Constrained Neural Network-based Model Predictive Control for Quadrotors Using the Sine Cosine Algorithm

Authors

  • Mohamed Benrabah
    Affiliation
    Laboratory of Robotics, Parallelism and Embedded Systems, Department of Automatic, Faculty of Electrical Engineering, University of Science and Technology Houari Boumediene, P. O. B. 32, Bab Ezzouar, 16111 Algiers, Algeria
  • Mohamed Lamine Fas
    Affiliation
    Laboratory of Electrical Systems and Remote Control, Department of Automatic and Electrical Engineering, Faculty of Technology, Blida 1 University, P. O. B. 270, 09000 Blida, Algeria
  • Chafea Stiti
    Affiliation
    Laboratory of Electrical Systems and Remote Control, Department of Automatic and Electrical Engineering, Faculty of Technology, Blida 1 University, P. O. B. 270, 09000 Blida, Algeria
  • Kamel Kara
    Affiliation
    Laboratory of Electrical Systems and Remote Control, Department of Automatic and Electrical Engineering, Faculty of Technology, Blida 1 University, P. O. B. 270, 09000 Blida, Algeria
https://doi.org/10.3311/PPee.40289

Abstract

In this paper, an efficient nonlinear control algorithm, called Constrained Neural Networks based Model Predictive control using Sine Cosine Algorithm (CNNMPC-SCA) is developed to control the dynamics of quadrotors. The main objective is to design an efficient controller for quadrotors that ensures satisfactory performance while minimizing the gap between the quadrotor positions and the reference trajectories. Indeed, a novel dynamic model architecture of the quadrotor is developed using several Nonlinear Autoregressive Exogenous (NARX) neural networks, this model aims to accurately predict the future behavior of the quadrotor within a short and acceptable time frame, making it suitable for implementation in the control process. The designed model was validated and then integrated into the CNNMPC-SCA algorithm. Furthermore, the metaheuristic algorithm known as the Sine Cosine Algorithm (SCA) was modified and employed to solve the non-convex, nonlinear optimization problem of the proposed predictive controller. To assess the efficiency of the proposed CNNMPC-SCA algorithm, a comparative study was conducted using the Adaptive Fuzzy PID controller and the hybrid Fuzzy PID controller. The obtained results demonstrate that the proposed control algorithm achieves better control performances compared to those obtained using the other considered controllers.

Keywords:

quadrotor control, nonlinear model predictive control, metaheuristic, NARX, sine cosine algorithm

Citation data from Crossref and Scopus

Published Online

2025-07-01

How to Cite

Benrabah, M., Fas, M. L., Stiti, C., Kara, K. “Constrained Neural Network-based Model Predictive Control for Quadrotors Using the Sine Cosine Algorithm”, Periodica Polytechnica Electrical Engineering and Computer Science, 2025. https://doi.org/10.3311/PPee.40289

Issue

Section

Articles