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In this letter a distributed model predictive control scheme (dMPC) is proposed for a multi-agent team that is subject to a set of time-constrained spatial tasks encoded in Signal Temporal Logic (STL). Here, the agents are subject to both individual and collaborative STL tasks. In order to ensure the satisfaction of the collaborative tasks while avoiding the computational burden of a centralized problem, we propose a sequential dMPC scheme and show the recursive feasibility property of the framework given appropriately designed terminal ingredients. The resulting MPC problems are solved in discrete-time yet continuous-time satisfaction of the STL tasks is ensured with appropriate tightening of the constraint sets., QC 20240319

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots’ as well as the objects’ specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework., QC 20240215

This paper studies the distributed formation control problem of second-order multi-agent systems (MASs) with limited communication ranges and collision avoidance constraints. A novel connectivity preservation and collision-free distributed control algorithm is proposed by combining prescribed performance control (PPC) and exponential zeroing control barrier Lyapunov functions (EZCBFs). In particular, we impose the time-varying performance constraints on the relative position and velocity errors between the neighboring agents, and then a PPC-based formation control algorithm is developed such that the connectivity of the communication graph can be preserved at all times, and the prescribed transient and steady performance on the relative position and velocity error can be achieved. Subsequently, by introducing the EZCBFs method, an inequality constraint condition on the control input is derived to guarantee the collision-free formation motion. By regarding the PPC-based formation controller as a nominal input, an actual formation control input is given by solving the quadratic programming (QP) problem such that each agent achieves collision-free formation motion while guaranteeing the connectivity and prescribed performance as much as possible. Finally, numerical simulation is carried out to validate the effectiveness of the proposed algorithm., QC 20231201

In this paper, we analyze the system behavior for general nonlinear control-affine systems when a control barrier function-induced quadratic program-based controller is employed for feedback. In particular, we characterize the existence and locations of possible equilibrium points of the closedloop system and also provide analytical results on how design parameters affect them. Based on this analysis, a simple modification on the existing quadratic program-based controller is provided, which, without any assumptions other than those taken in the original program, inherits the safety set forward invariance property, and further guarantees the complete elimination of undesired equilibrium points in the interior of the safety set as well as one type of boundary equilibrium points, and local asymptotic stability of the origin. Numerical examples are given alongside the theoretical discussions., QC 20231205

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 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., QC 20240920

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 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., QC 20240920

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework.

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., QC 20240920

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.

In this work, we present a coordination strategy tailored for scenarios involving multiple agents and tasks. We devise a range of tasks using signal temporal logic (STL), each earmarked for specific agents. These tasks are then imposed through control barrier function (CBF) constraints to ensure completion. To extend existing methodologies, our framework adeptly manages interactions among multiple agents. This extension is facilitated by leveraging nonlinear model predictive control (NMPC) to compute trajectories that avoid collisions. An integral aspect of our approach is the integration of a human-in-the-loop (HIL) model. This model enables real-time integration of human directives into the coordination process. A novel task allocation protocol is embedded within the frame-work to guide this process. We substantiate our methodology through a series of experiments, which corroborate the viability and relevance of our algorithms., Part of ISBN [9798350384574]QC 20240919

Signal Temporal Logic (STL) offers an expressive formalism for describing complex high-level tasks in dynamical systems. This paper introduces a time-varying Control Barrier Function (CBF) for control-affine nonlinear stochastic systems to fulfill STL specifications. The CBFs are used in a robust optimization problem to provide a lower bound on the satisfaction probability of a given STL specification with a predetermined robustness level. We present an online control synthesis approach to minimize a performance function while having the required satisfaction guarantee. We finally provide a tractable solution to the robust optimization for STL with linear and quadratic predicate functions. To illustrate the effectiveness of the method, we apply it to a simple linear case study and to the path-planning problem for a nonlinear wheeled mobile robot., Part of ISBN [9783907144107]QC 20240830

