Mohamed Rabik Mohamed Ismail

The increasing popularity of electric vehicles is inevitable due to their lower dependence on conventional fuel and reduced air pollution. Among various drivetrain architectures, four-wheel independently driven electric vehicles (4WID-EVs) have gained significant attention owing to their superior load-carrying and dynamic performance. However, the distributed four-motor architecture makes them vulnerable to unpredictable motor failures, necessitating an effective fault-tolerant control strategy. This work proposes a common sliding mode controller (CSMC)-integrated quantum complete graph neural network (QCGNN) for adaptive tuning under one-, two-, and three-motor failure conditions at reference speeds of 20 and 40 m/s, ensuring stable operation through continuous state feedback. Simulation results demonstrate fault recovery within 3 s, a rise time of 1.2–1.3 s, a settling time below 5.5 s, a peak overshoot below 8%, and a steady-state error below 0.2%. Compared with the QCGNN-Optimal LQR, the proposed QCGNN-CSMC reduces the Mean Absolute Error (MAE) from 34 to 18, Root Mean Square Error (RMSE) from 41 to 23, and the steady-state error from 1.07 to 0.56, while maintaining R² values above 0.90. Rapid controller prototyping further validates the robustness, reliability, and real-time applicability of the proposed fault-tolerant control framework for 4WID-EVs.

Semiconductor laser diode machining has emerged as a disruptive solution improving precision and efficiency in leather cutting making it an indispensable technology in industries such as automotive and aerospace. This study investigates the improvement of Normalized Performance Index (NPI) through the application of Extremum Seeking Control (ESC) to optimize the laser diode cutting process focusing on minimizing carbonization and kerf width while maximizing material removal rate (MRR) to improve the overall process efficiency. As this technology requires less power compared to conventional cutting methods and also reduce the overall carbon footprint promotes sustainability and supports ecofriendly manufacturing practices. The classic control techniques such as Linear-Quadratic Regulator (LQR) controllers and Proportional-Integral-Derivative (PID) controllers find it difficult to adjust to real-time process fluctuations. With adaptive real-time capabilities of ESC this approach offers a possible substitute by dynamically modifying parameters such as Pulse Width Modulation (PWM) and Stand-off Distance (SOD) to preserve the ideal cutting conditions. The impact of ESC on the Material Removal Rate (MRR), kerfwidth and carbonization of leather analysed in the proposed study. The results indicate that ESC considerably enhances the adaptability and consistency of the cutting process reducing carbonization and maintaining constant quality cut across varying conditions. These findings contribute to the advancement of semiconductor laser diode assisted leather cutting and proposes a unique technique for incorporating ESC into manufacturing processes for improved performance contributing to more sustainable and efficient leather cutting practices.

Owing to the recent attention towards the growing issue of global warming, the automotive industry is shifting towards more capable and eco-friendly vehicles with longer ranges than conventional vehicles. Although the transition to eco-friendly vehicles faces several challenges, including component failures due to mechanical wear, electrical voltage fluctuations, motor damage from overloads, infrastructure, and external environmental disturbances. The four-wheel independent drive electric vehicle (4WID-EV) is often used as an alternative to the single-drive electric vehicle, providing improved traction control and reducing the increased load on the individual motors. This study proposes an optimally enhanced controller to control the linear and nonlinear trajectories of four independent motors to evaluate the electric vehicle’s speed and address challenges involved in torque distribution to the independent drive, especially under various motor failure conditions. The computed results reveal that the proposed optimal linear quadratic regulator (LQR) controller accurately predicts better than the conventional proportional integral derivative (PID) controller in terms of the vehicle’s speed under various motor failures. Specifically, the optimal LQR controller achieves a faster settling time of 2.5 s, a lower overshoot of 0.8%, a mean error of 0.0441 rad/s, and a mean squared error (MSE) of 0.0820 (rad/s²). These results indicate that the proposed controller enhances stability and accuracy, improving adaptability even under motor failure conditions in 4WID-EVs.

