We provide a comprehensive review and practical implementation of a recently developed
model predictive control (MPC) framework for controlling unknown systems using only …

Among the multitude of modern control methods, model predictive control (MPC) is one of
the most successful–. As noted in “Summary,” this success is largely due to the ability of …

This paper develops RLS-based MPC (RLSMPC), which uses multiple implementations of
recursive least squares (RLS) to perform model predictive control (MPC). RLSMPC uses …

With the emergence of vehicle-communication technologies, many researchers have
strongly focused their interest in vehicle energy-efficiency control using this connectivity. For …

This paper examines the applicability of Predictive Cost Adaptive Control (PCAC) to the
design of online adaptive autopilots for tail-controlled missiles. The plant dynamics of these …

This work proposes an algorithm for feedback ANC that does not require a prior secondary
path model and usually remains stable after fast secondary path changes, as other …

Model predictive control (MPC) for uncertain systems in the presence of hard constraints on
state and input is a non-trivial problem, and the challenge is increased manyfold in the …

This paper proposes a learning reference governor (LRG) approach to enforce state and
control constraints in systems for which an accurate model is unavailable. This approach …

In this paper, sampled-data output-feedback model predictive control (MPC) with online
identification is used to control nonlinear continuous-time plants. Using a linear model …

This paper studies the trajectory tracking of a constrained mobile robot under slippery
conditions. The goal is to propose a controller for real‐time operations of time‐varying …