Embedding Constrained Model Predictive Control in a Continuous-Time Dynamic Feedback
For control engineers, this provides a novel continuous-time formulation of MPC that avoids discrete-time sampling issues and improves constraint satisfaction through a reference governor.
This paper introduces a continuous-time dynamic feedback implementation of model predictive control that embeds the optimal control solution into the controller's internal states, achieving asymptotic stability when controller dynamics are sufficiently fast. Augmenting with an Explicit Reference Governor significantly expands the set of feasible initial conditions.
This paper introduces a continuous-time constrained nonlinear control scheme which implements a model predictive control strategy as a continuous-time dynamic system. The approach is based on the idea that the solution of the optimal control problem can be embedded into the internal states of a dynamic control law which runs in parallel to the system. Using input to state stability arguments, it is shown that if the controller dynamics are sufficiently fast with respect to the plant dynamics, the interconnection between the two systems is asymptotically stable. Additionally, it is shown that, by augmenting the proposed scheme with an add-on unit known as an Explicit Reference Governor, it is possible to drastically increase the set of initial conditions that can be steered to the desired reference without violating the constraints. Numerical examples demonstrate the effectiveness of the proposed scheme.