A lot of automatic feedback control and learning tasks carried out on many dynamical
systems still fundamentally rely on a form of proportional–integral–derivative (PID) control …

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 …

Nonlinear system identification is an extremely broad topic, since every system that is not
linear is nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld …

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 …

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 …

We experim ented the system identification and control of an activate d sludge process by
using an au to regressive model in an real was tewater treatment plant. The control system …

System identification is the art and science of building mathematical models of dynamic
systems from observed input–output data. It can be seen as the interface between the real …

Model predictive control (MPC) is an enabling technology in applications requiring
controlling physical processes in an optimized way under constraints on inputs and outputs …

Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

Real-time adaptation is imperative to the control of robots operating in complex, dynamic
environments. Adaptive control laws can endow even nonlinear systems with good trajectory …