Cancer development is an evolutionary process that mirrors the evolution of biological species. While species evolve through mutations and natural selection acting on individuals within a population, cancer evolves through mutations and selection acting on cells within a tissue. This process is further complicated by factors such as tumor cell heterogeneity, cellular interactions, and the selective pressure exerted by the tumor microenvironment. Understanding these mechanisms and the evolutionary dynamics of cancer cells is crucial to gaining deeper insights into the disease. This paper first presents a refined eco-evolutionary model of cancer, with key parameters grounded in experimental data, explicitly incorporating the aforementioned aspects. Building upon this, we propose a framework to design and compare evolutionarily informed therapeutic control strategies derived from multi-objective optimization. These strategies address the multifaceted nature of cancer treatment, seeking to simultaneously minimize tumor size and the development of therapeutic resistance, while maximizing the patient’s quality of life. Simulations validate the model and show the efficacy of these evolution-aware therapies in managing tumor progression while mitigating treatment-related side effects.

In many applications, security is a serious issue due to the high risk of cyber-attacks. An adversary can cause severe damage by providing wrong information about the system and consequently leading the controller to perform incorrectly. Detecting any malicious activity is necessary to cover up its negative effects and make the system operate reliably. In this paper, we propose a new approach to analyze security and diagnosability of a Finite-state machine (FSM) under multiple attacks. Different kinds of attacks are modeled by an FSM and the composition of the nominal and attack model can express all the effects of possible attacks on the given system. We define different concepts of security and give conditions under which detectability of the attacks is possible. Moreover, diagnosability of an FSM affected by multiple attacks is addressed, and the special case of critical observability under attack is characterized. Note to Practitioners—Nowadays, cyber-physical systems (CPSs) are being widely used in industry and the extensive use of communication networks by CPSs raises the concern of vulnerability to malicious attacks. Therefore, it is a major challenge to detect the attack specifically when multiple attacks might launch on different sensors or communication channels. These facts motivate us to investigate the attack detectability properties of a system modeled by Finite State Machines (FSMs). To this end, we consider one of the well-known types of cyber-attacks which can inject, replace or remove output information in the communication network, called the man-in-the-middle attack. This may also be modeled by an FSM. In this paper, we provide a good understanding of the security level of a system under this powerful kind of attack, which can provide better insight into the weaknesses and strengths of the system before designing a supervisor. In some applications e.g. in air traffic control, the designer needs to detect if a state belongs to a “critical set”, i.e. a set of dangerous or unsafe states. As a second important contribution of the paper, we investigate under which conditions this is possible even if the system is under attack. An extension to the more general property of diagnosability is illustrated.

Interconnected and autonomous vehicles are proven to be helpful in reducing traffic congestion and dangerous emissions while enhancing safety on our roads. In this context, the present paper introduces a human-inspired Adaptive Cruise Control dedicated to improving the passenger experience using Model Predictive Control and traffic macroscopic information. To better describe the characteristics of human drivers, first a human-inspired microscopic hybrid automaton is considered, and an optimization problem targeting consumption minimization and collision avoidance is designed with a receding horizon approach. Then, traffic macroscopic information is included in the controller definition so that a mesoscopic Adaptive Cruise Control model is obtained. Simulations showing the efficacy of the proposed approaches for safety and eco-driving are provided.

The paper investigates the possibility of using macroscopic information to improve control performance of a vehicular platoon composed of autonomous vehicles. A mesoscopic traffic modeling framework is proposed, and closed loop String Stability is achieved by exploiting Input-to-State Stability (ISS) for the analysis of the platoon. Examples of mesoscopic control laws are illustrated and simulations show the effectiveness of our approach in ensuring String Stability and improving the platoon behavior.

In this work, we present a decentralized scheme for the control of a freeway traffic system over a non-ideal trans- mission channel, involving network delays ultimately resulting in possible packet losses in the digital communication. The feed- back scheme exploits the well-known Cell Transmission Model (CTM) and a ramp metering control, with actuation consisting of on-ramp traffic lights. In this context, the Model Predictive Control (MPC) approach is applied locally, in a decentralized way, which is preferable from the computational viewpoint with respect to centralized solutions, the complexity of which would grow rapidly with the dimension of the system at hand, making real-time traffic control potentially ineffective. The simulation setup is based on a traffic model whose parameters are identified using data produced by a commercial microscopic simulator of the traffic system. Preliminary performance results show the effectiveness of the approach taken, also in the case of parameter uncertainties, and look promising in view of future investigations.

New connection constraints for the power network (grid codes) require more flexible and reliable systems, with robust solutions to cope with uncertainties and intermittence from renewable energy sources (renewables), such as photovoltaic (PV) arrays. The interconnection of such renewables with storage systems through a direct current (DC) MicroGrid can fulfill these requirements. A “Plug and Play” approach based on the “System of Systems” philosophy using distributed control methodologies is developed in this paper. This approach allows to interconnect a number of elements to a DC MicroGrid as power sources, such as PV arrays, storage systems in different time scales, such as batteries and supercapacitors, and loads, such as electric vehicles and the main ac grid. The proposed scheme can easily be scalable to a much larger number of elements.

