This paper considers the safety-critical control problem for nonlinear second-order multi-agent systems with constraints of each agent and inter-agent ones. We overcome the challenge of the time-varying and position-dependent communication network with limited sensing range by introducing a truncated function for the smooth addition and deletion of links in the edge set, and design a distributed and locally Lipschitz-continuous safety-critical control law, composed of a nominal controller for the objectives such as consensus, formation, and position swapping, etc., and a safety controller, which only takes effect when some neighboring agent enters the custom-designed boundary set. Meanwhile, to rigorously verify the safety of the whole multi-agent system, a continuously differentiable control barrier function is proposed under a relaxed feasibility condition in the sense that it is imposed on each subsystem and only needed in the boundary area.

This paper explores the cubic-regularized Newton method within a federated learning framework while addressing two major concerns: privacy leakage and communication bottlenecks. We propose the Differentially Private Federated Cubic Regularized Newton (DP-FCRN) algorithm, which leverages second-order techniques to achieve lower iteration complexity than first-order methods. We incorporate noise perturbation during local computations to ensure privacy. Furthermore, we employ sparsification in uplink transmission, which not only reduces the communication costs but also amplifies the privacy guarantee. Specifically, this approach reduces the necessary noise intensity without compromising privacy protection. We analyze the convergence properties of our algorithm and establish the privacy guarantee. Finally, we validate the effectiveness of the proposed algorithm through experiments on a benchmark dataset.

A differential dynamic programming (DDP)-based framework for inverse reinforcement learning (IRL) is introduced to recover the parameters in the cost function, system dynamics, and constraints from demonstrations. Different from existing work, where DDP was usually used for the inner forward problem, our proposed framework uses it to efficiently compute the gradient required in the outer inverse problem with equality and inequality constraints. The equivalence between the proposed and existing methods based on Pontryagin’s Maximum Principle (PMP) is established. More importantly, using this DDPbased IRL with an open-loop loss function, a closed-loop IRL framework is presented. In this framework, a loss function is proposed to capture the closed-loop nature of demonstrations. It is shown to be better than the commonly used open-loop loss function. We show that the closed-loop IRL framework reduces to a constrained inverse optimal control problem under certain assumptions. Under these assumptions and a rank condition, it is proven that the learning parameters can be recovered from the demonstration data. The proposed framework is extensively evaluated through four numerical robot examples and one realworld quadrotor system. The experiments validate the theoretical results and illustrate the practical relevance of the approach.

Global food systems are at the center of some of the most pressing modern societal challenges: They are significant contributors to a range of systemic issues, including health problems and chronic diseases, greenhouse gas emissions and general environmental degradation, and increasing financial burdens on healthcare and economies. Within these complex systems, final sustainable consumption, which refers to the adoption of diets that are both healthy and environmentally friendly, plays a critical role. Significant changes in contemporary dietary patterns are essential to address the rising burden of chronic diseases and public health outcomes and the escalating climate crisis. Achieving these shifts requires coordinated action from policymakers, consumers, and the scientific community in an effort to support the development, implementation, and evaluation of advertising and policy instruments that promote healthier and more sustainable dietary choices. However, driving changes in dietary behavior is a complex challenge, shaped by the interplay of heterogeneous influences, including biological, social, cultural, environmental, political, and economic factors, and further complicated by the difficulty of validating proposed approaches in ways that are both efficient and ethically sound. This vision paper presents the problem of promoting healthy and environmentally friendly diets and their implications for environmental sustainability. In particular, it discusses a systems approach based on social network dynamics and social interventions, illustrating recent findings that demonstrate the potential of influence strategies to drive dietary change. Finally, key scientific challenges and emerging research opportunities are highlighted.

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease.

