Algorithms for PID Controller 2019

Anastasios Dounis

ISBN 10: 3725848157 / ISBN 13: 9783725848157

Published by Mdpi AG, 2025

About this Item

Seller Inventory # LU-9783725848157

Bibliographic Details

• Title: Algorithms for PID Controller 2019
• Publisher: Mdpi AG
• Publication Date: 2025
• Language: English
• ISBN 10: 3725848157
• ISBN 13: 9783725848157
• Binding: Hardback
• Condition: New
• Dimensions: 16.99W x 1.6H x 24.41D centimeters 16.99 centimeters width by 1.6 centimeters height by 24.41 centimeters depth
• Weight: 594 grams

About this title

Synopsis

The reprint focuses on advanced PID controller-tuning algorithms in addition to conventional approaches based on mathematical controlled system analysis. Stavrov and al. proposed an improved version of a conventional PID controller based on a quadratic error model. De Moura Oliveira et al. proposed a PSO technique for PID controller design. Alimohammadi et al. introduced a multi-loop Model Reference Adaptive Control, leveraging a NARX model as the reference model, which was integrated with a Fractional Order PID. Alekseeva proposed a PD Steering Controller utilizing the predicted position on tracks for autonomous vehicles driven on slippery roads. A Neural PID controller for Unmanned Aerial Vehicles was presented by Avila et al., based on a Multilayer Perceptron trained with an Extended Kalman Filter. A study of six types of multi-loop model reference (ML-MR) control structures and design schemes for PID control loops is presented by Alagoz and al. Smeresky, Rizzo and Sands explore and analyze deterministic artificial intelligence composed of self-awareness statements along with a novel, optimal learning algorithm. Radac and Lala suggest a solution for the Output Reference Model tracking control problem, based on approximate dynamic programming and the Value Iteration (VI) algorithm for controller learning. A Kalman-Filter-Based tension control system for industrial Roll-to-Roll system is also presented by Hwang et al.