In this work we consider the problem of control under Signal Temporal Logic specifications (STL) that depend on relative position information among neighboring agents. In particular, we consider STL tasks for given pairs of agents whose satisfaction is translated into a set of setpoint output tracking problems with transient and steady-state constraints. Contrary to existing work the proposed framework does not require initial satisfaction of the funnel constraints but can ensure their satisfaction within a pre-specified finite time. Given a tree topology in which agents sharing a STL task form an edge, we show that the resulting control laws ensure the satisfaction of the STL task as well as boundedness of all closed loop signals using only local information., Part of ISBN [9783907144107]QC 20240830

In this paper, we present a novel 3D formation control scheme for directed graphs in a leader-follower multi-agent setup,, achieving (almost) global convergence to the desired shape. Specifically, we introduce three controlled variables representing bispherical coordinates that uniquely describe the formation in 3D. Acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) are used to model the inter-agent sensing topology, while the agents' dynamics are governed by single-integrator model. Our analysis demonstrates that the proposed decentralized formation controller ensures (almost) global asymptotic stability while avoiding potential shape ambiguities in the final formation. Furthermore, the control laws are implementable in arbitrarily oriented local coordinate frames of follower agents using only low-cost onboard vision sensors, making it suitable for practical applications. Finally, we validate our formation control approach by a simulation study., QC 20240830; Part of ISBN [9783907144107]

We develop an algorithm to control an underactuated unmanned surface vehicle (USV) using kinodynamic motion planning with funnel control (KDF). KDF has two key components: motion planning used to generate trajectories with respect to kinodynamic constraints, and funnel control, also referred to as prescribed performance control (PPC), which enables trajectory tracking in the presence of uncertain dynamics and disturbances. We extend PPC to address the challenges posed by underactuation and control input saturation present on the USV. The proposed scheme guarantees stability under user-defined prescribed performance functions where model parameters and exogenous disturbances are unknown. Furthermore, we present an optimization problem to obtain smooth, collision-free trajectories while respecting kinodynamic constraints. We deploy the algorithm on a USV and verify its efficiency in real-world open-water experiments., QC 20240906

We consider the synthesis problem of a multi-agent system under signal temporal logic (STL) specifications representing bounded-time tasks that need to be satisfied recurrently over an infinite horizon. Motivated by the limited approaches to handling recurring STL systematically, we tackle the infinite-horizon control problem with a receding horizon scheme equipped with additional STL constraints that introduce minimal complexity and a backward-reachability-based terminal condition that is straightforward to construct and ensures recursive feasibility. Subsequently, we decompose the global receding horizon optimization problem into agent-level programs the objectives of which are to minimize local cost functions subject to local and joint STL constraints. We propose a scheduling policy that allows individual agents to sequentially optimize their control actions while maintaining recursive feasibility. This results in a distributed strategy that can operate online as a model predictive controller. Last, we illustrate the effectiveness of our method via a multi-agent system example assigned a surveillance task., Part of ISBN 9783907144107QC 20240827

A geometric generalization of contraction theory called k-contraction was recently developed using k-compound matrices. In this article, we focus on the relations between k-contraction and two other generalized contraction frameworks: partial contraction (also known as virtual contraction) and horizontal contraction. We show that in general these three notions of contraction are different. We here provide new sufficient conditions guaranteeing that partial contraction implies horizontal contraction, and that horizontal contraction implies k-contraction. We use the Andronov-Hopf oscillator to demonstrate some of the theoretical results., QC 20240502

