Dynamically Embedded Model Predictive Control

The paper describes a continuous-time dynamic feedback law that mimics the behavior of a model predictive controller. The proposed control strategy is based on implementing a continuous-time reformulation of the primal-dual gradient descent to track the solution of the optimal control problem. The optimal control action is thus embedded in the internal state vector of the dynamic control law, which runs in parallel to the controlled system. Using input to state stability arguments, it is shown that if the rate of change of the dynamic control law is sufficiently large with respect to the plant dynamics, the interconnection between the plant and the proposed controller is exponentially stable. The effectiveness of the control scheme is demonstrated with a numerical example.

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