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All Journal International Journal of Electrical and Computer Engineering
Tiep, Do Khac
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Computer Science & IT Electrical & Electronics Engineering

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Fuzzy-proportional-integral-derivative-based controller for stable control of unmanned aerial vehicles with external payloads Tiep, Do Khac; Tien, Nguyen Van
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5094-5106

Abstract

In the paper, a proportional derivative (PD) controller and a fuzzy system tuning gains from proportional integral derivative controller are applied to stabilize an unmanned aerial vehicle (UAV), to control the attitude. Inputs of fuzzy logical controller consist of the speed required for the distance between the current position of quadcopter and the defined reference point and differences between orientation angles and variance in differences. Outputs of fuzzy logical controller consist of the proportional integral derivative coefficients which make pitch, roll, yaw and height values. The fuzzy-PD control algorithm is real-time applied to the quadcopter in MATLAB/Simulink environment. Based on data from experimental studies, although both classical proportional integral derivative controller and fuzzy-PD controller have accomplished to track a defined trajectory with the quadcopter.
An energy-optimized A* algorithm for path planning of autonomous underwater vehicles in dynamic flow fields Tiep, Do Khac; Tien, Nguyen Van; Thanh, Cao Duc
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i2.pp753-765

Abstract

This paper presents the development and implementation of an energy-optimized A* algorithm for autonomous underwater vehicle (AUV) path planning in these complex environments. The core of the approach is the integration of a computationally efficient flow field model and a detailed AUV energy consumption model directly into the A* search heuristic. The energy model considers factors such as drag forces, relative velocity between the AUV and the flow, and AUV maneuvering. The A* cost function is modified to prioritize paths that minimize the predicted total energy expenditure, while simultaneously ensuring obstacle avoidance and path feasibility. The algorithm was implemented and validated using a simulated environment with varying flow conditions. Results demonstrate that the proposed energy-optimized A* algorithm achieves a significant reduction in energy consumption – up to 50% in tested scenarios – compared to a standard A* implementation, while successfully generating collision-free and dynamically feasible paths. This work contributes a practical and effective solution for energy-aware AUV navigation in dynamic underwater environments, enabling longer mission durations and improved operational efficiency.
Co-Authors Thanh, Cao Duc Tien, Nguyen Van