Graphene oxide (GO) Langmuir-Blodgett (LB) thin films were prepared to use as chemical sensor element. The properties of GO LB thin film layers were characterized via Quartz crystal microbalance (QCM) method. Selectivity of the sensor to various organic vapors was investigated by surface plasmon resonance (SPR) and QCM techniques. Fick’s Equations were used for calculating the diffusion coefficients ( D ) values of organic vapors. It was observed that chemical gas sensing characterictic of the GO film sensor shows a large response to some hazardous organic vapors. Then, by using experimental measurements that obtained during this process, NARX-ANN based mathematical model of frequency shift of the quartz resonator was designed. The results of NARX-ANN verifies the efficiency of the designed model.

In this study, we focus on real-time implementations of a practical distributed adaptive formation control scheme for a multi-quadrotor system with uncertain inertial system parameters. We design a decentralized controller based on the leader-follower formation approach to motion control of such a system in rigid formation. The proposed control approach has a two-level structure: At the high level, a distributed control scheme is designed for the kinematic formation control problem. In the low-level, we analyze each single quadrotor control design in three parts. The first is an adaptive linear quadratic controller under consideration of inertial uncertainties for the pitch and roll dynamics, and in this case, we design an on-line parameter estimation with the least squares approach, excepting yaw and altitude dynamics. The second is proportional controller for the yaw motion. The third is proportional-integral-derivative controller for altitude. Throughout the formation control implementations, the controllers successfully satisfy the formation maintenance objective. Simulations and experimental results are presented considering various scenarios, demonstrating the effectiveness of our algorithm.

Recently, the electrification and automation of heavy-duty trucks has gained significant attention from both industry and academia, driven by new legislation introduced by the European Union. During a typical drive cycle, the mass of an urban service truck can vary substantially as waste is collected, yet most existing studies rely on a single controller with fixed gains. This limits the ability to adapt to mass changes and results in suboptimal energy usage. Within the framework of the EU-funded OBELICS and ESCALATE projects, this study proposes a novel control strategy for a semi-autonomous refuse truck. The approach combines a particle swarm optimization algorithm to determine optimal controller gains and a multiple model controller to adapt these gains dynamically based on real-time vehicle mass. The main objectives of the proposed method are to (i) optimize controller parameters, (ii) reduce overall energy consumption, and (iii) minimize speed tracking error. A cost function addressing these objectives is formulated for both autonomous and manual driving modes. The strategy is evaluated using a real-world drive cycle from Eskişehir City, Turkiye. Simulation results show that the proposed MMC-based method improves vehicle performance by $5.19\%$ in autonomous mode and $0.534\%$ in manual mode compared to traditional fixed-gain approaches.

In this study, a novel multiple model adaptive control scheme is designed and implemented for quadrotor unmanned aerial vehicles (UAVs). The proposed scheme involves a mixing adaptive controller that blends a set of pre-designed linear quadratic controllers. A particular goal of the design is guaranteeing robustness in lateral motion against modeling uncertainties. The designed controller scheme is tested via real-time experiments on Quanser Qball-X4 UAVs. The experimental results verify the efficiency of the proposed scheme.