The behavioral approach to systems theory, put forward 40 years ago by Jan C. Willems,
takes a representation-free perspective of a dynamical system as a set of trajectories. Till …

This paper provides a review of model predictive control (MPC) methods with active
uncertainty learning. System uncertainty poses a key theoretical and practical challenge in …

We consider the problem of optimal trajectory tracking for unknown systems. A novel data-
enabled predictive control (DeePC) algorithm is presented that computes optimal and safe …

In this article, we discuss connections between sequential system identification and control
for linear time-invariant systems, often termed indirect data-driven control, as well as a …

In this article we study the problem of finite-time constrained optimal control of unknown
stochastic linear time-invariant (LTI) systems, which is the key ingredient of a predictive …

During the past half century, modern control theory has developed greatly and many
branches and subfields have emerged, for example, linear system theory, optimal control …

In this paper, a data-driven model-free adaptive control (MFAC) approach is proposed
based on a new dynamic linearization technique (DLT) with a novel concept called pseudo …

In this work, a novel data-driven control approach, model-free adaptive control, is presented
based on a new dynamic linearization technique for a class of discrete-time single-input and …

At publication, The Control Handbook immediately became the definitive resource that
engineers working with modern control systems required. Among its many accolades, that …

Bayesian inference provides a consistent method for the extraction of information from
physics experiments even in ill-conditioned circumstances. The approach provides a unified …