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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) TELKOMNIKA (Telecommunication Computing Electronics and Control)
Anass Mansouri
Sidi Mohamed Ben Abdellah University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : telkomnika telecommunication computing electronics and control

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Parallel field programmable gate array implementation of the sum of absolute differences algorithm used in the stereoscopic system Mohamed Sejai; Anass Mansouri; Saad Bennani Dosse; Yassine Ruichek
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 20, No 6: December 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v20i6.22852

Abstract

Stereo vision is a popular method for an artificial vision-based environment perception system used in various applications such as intelligent transportation. With two cameras, the disparity map is calculated to find the distance and depth of objects in front of a moving vehicle. The key element of the stereoscopic system is based on the sum of absolute differences (SAD) algorithm, which is the most repeated operation in the stereo matching subsystem; however, this algorithm requires a very intensive processing time, statistical analysis show that the SAD block can consume more than 80% of the overall processing time of the algorithm. In this paper we propose a highly efficient hardware architecture of the SAD algorithm for real time stereo matching, the proposed architecture is established by a hierarchical parallel architecture of the SAD block, and verified by simulation and successfully implemented in Cyclone IV field programmable gate array (FPGA), it provides a significant reduction of processing time and the performance of the stereo imaging system is able to achieve 30 frames per second of 640×480 resolution color images.
Hybrid neural network-augmented model predictive control for differential-drive robot tracking Taoufik Belkebir; Hicham Belkebir; Anass Mansouri
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 5: October 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i5.27870

Abstract

Nonlinear model predictive control (NMPC) is widely adopted for mobile robot trajectory tracking, yet the interaction between transcription methods and learning-based enhancements remains unstudied for differential-drive platforms. This paper presents a unified CasADi/Interior Point OPTimizer (IPOPT) evaluation of fourth-order Runge-Kutta (RK4) multiple shooting (MS) and Hermite-Simpson (HS) direct collocation on a TurtleBot3 Burger across ten scenarios spanning five trajectory families, model mismatch, and sensor noise. A ?-ambiguity in the standard ???2 (??) heading cost is identified and corrected, reducing figure-eight tracking error by approximately 95%. RK4 MS achieves superior baseline accuracy in 9 of 10 experiments (? < 0.01). Three hybrid neural approaches are evaluated: warm-starting reduces solve time by 16% with no accuracy loss; adaptive transcription selection outperforms the baseline in 3 of 10 experiments; and neural approximate control achieves sub-millisecond inference but degrades on unseen trajectories (51%-64% fallback rate). A generalization hierarchy emerges: solver-in-the-loop methods generalize without measurable degradation, whereas solver-replacement methods require trajectory-specific training. These findings suggest that solver involvement is a key factor influencing generalization in learning-augmented predictive control. All results are statistically validated across 500 simulations.
Co-Authors Ali Ahaitouf Hicham Belkebir Jawhra El Hmidi Mohamed Sejai Saad Bennani Dosse Taoufik Belkebir Yassine Ruichek