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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science
Cong, Cuong Nguyen
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Mechanical Engineering

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Robust Adaptive Trajectory Tracking Sliding Mode Control for Industrial Robot Manipulator using Fuzzy Neural Network Xuan, Quynh Nguyen; Cong, Cuong Nguyen; Ba, Nghien Nguyen
Journal of Robotics and Control (JRC) Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i2.20722

Abstract

This paper presents a control method for a two-link industrial robot manipulator system that uses Fuzzy Neural Networks (FNNs) based on Sliding Mode Control (SMC) to investigate joint position control for periodic motion and predefined trajectory tracking control. The proposed control scheme addresses the challenges of designing a suitable control system that can achieve the required approximation errors while ensuring the stability and robustness of the control system in the face of joint friction forces, parameter variations, and external disturbances. The control scheme uses four layers of FNNs to approximate nonlinear robot dynamics and remove chattering control efforts in the SMC system. The adaptive turning algorithms of network parameters are derived using a projection algorithm and the Lyapunov stability theorem. The proposed control scheme guarantees global stability and robustness of the control system, and position is proven. Simulation and experiment results from a two-link IRM in an electric power substation are presented in comparison to PID and AF control to demonstrate the superior tracking precision and robustness of the proposed intelligent control scheme.
Recognition of plant leaf diseases based on deep learning and the chemical reaction optimization algorithm Ba, Nghien Nguyen; Thi, Nhung Nguyen; Quoc, Dung Vuong; Cong, Cuong Nguyen
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp447-458

Abstract

Agriculture plays a crucial role in developing countries such as Vietnam, where 70 percent of the population is employed in agriculture, and 57 percent of the social labor force works in the agricultural sector. Therefore, crop productivity directly affects the lives of many people. One of the primary reasons for reduced crop yields is plant leaf diseases caused by bacteria, fungi, and viruses. Hence, there is a need for a method to help farmers identify leaf diseases early to take appropriate action to protect crops and shift to smart agricultural production. This paper proposes lightweight deep learning (DL) models combined with a support vector machine (SVM), with hyperparameters fine-tuned by chemical reaction optimization (CRO), for detecting plant leaf diseases. The main advantage of the method is the simplicity of the architecture and optimization of the DL model’s hyperparameters, making it easily deployable on low hardware devices. To test the performance of the proposed method, experiments are performed on the PlantVillage dataset using Python. The superiority of the proposed method over the well-known visual geometry group-16 (VGG-16) and MobileNetV2 models is demonstrated by a 10% increase in accuracy prediction and a decrease of 5% and 66% in training time, respectively.
Comparison among search algorithms for hyperparameter of support vector machine optimization Nghien, Nguyen Ba; Cong, Cuong Nguyen; Thi, Nhung Nguyen; Dung, Vuong Quoc
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3802-3815

Abstract

Support vector machine (SVM) is widely used in machine learning for classification and regression tasks, but its performance is highly dependent on hyperparameter tuning. Therefore, fine-tuning these parameters is key to improving accuracy and generality. Recently, many researchers have focused only on applying different algorithms to optimize these parameters. There is a shortage of studies that compare the performance of these methods. Hence, research is needed to compare the performance of these algorithms for the hyperparameters of the SVM optimization problem. This paper compares five optimization algorithms for tuning SVM hyperparameters: grid search (GS), random search (RS), Bayesian optimization (BO), genetic algorithm (GA), and the novel chemical reaction optimization (CRO) algorithm. Experimental results on benchmark datasets such as iris, digits, wine, breast cancer Wisconsin, and credit card fraud demonstrate that CRO consistently outperforms other methods in terms of classification scoring metrics and computational time. It achieves improvements in accuracy, precision, recall, and F1-score of up to 1% on balanced datasets and up to 10% on highly imbalanced datasets such as credit card fraud. It also reduces computation time by up to 50% compared to GS, BO, and RS. These findings suggest that CRO is a promising approach for hyperparameter optimization (HPO) of SVM models.
Co-Authors Ba, Nghien Nguyen Dung, Vuong Quoc Nghien, Nguyen Ba Quoc, Dung Vuong Thi, Nhung Nguyen Xuan, Quynh Nguyen