  This paper proposes a novel tube-based Model Predictive Control (MPC)
framework for tracking varying setpoint references with linear systems subject
to additive and multiplicative uncertainties. The MPC controllers designed
using this framework exhibit recursively feasible for changing references, and
robust asymptotic stability for piecewise constant references. The framework
leverages configuration-constrained polytopes to parameterize the tubes,
offering flexibility to optimize their shape. The efficacy of the approach is
demonstrated through two numerical examples. The first example illustrates the
theoretical results, and the second uses the framework to design a lane-change
controller for an autonomous vehicle.
