The simplicity, transparency, reliability, high efficiency and robust nature of PID controllers
are some of the reasons for their high popularity and acceptance for control in process …

This article provides a study of modern and classical approaches used for PID tuning and its
applications in various domains. Most of the control systems that are implemented to date …

We present a reinforcement learning framework, called Programmatically Interpretable
Reinforcement Learning (PIRL), that is designed to generate interpretable and verifiable …

Many industrial processes utilize proportional-integral-derivative (PID) controllers due to
their practicality and often satisfactory performance. The proper controller parameters …

Covers PID control systems from the very basics to the advanced topics This book covers the
design, implementation and automatic tuning of PID control systems with operational …

Deep reinforcement learning (RL) is an optimization-driven framework for producing control
strategies for general dynamical systems without explicit reliance on process models. Good …

The vast majority of automatic controllers used to compensate industrial processes are PI or
PID type. This book comprehensively compiles, using a unified notation, tuning rules for …

System Identification shows the student reader how to approach the system identification
problem in a systematic fashion. The process is divided into three basic steps: experimental …

The paper describes procedures for automatic tuning of regulators of the PID type to
specifications on phase and amplitude margins. The key idea is a simple method for …

Proportional–integral–derivative (PID) controllers are the most adopted controllers in
industrial settings because of the advantageous cost/benefit ratio they are able to provide …