In the control of vehicular platoons, the disturbances acting on one vehicle can propagate and affect other vehicles. If the disturbances do not amplify along the vehicular string, then it is called string stable. However, it is usually difficult to achieve string stability with a distributed control setting, especially when a constant spacing policy is considered. This note considers the string unstable cases and studies disturbance propagation in a nonlinear vehicular platoon consisting of n+1 vehicles where the (virtual) leading vehicle provides the reference for a constant spacing policy. Apart from the communications between consecutive vehicles, we also assume that each vehicle can receive information from r neighbors ahead, that is, the vehicular platoon has communication range r. Inspired by existing control protocols, a unified distributed nonlinear control law with a variable r is proposed to facilitate the analysis. For the maximal overshoot of the inter-vehicular spacing errors, we explicitly show that the effect of disturbances, including the external disturbances acting on each vehicle and the acceleration of the leading vehicle, is scaled by O([Formula presented]) for a fixed n. This implies that disturbance propagation can be reduced by increasing communication range. Numerical simulation is provided to illustrate the main results., QC 20240424

A current trend in research on multi -agent control systems is to consider high-level task specifications that go beyond traditional control objectives and take into account the heterogeneity of each agent in the system, i.e., the different capabilities of the agents in terms of actuation, sensing, communication and computation. This article provides an overview of our work on the problem of control of heterogeneous multi -agent systems under both spatial and temporal constraints as well as our perspective on the challenges and open problems associated with the consideration of such spatiotemporal constraints. Initially, we review a set of control strategies introduced by the authors addressing the satisfaction of cooperative tasks such as formation control as well as individual objectives such as reference tracking. The satisfaction of those objectives is ensured using prescribed performance control. Building upon these approaches we then review recent results on control under high-level spatiotemporal objectives expressed in Signal Temporal Logic, a formal language that allows to express complex spatial tasks that must be satisfied within pre -defined deadlines. Theoretical results considering multi -agent systems with various capabilities under spatiotemporal constraints are presented., QC 20240527

We derive necessary and sufficient conditions for leader–follower multi-agent systems such that we can further apply prescribed performance control to achieve the desired formation while satisfying certain transient constraints. A leader–follower framework is considered in the sense that a group of agents with external inputs are selected as leaders in order to drive the group of followers in a way that the entire system can achieve target formation within certain prescribed performance transient bounds. We first derive necessary conditions on the leader–follower graph topology under which the target formation together with the prescribed performance guarantees can be fulfilled. Afterwards, the derived necessary conditions are extended to necessary and sufficient conditions for leader–follower formation control under transient constraints. Finally, the proposed results are illustrated with simulation examples., QC 20240617

Signal temporal logic (STL) formulas have been widely used as a formal language to express complex robotic specifications, thanks to their rich expressiveness and explicit time semantics. Existing approaches for STL control synthesis suffer from limited scalability with respect to the task complexity and lack of robustness against the uncertainty, for example, external disturbances. In this paper, we study the online control synthesis problem for uncertain discrete-time systems subject to STL specifications. Different from existing techniques, we propose an approach based on STL, reachability analysis, and temporal logic trees. First, based on a real-time version of STL semantics, we develop the notion of tube-based temporal logic tree (tTLT) and its recursive (offline) construction algorithm. We show that the tTLT is an under-approximation of the STL formula, in the sense that a trajectory satisfying a tTLT also satisfies the corresponding STL formula. Then, an online control synthesis algorithm is designed using the constructed tTLT. It is shown that when the STL formula is robustly satisfiable and the initial state of the system belongs to the initial root node of the tTLT, it is guaranteed that the trajectory generated by the control synthesis algorithm satisfies the STL formula. We validate the effectiveness of the proposed approach by several simulation examples and further demonstrate its practical usability on a hardware experiment. These results show that our approach is able to handle complex STL formulas with long horizons and ensure the robustness against the disturbances, which is beyond the scope of the state-of-the-art STL control synthesis approaches., QC 20240624

