Dr Mohamed Amine Ben Abdallah

Recently, the electrification and automation of heavy-duty trucks has gained significant aaention 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.

The trend toward shorter supply chains and home delivery has rapidly increased delivery van traffic. Consequently, in the 20 years prior to 2018, delivery traffic has increased 71%, while passenger vehicles have increased only 13%. This drastic change in traffic patterns presented new challenges to decision makers and fortunately coincided with changes in the automotive industry, i.e., the advent of automation. However, the design of a controller is not straightforward due to the complex and nonlinear vehicle dynamics and the nonlinear relationship between controller, tracking error, and trajectory. This paper proposes a novel hybrid artificial intelligence-based lateral control system for an autonomous delivery van to address these challenges to achieve the lowest RMS value of tracking errors. The strategy consists of multiple simultaneously operating fuzzy controllers whose output signals are optimally weighted by a genetic algorithm to determine the proper allocation of control signals for determining the final steering angle. Six different scenarios are implemented to evaluate the algorithm, and a comparative analysis is performed with two alternative state-of-the-art methods: i) manually weighted and ii) geometrically weighted controllers. During the tests, the vehicle's speed varied, and the roads considered ranged from simple roads to a series of curves. The results show that the proposed strategy leads to a reduction up to 91.2% and 61.1% in tracking error, compared to the manual and geometric weighted alternatives, respectively.