This paper investigates the problem of information control in networked multi-user systems, where agents such as robots, sensors, and software entities interact via a communication network to achieve individual or shared goals. Information control involves deciding which state estimates to share or broadcast, balancing cooperation among friends and privacy from adversaries. Since each user has only partial knowledge of the system, efficient protocols for sharing relevant data to balance privacy, security, and transparency is needed. This study models multi-user systems as discrete-event systems where agents need to distinguish certain state pairs in order to perform their tasks. We systematically study and solve critical problems to address the key aspects of information control: determining the necessity of shared information, minimizing communication for security, and maximizing public information release when required. A framework that addresses private communications, public broadcasting, and adversarial dynamics, offering strategies to meet both security and transparency requirements is introduced. Solutions and algorithms are proposed to solve these problems.

This article presents a novel graph neural network (GNN)-based surrogate model (GNN-SM) for predicting state evolution in reconfigurable battery packs. By leveraging graph-based representations of battery cell interconnections, the proposed approach addresses the unique challenge of estimating the imbalance in state-of-charge (SOC) and temperature of cells of a battery pack in dynamic battery configurations. Unlike conventional methods that focus on instantaneous state estimation, ourGNN-SMpredicts future SOC and temperature distributions by considering both current system state and switch configuration. The model architecture combines graph attention networks with pooling operations to effectively capture cell-to-cell interactions and battery pack-level dynamics. Numerical results under constant current and constant power discharge conditions demonstrate thatGNN-SMsignificantly outperforms baseline feedforward neural network (FNN) and FNN-attention models, achieving up to 73.7% reduction in root-mean-square error for temperature imbalance prediction and 46% reduction for SOC imbalance prediction. Furthermore, the model provides a 1629-fold speedup over high fidelity physics-based simulator while maintaining mean absolute percentage errors below 2% for temperature and 8% for SOC predictions. The scalability ofGNN-SMis further validated on a 100-cell reconfigurable battery pack, where the proposed approach achieves high accuracy despite being trained on an extremely small fraction of all possible configurations. Finally, robustness analysis under sensor noise conditions demonstrates thatGNN-SMmaintains reliable predictions even under high noise levels.

Electrical vehicles (EV) have been considered to be an effective way to combat global climate change. To extend the driving range of EV, this paper studies the active battery cell balancing control based on linear parametric varying model predictive control (MPC). Specifically, an equivalent circuit model is used to predict cell terminal voltage, and three different MPC-based battery cell balancing control strategies are proposed to dynamically transport electricity from cell to cell to reduce the imbalance. In particular, for the first control strategy, MPC is set up to be a tracking controller with the primary control objective of forcing all cells’ terminal voltage to follow the same trajectory generated by a nominal cell model; for the second control strategy, MPC maximizes the lowest cell voltage, so that the battery operating range can be extended; for the third and last strategy, MPC minimizes the maximum variation among cell terminal voltages. To assess the effectiveness of the proposed battery cell balancing control strategies, simulations are performed on all three MPC formulations, using both steady-state and transient conditions. Numerical results show that the proposed battery cell balancing control can achieve a driving range extension of 9% for dynamic driving cycle and 7% for steady-state condition, based on our simulation setup. Compared to the existing work, our approaches do not require the over-restrictive assumption that the trip duration is known in advance, while at the same time achieve similar driving range extension. Furthermore, it is also shown that different driving condition favors different cell balancing control strategy, indicating a need for a hybrid approach. Finally, real time implementability is demonstrated via throughput analysis.Note to Practitioners—Improving the efficiency of electric vehicles is of paramount importance to combat the global climate challenge. This paper contributes by proposing effective cell level balancing control methodologies to extend the driving range of electric vehicles to improve their energy efficiency and public acceptance. The control methods, which are based on model predictive control, are analytically derived with details for embedded implementation. Simulation results demonstrate the effectiveness of the proposed methodologies, with future work to investigate the applicability of nonlinear model predictive control with large number of cells.

Reducing traffic accidents and associated casualties is a growing concern for modern human society. The secondary or even chain collisions for an unstable vehicle after an initial impact can result in more hazards and fatalities. Passive safety systems such as airbags and seat belts only provide limited level of protection for vehicle occupants, but cannot prevent collision accidents, while active safety systems usually work before the initial collision. Therefore, it is of great significance to develop dedicated post-impact stability control systems to help vehicles quickly restore stability to mitigate and/or avoid secondary collisions. However, the loss of original nonholonomic constraint property and the nonlinearity and saturation of tire forces due to post-impact sideslip, over-spinning, and drifting motions pose great challenges in controller design. Moreover, how to simulate and analyze the collision process and to further construct a simulation environment is the primary problem to solve for enabling controller development. Also, exploring repeatable, effective and low-cost experiment methods lays the foundation for controller verification. This paper aims to provide an overview of the latest technological advancements in collision modeling, control synthesis, and experimental procedures for post-impact stability control. The advantages and disadvantages of different modeling, control and experimental approaches are compared in succession. Finally, the paper discusses the challenges encountered in existing research and the prospects for post-impact active safety control systems.

