Prof. Raafat Shalaby

Program Director of Master of Science in Mechatronics Engineering (MECT)

Faculty Office Ext.

1740

Faculty Building

UB2

Office Number

S25

Bio

Biography

Dr. Raafat Shalaby is a Full Professor of Intelligent Control Systems at the Mechanical Engineering Program, Nile University. With over 20 years in academia, he has held leadership roles, including Director of the Mechatronics Master’s Program at Nille University (2022–present) and former Director of the Mechanical Engineering Program at Nile University (2020–2022).

He earned his Ph.D. (Dr.-Ing.) in Industrial Electronics and Control Engineering from the Faculty of Electrical Engineering at the Technical University of Berlin (TU-Berlin), Germany, in 2011, following a B.Sc. and M.Sc. in Automatic Control from Menofia University.

Prof. Shalaby has a rich teaching portfolio, having taught undergraduate and graduate courses across major Egyptian institutions. His teaching philosophy centers on inspiring students to exceed their limits through discipline and perseverance.

His interdisciplinary research spans intelligent control systems, fractional-order modeling, meta-heuristic optimization, and machine- and reinforcement learning—particularly for mechatronic and biomedical applications. He has authored over 45 peer-reviewed publications and currently supervises numerous M.Sc. and Ph.D. theses.

Recent Publications

The just-in-time concept, mass customization, omnichannel distribution, and the rising global population have all fueled the logistics sector. Consequently, using automation inside the warehouses to make them more dynamic and sustainable for the future is one of the crucial components to adapt to this quick shift. Giants in the industry and technology are becoming more interested in the “smart

As the technological advancements in felid such as electronic, robotics, and artificial intelligence continue to grow and flourish, the more the traditional methods of doing things starts to get absolute. This phenomenon cannot be more clearly observed in like medicine, technological advancements changed the ways things are done from the way that the doctors preform their surgical operations to

This paper presents a comprehensive study on the optimization of PID controller parameters for a ball & plate system through the utilization of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The objective of the study is to attain precise steady-state response while shortening settling time and minimizing overshoot. The assessment of controller performance is conducted using the

This paper presents the implementation of a Fractional Order PID (FOPID) controller for regulating the angular speed of a Brushless DC (BLDC) sensorless motor. The controller is optimized using the Flower Pollination Algorithm and Genetic Algorithm (GA) to determine the optimal values for the fractional order system parameters and PID parameters of the controller. A comparison between the results

In this paper, an online optimization approach of a fractional-order PID controller based on a fractional-order actor-critic algorithm (FOPID-FOAC) is proposed. The proposed FOPID-FOAC scheme exploits the advantages of the FOPID controller and FOAC approaches to improve the performance of nonlinear systems. The proposed FOAC is built by developing a FO-based learning approach for the actor-critic

A fault detection algorithm is proposed in this paper for wind farms. The algorithm is based on machine learning adopting Support Vector Machines (SVM). The focus is on the detection of grid faults for a Wind Farm of Doubly Fed Induction Generator (WF-DFIG). A high-fidelity model is used to simulate the system performance in faulty and healthy conditions. Statistical features based on steady-state

Holonomic robots are advantageous in several applications. The work presented in this paper performs the simulation of a 3 omni wheel mobile robot using MATLAB and V-REP. The simulated robot contains proximity sensors and cameras for obstacle detection and uses the Artificial Potential Field algorithm for path planning. The robot can map the surrounding environment while deviates and categorizes

This paper seeks to improve the efficiency of photovoltaic (PV) water pumping system using Fractional-order Fuzzy Maximum Power Point Tracking (FoF-MPPT) control and Gray Wolf Optimization (GWO) technique. The fractional calculus has been used to provide an enhanced model of PV water pumping system to, accurately, describe its nonlinear characteristics. Moreover, three metaheuristic optimizers are

the proposed paper aims to design and model an air levitation system, which is a highly nonlinear system because of its fast dynamics and low damping. The system is trained using a Nonlinear Autoregressive model with exogenous input (NARX model). An enhanced height measurement system, modified setup, and several training techniques have been used to overcome the restrictions that the non-linearity

Grants

Projects

Research Project

Developing Robot Assisted Therapy for Upper Limb Using Virtual Reality and Machine Learning

The proposed device aims to optimize the physical rehabilitation experience for patients with upper limb dysfunctionality via the usage of virtual reality games and customized therapy programs. The system will be provided with a variety of gaming scenarios for each patient and the movement will be determined based on the patient’s condition. Objective/Contributions: The proposed research aims to

Research Project

Motion Planning and Control for a Multi-Purpose AI Based Autonomous Robot

Numerous algorithms have been developed over the last few years to create real-time path-planning systems for autonomous robots. There are three things or activities that must be followed or carried out by an autonomous robotic system to enable the execution of the task of robot navigation. These activities are mapping and modeling the environment, path planning and driving systems. The selection

Research Project

Academic System Resource Planning: A Fully-Automated Smart Campus/ASRP

Objectives: The project aims at creating a smart automated academic and administrative university environment infiltrating the global perspective of Education Quality and best practices for University Management and Academic System Resource Planning within the HE system in EG through the design and development of a smart digital platform for monitoring, analysis and closed-loop feedback control of

Research Project

Optimization of Solar Tree Performance in Egypt: A Simulation-Based Investigation

Abstract This project explores the optimization of solar tree performance in Egypt through the orientation and positioning of solar panels. Solar energy is a crucial and abundant resource in Egypt, and the paper proposes the use of solar trees as a promising solution to harness this energy efficiently. The design process involves two main aspects: optimizing the orientation of solar panels and

Research Project

Optimal Fractional-Order PID Controller based on Fractional-Order Actor-Critic Algorithm

Abstract This project proposes an online optimization approach for a fractional-order PID controller using a fractional-order actor-critic algorithm (FOPID-FOAC), aiming to enhance the performance of nonlinear systems. The FOPID-FOAC scheme combines the advantages of fractional-order PID controllers and actor-critic reinforcement learning algorithms. The proposed FOAC algorithm employs fractional

Research Project

Fractional-order Fuzzy Sliding Mode Control of Uncertain Nonlinear MIMO Systems Using Fractional-order Reinforcement Learning

Abstract This project presents a novel approach to enhance the control performance of unknown multiple-input and multiple-output (MIMO) nonlinear systems. The proposed method integrates a fractional-order fuzzy sliding mode controller with online fractional-order reinforcement learning (FOFSMC-FRL). The controller utilizes two Takagi–Sugeno–Kang (TSK) fuzzy neural network actors to approximate the

Research Project

Observer-Based Adaptive Event-Triggered Fractional-Order Sliding Mode Control Using Online Fractional-Order Learning Approach

Abstract This project introduces an innovative adaptive event-triggered control strategy (ETS) for networked uncertain nonlinear systems with unmeasured states. The proposed method, called ETFFSMC-FAC, combines a fractional-order fuzzy sliding mode controller with a fractional-order actor-critic (FAC) approach. Initially, unmeasured states are estimated using a sigma-point Kalman filter (SKF)