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A comparative study of MPC and optimised PID control
Abstract
The paper presents a comparative study between the performance of model predictive control (MPC) and optimised proportional-integral-differential (PID) control on different systems. An objective function derived from rise time, settling time, percentage overshoot, and steady-state error is minimised in order to obtain the gains of the PID controller. The bacterial foraging (BF) algorithm is used to tune the parameters of the PID controller through performance optimisation of the system. System performance characteristics are compared to another controller designed based on MPC. The rise time, settling time, percentage overshoot, integral absolute error (IAE), and integral time-multiplied absolute error (ITAE) of the controllers are compared when acting on systems of different orders. Controller testing and performance comparison are carried out on first, second, third, forth, and fifth order systems. Results indicate that MPC consistently outperforms optimised PID control in all cases.
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“A PID controller is a”
From Research on Energy-Saving Optimization of Mushroom Growing Control Room Based on Neural Network Model Predictive Control (2025) · cites this paper as [8]
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Song, Y., Zheng, W., Guo, G., Wang, M., Luo, C., Chen, C., & Li, Z. (2025) · Energies · doi:10.3390/en18205550 · Cited by 3
Metadata
- Authors
- Salem, F. M., Mosaad, M. I., & Awadallah, M. A.
- Affiliations
- Electronics Research Institute (EG)
- Higher Technological Institute (EG)
- Zagazig University (EG)
- Venue
- International Journal of Industrial Electronics and Drives
- Publisher
- Inderscience Publishers
- Published
- 2015
- Volume
- 2
- Issue
- 4
- Pages
- 242
- ISSN
- 1757-3874 (ISSN-L)
- 1757-3882
- OpenAlex
- W2343136382
- Cited by
- 17
- References
- 0
- Citations by year
- 2026: 4 · 2025: 2 · 2024: 4 · 2023: 1 · 2022: 4 · 2021: 1 · 2020: 1
- FWCI
- 0.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Industrial Electronics and Drives (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Design
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Keywords
- model predictive control, PID controller tuning, bacterial foraging optimization, controller performance comparison, rise time, settling time, overshoot, steady-state error, integral absolute error, high-order systems
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