International Journal of Modelling, Identification and Control (5 papers in press)

Regular Issues

• Open Access

  Constant pressure PID control of non invasive intracranial pressure monitor based on wavelet neural network ( Free Full-text Access )

  by Gaigai Zhang, Qingyun Yang 
  Abstract: To enhance the accuracy of constant pressure control in non-invasive intracranial pressure monitors, a study on constant pressure PID control of non-invasive intracranial pressure monitors based on wavelet neural networks is proposed. Collect intracranial pressure signals from non-invasive intracranial pressure monitors through sensors and preprocess them using wavelet filtering; Construct a wavelet neural network to learn the intrinsic patterns and features in signal data, and estimate the true intracranial pressure values; Based on the estimation results from the wavelet neural network, the parameters of the PID controller are dynamically adjusted and input as feedback signals. Adjust the non-invasive intracranial pressure monitor according to the magnitude and trend of the deviation value. The results show that the proposed method can achieve a steady-state error of less than 0.1mmHg, with a maximum overshoot of 6.2% and a maximum sensitivity of only 0.20, demonstrating high control accuracy and stability.
  Keywords: non-invasive intracranial pressure monitor; wavelet neural network; PID controller; constant pressure control.
  DOI: 10.1504/IJMIC.2025.10073014
   
  

• Open Access

  An intelligent distribution network source grid load storage optimisation scheduling based on improved ant lion algorithm ( Free Full-text Access )

  by Guangyong Zheng, Junjie Zhang, Weiquan Ye, Jin Yi, Lianghao Huang, Hui Jiang 
  Abstract: To enhance the stability of the distribution network's load and minimize its loss rate, an optimized scheduling approach for intelligent distribution network source load storage is introduced, leveraging an improved ant lion algorithm. Firstly, mathematical modeling is conducted on the diversified energy supply capacity within the intelligent distribution network, and a charging and discharging model is designed for the energy storage device. Secondly, a comprehensive optimization scheduling system is established with the goal of reducing costs and minimizing pollutant gas emissions, and multiple constraint factors are carefully planned. Finally, by improving the ant lion algorithm, a balance between global search and local optimization is achieved. The results of the experiments demonstrate that the proposed technique closely approximates the actual load in terms of overall load correspondence within the distribution network, with the power grid experiencing a consistent loss rate of approximately 3% across all periods.
  Keywords: intelligent distribution network; improve the ant lion algorithm; source network load storage; optimise scheduling.
  DOI: 10.1504/IJMIC.2025.10073123
   
  

• Open Access

  Variable frequency speed control method for wheeled grain harvester based on fuzzy control theory ( Free Full-text Access )

  by Haiyan Hu, Chang Su 
  Abstract: With the advancement of agricultural mechanization, higher requirements have been proposed for the work efficiency, automation level, and energy-saving performance of grain harvesters. Aiming to reduce the coefficient of variation of data and speed control overshoot, this paper designs a variable frequency speed control method for wheel grain harvesters based on fuzzy control theory. A frequency conversion mathematical model for wheeled grain harvesters was constructed, with motor speed online estimation achieved through adaptive disturbance rejection design and adaptive estimation model. Finally, fuzzy rules, feedforward compensation, and PID controller optimization were utilized to enable dynamic parameter adjustment and precise speed control. Experimental results demonstrated that after applying this method, the speed data coefficient of variation decreased from 0.06 to 0.02, with maximum speed overshoot reaching only 2.97%.
  Keywords: wheel grain harvester; variable frequency speed regulation; speed control; fuzzy control PID controller; feedforward compensation mechanism.
  DOI: 10.1504/IJMIC.2025.10073125
   
  
• Outlier detection algorithm based on deviation characteristic  
  by Yong Wang, Hongbin Wang, Pengcheng Sun, Xinliang Yin 
  Abstract: Outlier mining focuses on researching rare events through detection and analysis to dig out the valuable knowledge from them. In the static data set environment, the traditional LOF algorithm calculates the local outlier factor through the whole data set and requires a lot of computing time. To solve this problem, the algorithm divides the data space into grids, and calculates the local outlier factor based on the centroids of the grids. Since the grid number is less than data point number, the time complexity is obviously reduced under acceptable error. When the new data points are added, it can rapidly detect outliers. The contrast experiment results show that the new algorithm can reduce the computation time and improve the efficiency, while achieving comparable accuracy.
  Keywords: outlier detection; local outlier factor; deviation characteristic; fast LOF detection algorithm.
  
  
• A lightweight attention mechanism and self-supervised denoising approach for robust vehicle detection in adverse weather conditions  
  by Lina Sun 
  Abstract: Vehicle detection under adverse weather conditions remains a major challenge due to severe image degradation and complex noise. Traditional denoising methods often fail to retain essential features, while lightweight models typically trade accuracy for efficiency, limiting real-time application. This paper presents a self-supervised denoising framework tailored for foggy and rainy scenarios. It includes three modules: a global perception mask mapper for identifying noise regions, a denoising network that separates clean and noisy components, and a regularised re-visibility loss to enhance performance in blind spots. To ensure deployment feasibility, a lightweight attention module based on the general meta-mobile block is introduced, balancing speed and accuracy. Experiments on benchmark datasets demonstrate notable gains in image clarity and vehicle detection accuracy. The framework offers a robust, efficient solution for real-world systems like autonomous vehicles, and lays the groundwork for future research on adaptive denoising in low-visibility environments.
  Keywords: self-supervised learning; image denoising; vehicle detection; attention mechanism; model lightweighting.
  DOI: 10.1504/IJMIC.2025.10072537