New connection constraints for the power network (Grid Codes) require more flexible and reliable systems, with robust solutions to cope with uncertainties and intermittence from renewable energy sources (renewables), such as photovoltaic arrays. A solution for interconnecting such renewables to the main grid is to use storage systems and a Direct Current (DC) MicroGrid. A “Plug and Play” approach based on the “System of Systems” philosophy using distributed control methodologies is developed in the present work. This approach allows to interconnect a number of elements to a DC MicroGrid as power sources like photovoltaic arrays, storage systems in different time scales like batteries and supercapacitors, and loads like electric vehicles and the main AC grid. The proposed scheme can easily be scalable to a much larger number of elements.

Networked control systems (NCS) are spatially distributed systems where communication among plants, sensors, actuators and controllers occurs in a shared communication network. NCS have been studied for the last ten years and important research results have been obtained. These results are in the area of stability and stabilizability. However, while important, these results must be complemented in different areas to be able to design effective NCS. In this paper we approach the control design of NCS using symbolic (finite) models. Symbolic models are abstract descriptions of continuous systems where one symbol corresponds to an "aggregate" of continuous states. We consider a fairly general multiple-loop network architecture where plants communicate with digital controllers through a shared, non-ideal, communication network characterized by variable sampling and transmission intervals, variable communication delays, quantization errors, packet losses and limited bandwidth. We first derive a procedure to obtain symbolic models that are proven to approximate NCS in the sense of alternating approximate bisimulation. We then use these symbolic models to design symbolic controllers that realize specifications expressed in terms of automata on infinite strings. An example is provided where we address the control design of a pair of nonlinear control systems sharing a common communication network. The closed--loop NCS obtained is validated through the OMNeT++ network simulation framework.

The WirelessHART standard uses TDMA and channel hopping to control access to the network and to coordinate communication between network devices, in order to enhance reliability and to improve the throughput of the network. A problem in utilizing multiple channels is that current devices are usually equipped with a single transceiver. Thus, a node can only transmit or receive on one channel at a time. Moreover, contrary to today's wired control systems, if a single access point is used the communication becomes the bottle neck of the control system. Therefore this paper presents how one may schedule the WirelessHART communication using two access points. Furthermore the paper describes a scheduling algorithm managing a multihop multi-channel networked control system based on the WirelessHART standard. A simulation example of a multihop multi-channel network is also shown, using the fixed packet lost utility of the Matlab/Simulink-based tool TrueTime.

The design of engine control systems has been traditionally carried out using a mix of heuristic techniques validated by simulation and prototyping using approximate average-value models. However, the ever increasing demands on passengers' comfort, safety, emissions, and fuel consumption imposed by car manufacturers and regulations call for more robust techniques and the use of cycle-accurate models. We argue that these models must be hybrid because of the combination of time-domain and event-based behaviors. We present a hybrid model of the engine in which both continuous and discrete time-domain as well as event-based phenomena are modeled in a separate but integrated manner. Based on this model, we formalize the specification of the overall engine control by defining a number of hybrid control problems. To cope with the difficulties arising in the design of hybrid controllers, a design methodology is proposed. This methodology consists of a relaxation of the hybrid problem by simplifying some of its components to obtain a solvable problem,and then deriving a solution to the original control problem by appropriately modifying the control law so obtained to take into consideration the original specifications and models. The effectiveness of this approach is illustrated on three challenging problems: fast force-transient control, cutoff control, and idle speed control.

Conformational analysis is the problem of nding all minimal energy three-dimensional con gurations of molecules.Cyclic structures are of particular interest.An ecient algorithm based on a purely geometric approach that generates feasible con gurations very eciently is presented thus making full conformational analysis possible even for fairly large cyclic structures.

A complete statistical model for italian vowels is presented. The classification method contains the adaptive procedure which makes possible the evolution of the regions characteristic of each vowel in the F1-F2 plane and the effectiveness of which has been verified in a previous work [1]. In this paper, each part of the algorithm is described in its theoretical detail and justified on the basis of an extended experimentation. Results are given proving the accuracy of the classifier for vowel segments independent of the context and prosody and dependent upon the context and prosody.

Lip-reading is widely used by profoundly deaf individuals for the reception of the spoken language. This is a very difficult task because the labial image is ambiguous, The final goal of this work is to provide the deaf person with additional information (or "keys") which disambiguate the labial image. Phoneme recognition in continuous speech is used to produce the keys. To allow complete freedom in running speech, and in order to provide keys synchronously with speech production, no lexical, syntactical or semantical informations are used. Algorithms are adapted to a given speaker through a learning phase where prototypes are built for the phonetic units to be recognized. Recognition algorithms are a combination of segmentation and centisecond labeling. The keys system is optimized taking into account the confusions made by the recognition programs. Recognition scores for multiple speakers are indicated both at the phonetic level and at the keys level.