In this work, we propose a novel approach for the continuous-time control synthesis of nonlinear systems under nested signal temporal logic (STL) specifications. While the majority of existing literature focuses on control synthesis for STL specifications without nested temporal operators, addressing nested temporal operators poses a notably more challenging scenario and requires new theoretical advancements. Our approach hinges on the concepts of STL tree (sTLT) and control barrier function (CBF). Specifically, we detail the construction of an sTLT from a given STL formula and a continuous-time dynamical system, the sTLT semantics (i.e., satisfaction condition), and the equivalence or underapproximation relation between sTLT and STL. Leveraging the fact that the satisfaction condition of an sTLT is essentially keeping the state within certain sets during certain time intervals, it provides explicit guidelines for the CBF design. The resulting controller is obtained through the utilization of an online CBF-based program coupled with an event-triggered scheme for online updating the activation time interval of each CBF, with which the correctness of the system behavior can be established by construction. We demonstrate the efficacy of the proposed method for single-integrator and unicycle models under nested STL formulas., QC 20240502

In this article, we consider the periodic event-triggered consensus problem for single-integrator multiagent systems. In existing approaches, consensus is typically achieved by trigger schemes enforcing a monotone decrease of a Lyapunov function. Such trigger schemes may, however, result in more transmissions than are actually required to ensure consensus or meet certain performance specifications. This is because a monotone decrease may be a rather conservative condition for a given Lyapunov function in an event-triggered setting. To overcome the conservativity, we propose a novel window-based trigger scheme, which allows us to leverage existing trigger schemes from the literature to derive less conservative ones. This is achieved by taking the past system behavior into account and allowing a temporary increase of the Lyapunov function as long as a decreasing tendency is still guaranteed. For that, information from previous time steps within certain time windows is considered. We provide an explicit bound on the evolution of the Lyapunov function that is the same for the window-based scheme and the (monotone) original trigger schemes. To illustrate the benefits of the window-based scheme, we validate its efficacy through a comprehensive simulation study and demonstrate that the scheme typically reduces the average update rate of the underlying communication structure in comparison to the original trigger schemes., QC 20240502

In this work we consider the problem of control under Signal Temporal Logic specifications (STL) that depend on relative position information among neighboring agents. In particular, we consider STL tasks for given pairs of agents whose satisfaction is translated into a set of setpoint output tracking problems with transient and steady-state constraints. Contrary to existing work the proposed framework does not require initial satisfaction of the funnel constraints but can ensure their satisfaction within a pre-specified finite time. Given a tree topology in which agents sharing a STL task form an edge, we show that the resulting control laws ensure the satisfaction of the STL task as well as boundedness of all closed loop signals using only local information., QC 20240531

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., QC 20240920

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 develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework.

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework.

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework.

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots' as well as the objects' specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework.

In this paper, we present a novel 3D formation control scheme for directed graphs in a leader-follower configuration, achieving (almost) global convergence to the desired shape. Specifically, we introduce three controlled variables representing bispherical coordinates that uniquely describe the formation in 3D. Acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) are used to model the inter-agent sensing topology, while the agents' dynamics are governed by single-integrator model. Our analysis demonstrates that the proposed decentralized formation controller ensures (almost) global asymptotic stability while avoiding potential shape ambiguities in the final formation. Furthermore, the control laws are implementable in arbitrarily oriented local coordinate frames of follower agents using only low-cost onboard vision sensors, making it suitable for practical applications. Finally, we validate our formation control approach by a simulation study., Comment: Submitted to ECC 2024

In this work, we propose an extension of the previously introduced Corridor Model Predictive Control scheme for high-order and distributed systems, with an application for on-orbit inspection. To this end, we leverage high order control barrier function (HOCBF) constraints as a suitable control approach to maintain each agent in the formation within a safe corridor from its reference trajectory. The recursive feasibility of the designed MPC scheme is tested numerically, while suitable modifications of the classical HOCBF constraint definition are introduced such that safety is guaranteed both in sampled and continuous time. The designed controller is validated through computer simulation in a realistic inspection scenario of the International Space Station., Comment: Paper accepted at Conference on Decision and Control 2023 (CDC)

