Vertical hierarchical MPC for constrained linear systems Abstract   A hierarchical Model Predictive Control (MPC) formulation is presented for discretetime linear systems with state and input constraints. A vertical hierarchical controller, with one controller per level, reduces the computational burden associated with the solution of centralized MPC having long prediction horizons and short time steps. To guarantee satisfaction of state and input constraints in the presence of both known and unknown disturbances, a robust MPC formulation is used at each level while waysets are used as a novel coordination mechanism between controllers at different levels. These waysets are implemented as terminal state constraints on lowerlevel controllers and are computed online based on the optimal state trajectory of upperlevel controllers. To achieve the computational efficiency necessary for online calculation, waysets are represented as constrained zonotopes. State and input constraint satisfaction is proven for a hierarchical controller with an arbitrary number of levels and two numerical examples demonstrate the key features, performance, and scalability of the approach.

Cruise Control for a TwoWheeled Mobile Vehicle Using Its Mixed Logical Dynamical System Model This paper proposes a cruise control for a twowheeled mobile vehicle on a twodimensional plane. The cruise control is executed using model predictive control (MPC), such that input and state constraints can be addressed explicitly. For realtime execution of MPC, we model the dynamics of the vehicle as a mixed logical dynamical system rather than an underlying nonlinear model. The cruise control is then reduced to a mixed integer quadratic programming problem, for which efficient solvers using the convexities are available. Simulations and experiments demonstrate the effectiveness of the proposed cruise control through a computation time comparison with the nonlinear MPC method.