linear inverted pendulum models to extend the prediction horizon at a lower computational cost. Second, a cart-pendulum swing-up couples a DDP stage with a value function learned by an actor-critic policy; the hybrid controller succeeds where the policy alone fails. Although the examples are preliminary, they indicate that CPC can provide a flexible framework for achieving effective results with reduced computational effort by combining the strengths of different techniques.