Model predictive control (MPC) has demonstrated exceptional success for the high-
performance control of complex systems. The conceptual simplicity of MPC as well as its …

In the past ten years many Stochastic Model Predictive Control (SMPC) algorithms have
been developed for systems subject to stochastic disturbances and model uncertainties …

Safe autonomous navigation of microair vehicles in cluttered dynamic environments is
challenging due to the uncertainties arising from robot localization, sensing, and motion …

With decades of successful application of model predictive control (MPC) to industrial
processes, practitioners are now focused on ease of commissioning, monitoring, and …

Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of
systems with stochastic uncertainty. A key feature of SMPC is the inclusion of chance …

Constraint tightening to non-conservatively guarantee recursive feasibility and stability in
Stochastic Model Predictive Control is addressed. Stability and feasibility requirements are …

Driving requires forecasts. Forecasted movements of objects in the driving scene are
uncertain. Inevitably, decision and control algorithms for autonomous driving need to cope …

Nonlinear model predictive control (NMPC) is one of the few control methods that can
handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) …

In this letter, we consider the problem of deriving and incorporating accurate dynamic
models for model predictive control (MPC) with an application to quadrotor control. MPC …

Batteries are complex systems that need to be properly managed to guarantee safe and
optimal operations. Model predictive control (MPC) is an advanced control strategy that …