For constrained linear parameter varying (LPV) systems, this survey comprehensively
reviews the literatures on output feedback robust model predictive control (OFRMPC) over …

This article provides a solution to tube-based output feedback robust model predictive
control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded …

Model predictive controllers use dynamics models to solve constrained optimal control
problems. However, computational requirements for real-time control have limited their use …

Imitation learning (IL) can generate computationally efficient sensorimotor policies from
demonstrations provided by computationally expensive model-based sensing and control …

This paper designs an ellipsoidal tube‐based output feedback robust model predictive
control approach for linear systems with bounded disturbances and noises subject to …

This paper addresses the problem of designing output feedback controllers for constrained
linear systems subject to bounded process and measurement disturbances. The proposed …

In real-world applications, we often require reliable decision making under dynamics
uncertainties using noisy high-dimensional sensory data. Recently, we have seen an …

Linear models with additive unknown-but-bounded input disturbances are extensively used
to model uncertainty in robust control system design. Typically, the disturbance set is either …

When we rely on deep-learned models for robotic perception, we must recognize that these
models may behave unreliably on inputs dissimilar from the training data, compromising the …

Imitation learning (IL) can train computationally-efficient sensorimotor policies from a
resource-intensive model predictive controller (MPC), but it often requires many samples …