The performance of active suspension systems is directly related to the mechanical design and control of the system. The stable operation of the controller improves driving comfort and handling. The quarter car model is frequently used in the analysis of suspension systems due to its simple structure. In this study, Matlab Simulink software was used in the modeling, control and simulation of the quarter car active suspension model. System performance was investigated for four different road profiles with PID and fuzzy logic control methods. Two of the road profiles used are in the form of impact signals consisting of pits and bumps. The other two road profiles are random road disturbances with high frequency. The effects of active and passive suspension systems on driving comfort are compared by taking into account the control methods used. As a result of the study, it has been determined that the fuzzy logic controller gives better results in pulse signals consisting of bumps and pits, and the PID controller gives better results in high-frequency random road disturbances. In addition, with the use of fuzzy logic control method in the active suspension system, a significant decrease in the actuator force has occurred. This result is very interesting in terms of minimizing energy, reducing actuator sizes and reducing costs.

Autonomous vehicles are one of the increasingly widespread application areas in automotive technology. These vehicles show significant potential in improving transportation systems, with their ability to communicate, coordinate and drive autonomously. These vehicles, which move from source to destination without human intervention, appear to be a solution to various problems caused by people in traffic, such as accidents and traffic jams. Traffic accidents and traffic jams are largely due to driver faults and non-compliance with traffic rules. For this reason, it is predicted that integrating artificial intelligence (AI)-based systems into autonomous vehicles will be a solution to such situations, which are seen as a problem in social life. Looking at the literature, VGGNet, ResNet50, MobileNetV2, NASNetMobile, Feed Forward Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, and Gate Recurrent Units It is seen that deep learning models such as these are widely used in traffic sign classification studies. Unlike previous studies, in this study, a deep learning application was made for the detection of traffic signs and markers using an open-source data set and models of YOLOv5 versions. The original data set was prepared and used in the study. Labeling of this data set in accordance with different AI models has been completed. In the developed CNN models, the training process of the data set containing 15 different traffic sign classes was carried out. The results of these models were systematically compared, and optimum performance values were obtained from the models with hyperparameter changes. Real-time application was made using the YOLOv5s model. As a result, a success rate of 98-99% was achieved.

In this study, the position control simulation of a 3 Degree of Freedom (3DOF) robot arm was compared with machine learning and inverse kinematic analysis separately. The considered robot arm is designed in RRR pattern. In the inverse kinematic analysis of the robot arm, the geometric approach and the analytical approach are used together. Multi-Layer Perceptron (MLP) was used as a machine learning method. Some of the coordinate data that the robot arm can reach in the working space are selected and the MLP model is trained with these data. When training was done with MLP machine learning method, the correlation coefficient (R2) was obtained as 1. Coordinates of 3 different geometric models (helix, star and daisy) that can be included in the working space are used as test data of the MLP model. These tests are simulated in 3D in MATLAB environment. The simulation results were compared with the inverse kinematics analysis data. As a result, Mean Relative Error (MRE) values for helix, star and daisy shapes were calculated as 0.0007, 0.0033 and 0.0011, respectively, in the tests performed. Mean Squared Error (MSE) values were obtained as 0.0034, 0.0065 and 0.0040, respectively. This confirms that the proposed MLP model can operate this system at the desired stability.

We propose a novel systematic approach for the deployment optimization of unmanned aerial vehicles (UAVs). In this context, this study focuses on enhancing the coverage of UAV-mounted 6G mobile base stations. The number and placement optimization of UAV-mounted 6G mobile base stations, deployed to support terrestrial base stations during periods of increased population density in a given area, are addressed using a symbolic limited optimal discrete controller synthesis technique. Within the scope of this study, the UAVs’ altitude and attitude behaviors are optimized to ensure the most efficient trajectory toward the designated base station coordinates. Additionally, at their new locations, these behaviors are adjusted to facilitate accurate coverage estimation from the base stations they serve. In the deployment optimization of UAVs, the placement of base stations is determined using received signal strength data obtained through the ray-tracing-based channel modeling technique. The channel model considered critical parameters such as path loss, received power, weather loss, and foliage loss. Final average path loss values of 102.3 dB, 111.7 dB, and 127.4 dB were obtained at the carrier frequencies of 7 GHz, 26 GHz, and 140 GHz, respectively. These findings were confirmed with MATLAB-based ray tracing simulations. Our proposed approach is validated through experimental evaluations, demonstrating superior performance compared to existing methods reported in the literature.

In this study, the Taguchi optimisation technique was utilised to establish the composition of an asbestos-free brake pad. As a result of optimisation outputs, 18 various compositions were obtained. The produced specimens according to outputs were subjected to friction assessment and screening tests to evaluate their average friction coefficient and friction surface temperature. By assessing the results, the composition of brake pads, which can be used in vehicle brakes, was determined. The experimental results were also examined by variance analysis to demonstrate effect factors. Furthermore, when the test results were evaluated in terms of the friction coefficient, wear rate and temperature of the brake disc, a high coefficient of friction, a moderate wear rate and an acceptable temperature of the brake disc were obtained in sample 13. The composition of sample 13, which has the highest friction coefficient, is 3 g brass powder, 9 g cashew, 9 g carbon fibre, 9 g copper dust and 9 g graphite. Lastly, the average friction coefficients of brake pads were estimated through artificial neural network (ANN) analysis. With respect to the results, it was discovered that the generated ANN model promises a high ratio estimation capacity.

Suspension systems are of great importance in ensuring stable driving in vehicles and the appropriate reaction of vehicle sub-elements against disturbing inputs. Active suspension systems react quickly to road and driving conditions and positively affect vehicle dynamics and passenger comfort. The main factor in improving active suspension systems' performance is determining the suitable control method against the determined disturbing inputs. This study aims to contribute to the development of vehicle suspension technology by investigating the potential of optimizing the performance of active suspension systems with different control algorithms. In the study, a half-car model with an active suspension system was simulated on three different road profiles: bump-pit, sinusoidal, and ISO-8608. Fuzzy logic, PID, Fuzzy-PID, and MPC control methods are used to control the active suspension system, and their advantages over each other and the passive suspension system are investigated. The effectiveness of the control methods determined on each road profile has been analyzed in the evaluations made regarding vehicle dynamics and passenger comfort. As a result of the study, it is observed that the MPC control algorithm was able to control the active suspension system stably and quickly on all three road profiles with a high success rate. The best results are obtained by the MPC algorithm for bump pit, sinusoidal, and ISO8608 road profiles, and the RMSE values for each road profile are 1.466, 0.047, and 0.449, respectively. The suitable control method minimized vehicle body displacement and pitch angle and improved vehicle stability. In the passenger comfort evaluation, 33.49%, 47.79%, and 12.26% improvements were obtained using the MPC control method on bump-pit, sinusoidal, and ISO-8608 road profiles, respectively.