In this paper, we consider the decentralized control design problem for a particular case of networked switched affine systems. The considered network consists of interconnected (identical) linear systems with the controlled input switching among a finite set of values. We address a challenging case where the network can be only locally stabilized. Scalable Lyapunov based conditions are proposed for designing such decentralized controllers. The derived conditions (theoretical and linear matrix inequalities) do not depend on the number of subsystems. Ellipsoidal estimates of the domain of attraction are given.

Conditions for designing a feedback control law to remove the dense set of Unstable Periodic Orbits and chaotic attractors while preserving equilibria are formulated in terms of 2-contractivity of the closed loop system, by means of the second additive compound of the system's Jacobian. Matrix inequalities to allow computing the control gain matrix are discussed. An example of application of the method to the Thomas’ system is presented.

In this paper, we further study the effective computation of stabilizing controllers of multidimensional systems. Within the algebraic analysis approach, the stabilization problem can be characterized by the fact that a certain finitely presented A-module M, naturally associated with the multidimensional system, is projective, where A denotes the ring of multivariate rational functions without poles in the closed complex unit polydisc Dn. This condition can be reduced to the existence of an element s of a polynomial ideal I which has no zero in Dn. According to the Polydisc Nullstellensatz, the latter condition is equivalent to the fact that no complex zero of the elements of I belongs to Dn. If this condition is satisfied, using cyclic resultants and linear programming, we then propose a method to compute such a polynomial s. Finally, using computer algebra methods for effectively handling basic operations on R[s-1]-modules, where R is a polynomial ring, we show how to compute stabilizing controllers.

Sensor faults may be modelled as discrete random variables jointly distributed with process states and measurements, resulting in a hybrid (continuous and discrete) dynamical system. State estimation of such systems is challenging due to the combinatorial explosions following propagation of the discrete states. For this reason, a new algorithm is proposed which leverages the structure of the particle filter to incorporate discrete states representing sensor health. The discrete states characterize either normal or faulty conditions, with unique faults (e.g., stuck, out-of-range, etc.) as individual states. These become additional states to estimate which are subsequently used for fault detection and diagnosis. The method is evaluated using a simulated continuous stirred tank reactor benchmark and diagnoses sensor faults with an average sensitivity > 90% for two fault types and > 70% for three fault types.

Nowadays organizations are under increasing pressure to adopt sustainable solutions for material handling and management due to growing environmental awareness. This drive has spurred advancements in innovative technologies, optimizing logistic processes to reduce energy usage and emissions, and enhancing overall supply chain sustainability. This approach extends to internal material handling with forklifts and the emerging use of drones for last-mile operations. Despite the importance of sustainable logistic practices, the literature lacks a comprehensive review of research on forklifts and drones in logistic activities. Through a Systematic Literature Network Analysis (SLNA), this paper addresses this gap by conducting a comparative analysis of two bibliometric reviews: one on forklifts’ sustainable logistic applications and the other on drones’. This analysis aims to identify key contributors, countries, journals, and research themes in both areas. The SLNA results seek to highlight existing literature, productive countries, influential authors, and research themes. This paper provides theoretical contributions by identifying gaps in the current literature and highlighting key research themes related to sustainable logistic practices. Furthermore, it offers practical insights to industry stakeholders on integrating these practices using forklifts and drones, while also laying the groundwork for future research through a comparative analysis.

This paper presents a comprehensive modeling and control approach to enable an effective use of multirotor UAVs in both research and educational settings. The main focus lies in the development of a simple yet sufficiently accurate mathematical model capturing the essential features inherent in multirotor UAV dynamics. Special attention is devoted to modeling uncertainties to facilitate the design of robust and adaptive control strategies. A hierarchical control framework is proposed to streamline the design, tuning, and implementation of various control laws, thereby enhancing the ease of use and versatility of the UAV systems. Experimental activities conducted as part of a master-level course on adaptive control at Politecnico di Milano serve to validate the proposed modeling and control strategies. These activities offer students hands-on experience in implementing and fine-tuning control algorithms on real multirotor UAV platforms, thereby bridging the gap between theoretical concepts and practical applications.

Multi-omics integration is a novel approach that combines data from different biomolecular levels, such as genes, proteins, and metabolites, to obtain a comprehensive view of the biological system. This approach is even more relevant for studying plants, as plant metabolism includes complex and interconnected mechanisms responsible for environmental adaptation. Plant metabolism has also been studied using Genome-Scale Metabolic Models (GSMMs), which generate in silico phenotype predictions that can be integrated with experimental omics to improve data interpretability. In this work, transcriptomics and metabolomics data of grapes, and flux distributions predicted by GSMMs of V. vinifera were integrated using DIABLO to identify the relationships between genes, metabolites and reactions during grape development and potential biomarkers for each developmental stage. Malate, sucrose and glucose showed the highest correlations with genes and reactions and a high impact on the model's classifications of grape stage. The most contributing genes and reactions were more active in the green stage. This approach represents the first effort to integrate multi-omics and metabolic models for studying plant metabolism.

Industrial companies face challenges caused by volatile business environments. In particular, changing customer demands can have large impacts on their performance. Medium-term capacity management addresses this challenge by adapting the production capacity to the customer’s demand. This is usually done by experts or, in the best cases, supported by production planning software using linear programming or genetic algorithms.There is a growing trend for using production simulations or digital twins. These simulations can be coupled with demand forecasts and machine learning to create a planning assistant tool. This tool can support the operations manager to fnd the right set of measures to adapt the production capacity based on changing customer demand.In our work, we design such a planning assistant tool by using different algorithms, including deep reinforcement learning. We apply it in a learning factory for performance comparisons and can show promising results of deep reinforcement learning. Furthermore, we can show that this simple decision support system outperforms humans in the test setting.