As urban logistics continue to evolve, there is a growing need for innovative delivery solutions that can meet rising demand while addressing sustainability and cost-effectiveness. To tackle these challenges, ecofriendly transportation methods must be adopted to minimize environmental impact, enhance safety and optimize costs. Drone delivery has emerged as a transformative approach that offers speed, adaptability and reduced emissions compared to conventional logistics. However, its large-scale deployment presents technical and operational challenges that require an interdisciplinary approach. This paper examines these challenges through the integration of control engineering and computer science, identifying how their synergies can enhance efficiency and scalability in drone logistics by developing complementary solutions that address various logistical challenges.

This paper presents a comparative study of two parameter identification methods: an algebraic approach based on the direct manipulation of the system’s differential equations and the Recursive Least Squares (RLS) technique. The objective is to evaluate the performance of both methods in terms of estimation accuracy, noise robustness, and computational complexity. The methods are applied to a linear time-invariant system in the programmable logic controller (PLC) environment. Results show that the algebraic approach, although non-recursive, offers fast and robust estimates under moderate noise conditions, while the RLS excels in real-time adaptability in dynamic scenarios. The findings are discussed concerning real-time implementation constraints and potential applications, particularly in industrial systems: Central Air Handling Unit (AHU).

The ability to ensure an efficient inventory management, production schedules and distribution strategies allow companies to meet customer demand while minimizing costs and maximizing profitability. The work presented in this paper focuses on the dynamic inventory management from control theory perspective. The proposed approach employs the so-called "Model-Free Control (MFC)" concept, where the use of any supply chain model becomes useless. Application to a real case of multi-nodes petrochemical supply chain system as well as a comparative studies allow to demonstrate the relevance of the role of this control theory in the frame of supply chain management.

This paper investigates the controller design for a robust and efficient supply strategy for logistic constrained systems with perishable goods subject to uncertainties on the demand and the process delays. Here, instead of model-based control strategies, through a continuous, local identification of the level variations and forecasting via the setting of time series, an adaptive model-free control that does not need any prior knowledge of the supply chain behaviors is developed. The proposed approach takes into accounts not only the decay of goods stored in warehouse (perishing inventories) and prevents from exceeding the facilities capacities but also ensures full demand satisfaction irrespective of the value of delay and reacting significantly faster to the consumer's demand uncertainty. Quantitative analyzes prove a strong reduction of the storage cost, increase of the profit and bullwhip effect attenuation.

In the frame of supply chain networks, several approaches stemming from control theory seem to be more adapted to deal with dynamic inventory management. Nevertheless, the used methods, until today, are model-based control strategies where supply chain models play a major role. Due to the increasing complexity of such systems, the modeling of supply chains becomes more difficult and fails to capture all the dynamic behavior of the supply chain networks. This paper proposes as an alternative to these approaches a model-free control method and its corresponding intelligent controllers for inventory control in supply chain. Several concrete numerical simulations and comparative studies mainly with the internal model control show the efficiency of the approach and promising future of the obtained results even in the presence of various disturbances.