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All Journal Journal of Robotics and Control (JRC) International Journal of Robotics and Control Systems
Ayoub, Abdurahman Basil
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Journal : International Journal of Robotics and Control Systems

 1 
Nonlinear Model Predictive Control of a Magnetic Levitation System Using Artificial Protozoa Optimizer Noaman, Mohanad N.; Ayoub, Abdurahman Basil; Mahmood, Saif S.
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1668

Abstract

A magnetic levitation system (Maglev) is a sensitive, multi-parameter, nonlinear, and unstable system that is utilized to levitate a ferromagnetic object in free space. Due to its vast applications, various research studies in the field of control strategy have become extremely important and challenging. This work proposes the design of a nonlinear model predictive (NMPC) control scheme for the object position control against the nonlinearities and uncertainties of a Maglev system. A novel bio-inspired Artificial Protozoa Optimization (APO) algorithm is used to fine-tune the NMPC parameters, which include best weighting matrices ( ), shorter prediction horizons ( ), and shorter time steps ( ) to minimize the objective cost function. The effective performance of the NMPC is verified using simulation-based results in MATLAB. The CasADi toolbox is utilized to solve nonlinear optimization problems and handle the nonlinearity of the Maglev system model. Simulations are implemented for three trajectories tracking (step, sine, and square) with 20% and without Maglev parameters perturbations. To prove the superiority of the proposed controller, comparisons are made with the conventional Linear Quadratic Regulator (LQR) and proportional-integral-derivative (PID) controllers. Two performance indices are introduced, Integral of Squared Error (ISE) and Integral of Absolute Error (IAE), to examine the tracking performances of the NMPC, LQR, and PID controller.  The NMPC controller has shown more efficient performance and accurate results than other controllers. The contributions of this work include a new optimization technique of APO, a new engineering application of the APO integrated with NMPC to control a Maglev system, consideration of inherent nonlinearities and system constraints, and robustness improvement under perturbation.
NMPC Based-Trajectory Tracking and Obstacle Avoidance for Mobile Robots Qasim, Mohammed Salim; Ayoub, Abdurahman Basil; Abdulla, Abdulla Ibrahim
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1605

Abstract

This paper presents the design of a Nonlinear Model Predictive Controller (NMPC) for a wheeled Omnidirectional Mobile Robot (OMR) in order to track a desired trajectory in the presence of previously unknown static and dynamic obstacles in the environment around the robot. A laser rangefinder sensor is used to detect the obstacles where each obstacle occupies numerous points of every sensor reading. The points that belong to each obstacle are then clustered together using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. This research introduces a novel approach to represent obstacles as multiple rotated ellipses, enabling a more accurate representation of complex obstacle shapes without overestimating their boundaries, thereby allowing the robot to navigate through narrow passages. CoppeliaSim robotic simulator is utilized to create the virtual simulation environment as well as simulate the OMR dynamics. MATLAB with the help of the CasADi toolbox is used for the process of the laser rangefinder readings and the implementation of NMPC, respectively.  To validate the effectiveness and robustness of the proposed approach, three simulation scenarios are conducted, each involving distinct trajectories and varying densities of static and/or dynamic obstacles. The proposed control architecture exhibits remarkable performance, enabling the OMR to effectively navigate through narrow passages and avoid multiple static and dynamic obstacles while closely adhering to the desired trajectory.
Co-Authors Abdulla, Abdulla Ibrahim Mahmood, Saif S. Noaman, Mohanad N. Qasim, Mohammed Qasim, Mohammed Salim