Archive-gradient strategy enhanced teaching-learning-based optimization algorithm for cascade controller tuning in precision temperature control

doi:10.1016/j.tsep.2026.104756

Archive-gradient strategy enhanced teaching-learning-based optimization algorithm for cascade controller tuning in precision temperature control

2026-07-01

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DOI：10.1016/j.tsep.2026.104756

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Cascade control is effective in addressing time delays and large inertia in precision temperature control systems. The overall control performance is critically determined by parameter tuning. Intelligent optimization-based tuning methods have been widely applied to achieve optimal performance. However, existing optimization algorithms face significant challenges in rapidly and accurately attaining global optima, while optimization models struggle to yield satisfactory performance indices. In this study, an archive-gradient strategy is introduced into Teaching-Learning-Based Optimization (TLBO), resulting in a high-performance variant termed AGTLBO. The proposed strategy employs a greedy gradient estimation technique to significantly accelerate convergence, while developing an archive construction and utilization mechanism to effectively mitigate entrapment in local optima. AGTLBO is further applied to an optimization model incorporating disturbance rejection to prevent oscillations in cascade controller tuning, and this tuning method is implemented in an actual precision temperature control system. Comprehensive experiments on benchmark problems from CEC 2014, CEC 2020, and real-world applications demonstrate that AGTLBO delivers competitive or superior performance compared with recent state-of-the-art optimization algorithms, including some winners of CEC competitions. These results confirm that the archive-gradient strategy enables AGTLBO to achieve an excellent balance between exploitation and exploration. When applied to tuning-related optimization problems, AGTLBO identifies optimal controller parameters more efficiently and accurately than other algorithms. Furthermore, the tuning method, integrating AGTLBO with a disturbance rejection optimization model, effectively suppresses oscillations in the precision temperature control system and enhances overall performance.

Keywords:

Cascade control tuning

Teaching-Learning-Based Optimization

Gradient strategy

Archive strategy

Precision temperature control

Archive-gradient strategy enhanced teaching-learning-based optimization algorithm for cascade controller tuning in precision temperature control

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