Temporal logics have garnered significant attention in the control community due to their use for formal control synthesis, namely for the synthesis of control policies with provable correctness guarantees for more complex and interesting properties than traditional control objectives. Formal control under temporal logics is fundamentally challenging though, particularly when dealing with uncertain infinite systems and complex temporal logic specifications for real-time task planing, as the established methods struggle with handling models in high dimensions and with accommodating online deployment. In this article, we propose Temporal Logic Trees (TLT) as a mitigation for these challenges. TLT are constructed from Linear Temporal Logic (LTL) formulae via reachability analysis, offering an abstraction-free design method. Building upon the TLT framework, we present approaches for adaptive task planning and formal control synthesis that are usable on both finite and infinite systems. Furthermore, we demonstrate the applicability of our approach for online control synthesis, particularly in addressing time-varying tasks: namely, our method allows for dynamic online updates of the specifications, which showcases its practical utility and flexibility.

In grid-connected inverter systems, frequency-domain passivity theory is increasingly employed to analyze grid-inverter interactions and guide inverter control designs. However, due to difficulties in meeting sufficient passivity-based stability conditions at low frequencies, passivity theory often falls short of achieving stable system specifications. This article introduces an extended frequency-domain passivity theory. By incorporating a weighting matrix, an extended stability condition is derived. Compared to conventional passivity-based stability conditions, the proposed theory significantly reduces conservativeness and is more suited for analyzing grid-inverter interactions and guiding inverter control design. Theoretical analyses, numerical examples, and experimental results are provided to validate the effectiveness of the proposed methods.

This paper considers convex games involving multiple agents that aim to minimize their own cost functions using locally available information. A common assumption in the study of such games is that the agents are symmetric, meaning that they have access to the same type of information. Here we lift this assumption, which is often violated in practice, and instead consider asymmetric agents; specifically, we assume some agents have access to first-order gradient information and others have access to the zeroth-order oracles (cost function evaluations). We propose an asymmetric learning algorithm that combines the agent information mechanisms. We analyze the regret and Nash equilibrium convergence of this algorithm for convex and strongly monotone games, respectively. Specifically, we show that our algorithm always performs between pure first- and zeroth-order methods, and can match the performance of these two extremes by adjusting the number of agents with access to zeroth-order oracles. Therefore, our algorithm incorporates the pure first- and zeroth-order methods as special cases. We provide numerical experiments on a market problem for both deterministic and risk-averse games to demonstrate the performance of the proposed algorithm.

Autonomous Industrial Cyber-Physical Systems (ICPS) represent a future vision where industrial systems achieve full autonomy, integrating physical processes seamlessly with communication, computing and control technologies while holistically embedding intelligence. Cloud-Fog Automation is a new digitalized industrial automation reference architecture that has been recently proposed. This architecture is a fundamental paradigm shift from the traditional International Society of Automation (ISA)-95 model to accelerate the convergence and synergy of communication, computing, and control towards a fully autonomous ICPS. With the deployment of new wireless technologies to enable almost-deterministic ultra-reliable low-latency communications, a joint design of optimal control and computing has become increasingly important in modern ICPS. It is also imperative that system-wide cyber-physical security are critically enforced. Despite recent advancements in the field, there are still significant research gaps and open technical challenges. Therefore, a deliberate rethink in co-designing and synergizing communications, computing, and control (which we term “3C co-design”) is required. In this paper, we position Cloud-Fog Automation with 3C co-design as the new paradigm to realize the vision of autonomous ICPS. We articulate the state-of-the-art and future directions in the field, and specifically discuss how goal-oriented communication, virtualization-empowered computing, and Quality of Service (QoS)-aware control can drive Cloud-Fog Automation towards a fully autonomous ICPS, while accounting for system-wide cyber-physical security.

A resilient state estimation scheme for uniformly observable nonlinear systems, based on a method for local identification of sensor attacks, is presented. The estimation problem is combinatorial in nature, and so many methods require substantial computational and storage resources as the number of sensors increases. To reduce the complexity, the proposed method performs the attack identification with local subsets of the measurements, not with the set of all measurements. A condition for nonlinear attack identification is introduced as a relaxed version of existing redundant observability condition. It is shown that an attack identification can be performed even when the entire state cannot be recovered from the measurements. As a result, although a portion of measurements are compromised, they can be locally identified and excluded from the state estimation, and thus, the true state can be recovered. Simulation results demonstrate the effectiveness of the proposed scheme.