We propose a distributed control strategy to allow the control of a multi-agent system requiring k-hop interactions based on the design of distributed state and input observers. In particular, we design for each agent a finite time convergent state and input observer that exploits only the communication with the 1-hop neighbors to reconstruct the information regarding those agents at a 2-hop or more distance. We then demonstrate that if the k-hop based control strategy is set-Input to State Stable with respect to the set describing the goal, then the observer information can be adopted to achieve the team objective with stability guarantees.

This article considers the problem of controller synthesis of a fragment of signal temporal logic (STL) specifications for large-scale multiagent systems, where the agents are dynamically coupled and subject to collaborative tasks. A compositional framework based on continuous-time assume-guarantee contracts (AGCs) is developed to break the complex and large synthesis problem into subproblems of manageable sizes. We first show how to formulate the collaborative STL tasks as AGCs by leveraging the idea of prescribed performance control. The concept of contracts is used to establish our compositionality result, which allows us to guarantee the satisfaction of a global contract by the multiagent system when all agents satisfy their local contracts. Then, a closed-form continuous-time feedback controller is designed to enforce local contracts over the agents in a distributed manner, which further guarantees global task satisfaction based on the compositionality result. The effectiveness of our results is demonstrated by two numerical examples.

We propose a k-hop Distributed Prescribed Performance Observer (k-hop DPPO) for state estimation in multi-agent systems. The observer allows each agent to estimate the state of those agents that are 2-hop or more distant by communicating only with 1-hop neighbors, while guaranteeing that transient estimation errors satisfy prescribed performance defined a priori. Furthermore, we demonstrate that if the controller with perfect state knowledge drives the system towards the goal and the estimation based closed-loop system is set-Input to State Stable (set-ISS) with respect to the set describing the goal, then the state estimates can be adapted to achieve the teams objective. Simulation results are provided to demonstrate the effectiveness of the proposed results.

This paper presents a distributed control framework for leader-follower multi-agent systems with general linear dynamics to achieve consensus alongside prescribed transient performance. The multi-agent system is in a leader-follower configuration with only a set of agents selected as leaders and controlled via external inputs. In particular, we propose a distributed control framework comprising a prescribed performance controller for the leaders, while the followers are governed solely by a consensus protocol. When the decay rate of the performance functions is within a sufficient bound, we show that this distributed control law achieves consensus for the entire multi-agent system, with the guarantee that the trajectories of the consensus errors remain bounded by a time-varying prescribed transient performance function. Finally, the proposed results are illustrated through numerical examples.

This paper presents a distributed control framework for leader-follower multi-agent systems with general linear dynamics to achieve consensus alongside prescribed transient performance. The multi-agent system is in a leader-follower configuration with only a set of agents selected as leaders and controlled via external inputs. In particular, we propose a distributed control framework comprising a prescribed performance controller for the leaders, while the followers are governed solely by a consensus protocol. When the decay rate of the performance functions is within a sufficient bound, we show that this distributed control law achieves consensus for the entire multi-agent system, with the guarantee that the trajectories of the consensus errors remain bounded by a timevarying prescribed transient performance function. Finally, the proposed results are illustrated through numerical examples.

We propose a method to decompose signal temporal logic tasks for multi-agent systems under communication constraints. Specifically, given a task graph representing task dependencies among couples of agents in the system, we propose to decompose tasks assigned to couples of agents not connected in the communication graph by a set of sub-tasks assigned to couples of communicating agents over the communication graph. To this end, we parameterize the predicates' level set of tasks to be decomposed as hyper-rectangles with parametric centres and dimensions. Convex optimization is then leveraged to find optimal parameters maximising the volume of the predicate's level sets. Moreover, a formal treatment of conflicting conjunctions of formulas in the considered STL fragment is introduced, including sufficient conditions to avoid the insurgence of such conflicts in the final decomposition.

We propose a method to decompose signal temporal logic tasks for multi-agent systems under communication constraints. Specifically, given a task graph representing task dependencies among couples of agents in the system, we propose to decompose tasks assigned to couples of agents not connected in the communication graph by a set of sub-tasks assigned to couples of communicating agents over the communication graph. To this end, we parameterize the predicates' level set of tasks to be decomposed as hyper-rectangles with parametric centres and dimensions. Convex optimization is then leveraged to find optimal parameters maximising the volume of the predicate's level sets. Moreover, a formal treatment of conflicting conjunctions of formulas in the considered STL fragment is introduced, including sufficient conditions to avoid the insurgence of such conflicts in the final decomposition.

We introduce a novel distributed sampled-data control method tailored for heterogeneous multi-agent systems under a global spatio-temporal task with acyclic dependencies. Specifically, we consider the global task as a conjunction of independent and collaborative tasks, defined over the absolute and relative states of agent pairs. Task dependencies in this form are then represented by a task graph, which we assume to be acyclic. From the given task graph, we provide an algorithmic approach to define a distributed sampled-data controller prioritizing the fulfilment of collaborative tasks as the primary objective, while fulfilling independent tasks unless they conflict with collaborative ones. Moreover, communication maintenance among collaborating agents is seamlessly enforced within the proposed control framework. A numerical simulation is provided to showcase the potential of our control framework.

We introduce a novel distributed sampled-data control method tailored for heterogeneous multi-agent systems under a global spatio-temporal task with acyclic dependencies. Specifically, we consider the global task as a conjunction of independent and collaborative tasks, defined over the absolute and relative states of agent pairs. Task dependencies in this form are then represented by a task graph, which we assume to be acyclic. From the given task graph, we provide an algorithmic approach to define a distributed sampled-data controller prioritizing the fulfilment of collaborative tasks as the primary objective, while fulfilling independent tasks unless they conflict with collaborative ones. Moreover, communication maintenance among collaborating agents is seamlessly enforced within the proposed control framework. A numerical simulation is provided to showcase the potential of our control framework.

This paper investigates an important class of information-flow security property called opacity for stochastic control systems. Opacity captures whether a system's secret behavior (a subset of the system's behavior that is considered to be critical) can be kept from outside observers. Existing works on opacity for control systems only provide a binary characterization of the system's security level by determining whether the system is opaque or not. In this work, we introduce a quantifiable measure of opacity that considers the likelihood of satisfying opacity for stochastic control systems modeled as general Markov decision processes (gMDPs). We also propose verification methods tailored to the new notions of opacity for finite gMDPs by using value iteration techniques. Then, a new notion called approximate opacity-preserving stochastic simulation relation is proposed, which captures the distance between two systems' behaviors in terms of preserving opacity. Based on this new system relation, we show that one can verify opacity for stochastic control systems using their abstractions (modeled as finite gMDPs). We also discuss how to construct such abstractions for a class of gMDPs under certain stability conditions.