The rapid proliferation of vehicles globally presents significant challenges to road transportation efficiency and safety, including accidents, emissions, energy utilization, and road management. Autonomous vehicle platooning emerges as a promising solution within intelligent transportation systems, offering benefits like reduced fuel consumption and emissions, and optimized road use. However, implementing autonomous vehicle platooning faces obstacles such as stability under disturbances, safety protocols, communication networks, and precise control. This paper proposes a novel control strategy coordinated Kalman observer–Linear Quadratic Regulator (CKO-LQR) to ensure platoon formation stability in the presence of disturbances. The disturbances considered include vehicle movements, sensor noise, and communication delays, with the leading vehicle’s movement serving as the commanding signal. The proposed controller maintains a constant inter-gap distance between vehicles despite the disturbances utilizing a coordinated Kalman observer to estimate preceding vehicle movements. A comparative analysis with conventional PID controllers demonstrates superior performance in terms of faster settling times and robustness against disturbances. This research contributes to enhancing the efficiency and safety of autonomous vehicle platooning systems.

Enhancing road capacity, safety, and energy efficiency is a potential outcome of vehicle platooning. Since platooning involves driving close to each other, it is essential to have minimal stopping distance (SD) during emergency braking. However, the anti-lock braking system (ABS) in a vehicle and unknown road type would further increase the SD. For this, a novel spacing policy using extremum-seeking control (ESC) estimated ABS has been proposed in this paper. An optimal slip ratio of that particular road type can be tracked and found using ESC estimation to maintain the maximum friction in ABS for all road conditions to have a minimal SD. The primary objective is to minimize the inter-gap distance while incorporating the ABS features. The simulation and experimentation of ABS for the set of non-linear vehicles on different road conditions have been carried out and numerical results have been compared with conventional ABS systems. The results show that the ABS with ESC estimation reduces the SD by seeking the optimal slip ratio and a new spacing policy for the platoon has been expressed using regression analysis. The entire simulated scenario has been implemented in hardware to validate the proposed model as a quarter-car model.

Electric discharge machining (EDM) is one of the non-conventional machining processes that supports machining for high-strength and wear-resistant materials. It is a challenging task to select the process parameters in real-time to maximize the material removal rate since real-time process trials are expensive and the EDM process is stochastic. For the ease of finding process parameters, a modelling of the EDM process is proposed. Due to the non-linear relationship between the material removal rate (MRR) and discharge time, a model-free adaptive extremum-seeking controller (ESC) is proposed in the feedback path of the EDM process for finding an optimal value of the discharge time at which the maximum material removal rate can be achieved. The results of the model show a performance that is closer to the actual process by choosing steel workpieces and copper electrodes. The proposed model offers a lower error rate when compared with actual experimental process data. When compared to manual searching for an optimal point, extreme seeking online searching performed better as per the experimental results. It was observed that the experimental validation also proved that the ESC can produce a large MRR by tracking the extremum control. The present study has been limited to only the MRR, but it is also possible to implement such algorithms for more than one response parameter optimization in future studies. In such cases the performance measures of the process could be further enhanced, which could be used for a real-time complex die- and mold-making process using EDM.

Electric vehicles are often regarded as the greatest future substitute for conventional automobiles. A photovoltaic (PV) panel is used to create non-conventional solar energy for an electric vehicle, which can be utilized as a backup power source as well as to charge the battery. Tracking maximum power in solar panels is a difficult task for PV panels nowadays. An extremum seeking control method is used in this paper to track the Maximum Power Point by continuously varying sinusoidal signal in a PV panel to increase efficiency and finding the exact point where the maximum power is obtained as compared with conventional maximum power point tracking methods and algorithms. The Buck/Boost converter converts the power needed for a motor from the solar panel. The Buck/Boost converter is set up in such a way that variable speed controls can be achieved in a motor and also controlled by an ESC controller for ensuring the maximum power from the electrical vehicle solar panel. The State of charge and battery management system are key factors to be considered in the development of an electrical vehicle along with proposed system. A prototype model with a solar panel on top can be created to validate the proposed concept. To demonstrate a complete model, the batteries, controller, and motors will be mantled into a prototype car. The proposed technique involves adding an additional loop to the conventional ESC algorithm in order to improve the speed of convergence and results are compared with conventional power tracking methods.