Algorithms for PID Controller 2019 - Couverture rigide

Couverture rigide

ISBN 10 : 3725848157 ISBN 13 : 9783725848157

Editeur : Mdpi AG, 2025

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.

Détails de l'édition

• Éditeur: Mdpi AG
• Date d'édition: 2025
• Langue: anglais
• ISBN 10: 3725848157
• ISBN 13: 9783725848157
• Reliure: Relié
• Nombre de pages: 182
• Éditeur: Dounis Anastasios
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible