| Author Name | Affiliation | Postcode | | Qi Li* | 1. School of Control Science and Engineering, Dalian University of Technology, Dalian 116023, China
2. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116023, China | 116023 | | Menghan Yang | 1. School of Control Science and Engineering, Dalian University of Technology, Dalian 116023, China
2. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116023, China | 116023 | | Shifa Cui | 1. School of Control Science and Engineering, Dalian University of Technology, Dalian 116023, China
2. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116023, China | 116023 | | Kun Han | 1. School of Control Science and Engineering, Dalian University of Technology, Dalian 116023, China
2. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116023, China | 116023 |
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| Abstract: |
| The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control, adversely affecting its precision. This makes the control of such systems a significant and challenging research problem. To address this, a novel Neural Network Predictive Control (NNPC) algorithm is proposed, which integrates an Improved Deep Belief Network (IDBN) with an Improved Whale Optimization Algorithm (IWOA). First, the IDBN acts as a high-precision nonlinear prediction model, significantly improving multi-step prediction accuracy. Second, the IWOA is employed to optimize the predictive controller, featuring three major improvements: an improved population initialization, a modified convergence factor update mechanism, and an added disturbance strategy, which collectively accelerate convergence and enhance global search capability. Finally, simulation results demonstrate that the proposed NNPC algorithm achieves superior set-point tracking performance and strong robustness against external disturbances. |
| Key words: Industrial refrigeration systems Model predictive control Time-delay Whale optimization algorithm Deep belief network |
| DOI：10.11916/j.issn.1005-9113.2025061 |
| Clc Number:TP273+.5 |
| Fund: |
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| Descriptions in Chinese: |
| The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control, adversely affecting its precision. This makes the control of such systems a significant and challenging research problem. To address this, a novel Neural Network Predictive Control (NNPC) algorithm is proposed, which integrates an Improved Deep Belief Network (IDBN) with an Improved Whale Optimization Algorithm (IWOA). First, the IDBN acts as a high-precision nonlinear prediction model, significantly improving multi-step prediction accuracy. Second, the IWOA is employed to optimize the predictive controller, featuring three major improvements: an improved population initialization, a modified convergence factor update mechanism, and an added disturbance strategy, which collectively accelerate convergence and enhance global search capability. Finally, simulation results demonstrate that the proposed NNPC algorithm achieves superior set-point tracking performance and strong robustness against external disturbances. |
