Algorithms for PID Controller 2019
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...
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| Format: | Online |
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| Jezik: | engleski |
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MDPI - Multidisciplinary Digital Publishing Institute
2026
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| Online pristup: | https://directory.doabooks.org/handle/20.500.12854/170652 |
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| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-170652 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1706522026-01-02T16:21:32Z Algorithms for PID Controller 2019 Dounis, Anastasios Evolutionary PID control adaptive fuzzy PID control robust PID algorithms uncertainty of PID algorithm predictive control interval type-2 fuzzy PID controller reinforcement learning algorithm sliding model Lyapunov approach Kalman filtering implementations 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. 2026-01-02T16:21:28Z 2026-01-02T16:21:28Z 2025 book 978-3-7258-4815-7 https://directory.doabooks.org/handle/20.500.12854/170652 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11380 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4816-4 10.3390/books978-3-7258-4816-4 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 978-3-7258-4815-7 182 CH open access |
| spellingShingle | Evolutionary PID control adaptive fuzzy PID control robust PID algorithms uncertainty of PID algorithm predictive control interval type-2 fuzzy PID controller reinforcement learning algorithm sliding model Lyapunov approach Kalman filtering implementations Algorithms for PID Controller 2019 |
| title | Algorithms for PID Controller 2019 |
| title_full | Algorithms for PID Controller 2019 |
| title_fullStr | Algorithms for PID Controller 2019 |
| title_full_unstemmed | Algorithms for PID Controller 2019 |
| title_short | Algorithms for PID Controller 2019 |
| title_sort | algorithms for pid controller 2019 |
| topic | Evolutionary PID control adaptive fuzzy PID control robust PID algorithms uncertainty of PID algorithm predictive control interval type-2 fuzzy PID controller reinforcement learning algorithm sliding model Lyapunov approach Kalman filtering implementations |
| topic_facet | Evolutionary PID control adaptive fuzzy PID control robust PID algorithms uncertainty of PID algorithm predictive control interval type-2 fuzzy PID controller reinforcement learning algorithm sliding model Lyapunov approach Kalman filtering implementations |
| url | https://directory.doabooks.org/handle/20.500.12854/170652 |