Data-driven predictive control of connected and automated vehicles (CAVs) has received increasing attention as it can achieve safe and optimal control without relying on explicit dynamical models. However, employing the data-driven strategy involves the collection and sharing of privacy-sensitive vehicle information, which is vulnerable to privacy leakage and might further lead to malicious activities. In this paper, we develop a privacy-preserving data-enabled predictive control scheme for CAVs in a mixed traffic environment, where human-driven vehicles (HDVs) and CAVs coexist. We tackle external eavesdroppers and honest-but-curious central unit eavesdroppers who wiretap the communication channel of the mixed traffic system and intend to infer the CAVs’ state and input information. An affine masking-based privacy protection method is designed to conceal the true state and input signals, and an extended form of the data-enabled predictive leading cruise control under different data matrix structures is derived to achieve privacy-preserving optimal control for CAVs. Numerical simulations demonstrate that the proposed scheme can protect the privacy of CAVs against attackers without affecting control performance or incurring heavy computations.

This paper provides revisions to the algorithms presented by Chen et al., 2013 for testing diagnosability of stochastic discrete-event systems. Additional new contributions include PSPACE-hardness of verifying strong stochastic diagnosability (referred as A-Diagnosability in Thorsley et al., 2005) and a necessary and sufficient condition for testing stochastic diagnosability (referred as AA-Diagnosability in Thorsley et al., 2005) that involves a new notion of probabilistic equivalence.

While cryptography is used to protect the content of information (e.g., a message) by making it undecipherable, behaviors (as opposed to information) may not be encrypted and may only be protected by partially or fully hiding through creation of ambiguity (by providing covers that generate indistinguishable observations from secrets). Having a cover together with partial observability does cause ambiguity about the system behaviors desired to be kept secret, yet some information about secrets may still be leaked due to statistical difference between the occurrence probabilities of the secrets and their covers. In this paper, we propose a Jensen-Shannon divergence (JSD)-based measure to quantify secrecy loss in systems modeled as partially observed stochastic discrete event systems, which quantifies the statistical difference between two distributions, one over the observations generated by secret and the other over those generated by cover. We further show that the proposed JSD measure for secrecy loss is equivalent to the mutual information between the distributions over possible observations and that over possible system status (secret versus cover). Since an adversary is likely to discriminate more if he/she observes for a longer period, our goal is to evaluate the worst case loss of secrecy as obtained in the limit over longer and longer observations. Computation for the proposed measure is also presented. Illustrative examples, including the one with side-channel attack, are provided to demonstrate the proposed computation approach.

This paper studies the fault detection of discrete-time stochastic systems with linear-time temporal logic (LTL) as correctness requirement-A fault is a violation of LTL specification. The temporal logic allows system correctness properties to be specified compactly and in a user-friendly manner (being close to natural-languages), and supports automatic translation into other formal models such as automata. We introduce the notion of input-output stochastic hybrid automaton (I/O-SHA) and show that the refinement of a continuous physical system (modeled as stochastic difference equations) against a certain class of LTL correctness requirement can be modeled as an I/O-SHA. The refinement preserves the behaviors of the physical system and also captures requirement-violation as a reachability property. Probability distribution over the discrete locations of hybrid system is estimated recursively by computing the distributions for continuous variables for each discrete location. This is then used to compute the likelihood of fault, a statistic that we employ for the purpose of fault detection. The performance of the detection scheme is measured in terms of false alarm (FA) and missed detection (MD) rates, and the condition for the existence of a detector to achieve any desired rates of FA and MD is captured in form of Stochastic-Diagnosability, a notion that we introduce in this paper for stochastic hybrid systems. The proposed method of fault detection is illustrated by a practical example.

Two types of diagnosability of stochastic discrete-event systems (DESs) were introduced by Thorsley in 2005, where a necessary and sufficient condition for Strong Stochastic (SS)-Diagnosability (referred as A-diagnosability by Thorsley and Teneketzis, 2005), and a sufficient condition for Stochastic (S)-Diagnosability (referred as AA-diagnosability by Thorsley and Teneketzis, 2005), both with exponential complexity, were reported. In this paper, we present polynomial complexity tests for checking: (i) necessity and sufficiency of SS-Diagnosability; (ii) sufficiency of S-Diagnosability; and (iii) sufficiency as well as necessity of S-Diagnosability; the latter requires an additional notion of probabilistic equivalence. Thus, the work presented improves the accuracy as well as the complexity of verifying stochastic diagnosability.

This brief is focused on the parameter estimation problem of a second-order adaptive quadratic neuronal model. First, it is shown that the model discontinuities at the spiking instants can be recast as an impulse train driving the system dynamics. Through manipulation of the system dynamics, the membrane voltage can be obtained as a realizable model that is linear in the unknown parameters. This linearly parameterized realizable model is then utilized inside a prediction error-based framework to design a dynamic estimator that allows for rapid estimation of model parameters under a persistently exciting input current injection. Simulation results show the feasibility of this approach to predict multiple neuronal firing patterns. Results using both synthetic data (obtained from a detailed ion-channel-based model) and experimental data (obtained from in vitro embryonic rat motoneurons) suggest directions for further work.