In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph. Specifically, we propose to decompose tasks defined over multiple agents which require multi-hop communication, by a set of sub-tasks defined over the states of agents with 1-hop distance over the communication graph. To this end, we parameterize the predicates of the tasks to be decomposed as suitable hyper-rectangles. Then, we show that by solving a constrained convex optimization, optimal parameters maximising the volume of the predicate's super-level sets can be computed for the decomposed tasks. In addition, we provide a formal definition of conflicting conjunctions of tasks for the considered STL fragment and a formal procedure to exclude such conjunctions from the solution set of possible decompositions. The proposed approach is demonstrated through simulations., Comment: This paper is accepted at 2024 American Control Conference (ACC)

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge called ``Awareness Inside'', a nonrecurring call for proposals within Horizon Europe designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.

We consider a scenario of cooperative task servicing, with a team of heterogeneous robots with different maximum speeds and communication radii, in charge of keeping the network intermittently connected. We abstract the task locations into a $1D$ cycle graph that is traversed by the communicating robots, and we discuss intermittent communication strategies so that each task location is periodically visited, with a worst--case revisiting time. Robots move forward and backward along the cycle graph, exchanging data with their previous and next neighbors when they meet, and updating their region boundaries. Asymptotically, each robot is in charge of a region of the cycle graph, depending on its capabilities. The method is distributed, and robots only exchange data when they meet.

This paper introduces a novel control framework to address the satisfaction of multiple time-varying output constraints in uncertain high-order MIMO nonlinear control systems. Unlike existing methods, which often assume that the constraints are always decoupled and feasible, our approach can handle coupled time-varying constraints even in the presence of potential infeasibilities. First, it is shown that satisfying multiple constraints essentially boils down to ensuring the positivity of a scalar variable, representing the signed distance from the boundary of the time-varying output-constrained set. To achieve this, a single consolidating constraint is designed that, when satisfied, guarantees convergence to and invariance of the time-varying output-constrained set within a user-defined finite time. Next, a novel robust and low-complexity feedback controller is proposed to ensure the satisfaction of the consolidating constraint. Additionally, we provide a mechanism for online modification of the consolidating constraint to find a least violating solution when the constraints become mutually infeasible for some time. Finally, simulation examples of trajectory and region tracking for a mobile robot validate the proposed approach., Comment: 20 pages, 7 figures, extended version

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., Comment: 16 pages, 6 figures, journal submission

Constraint satisfaction is a critical component in a wide range of engineering applications, including but not limited to safe multi-agent control and economic dispatch in power systems. This study explores violation-free distributed optimization techniques for problems characterized by separable objective functions and coupling constraints. First, we incorporate auxiliary decision variables together with a network-dependent linear mapping to each coupling constraint. For the reformulated problem, we show that the projection of its feasible set onto the space of primal variables is identical to that of the original problem, which is the key to achieving all-time constraint satisfaction. Upon treating the reformulated problem as a min-min optimization problem with respect to auxiliary and primal variables, we demonstrate that the gradients in the outer minimization problem have a locally computable closed-form. Then, two violation-free distributed optimization algorithms are developed and their convergence under reasonable assumptions is analyzed. Finally, the proposed algorithm is applied to implement a control barrier function based controller in a distributed manner, and the results verify its effectiveness., Comment: 13 pages, 6 figures

In this work, we propose a continuous-time distributed optimization algorithm with guaranteed zero coupling constraint violation and apply it to safe distributed control in the presence of multiple control barrier functions (CBF). The optimization problem is defined over a network that collectively minimizes a separable cost function with coupled linear constraints. An equivalent optimization problem with auxiliary decision variables and a decoupling structure is proposed. A sensitivity analysis demonstrates that the subgradient information can be computed using local information. This then leads to a subgradient algorithm for updating the auxiliary variables. A case with sparse coupling constraints is further considered, and it is shown to have better memory and communication efficiency. For the specific case of a CBF-induced time-varying quadratic program (QP), an update law is proposed that achieves finite-time convergence. Numerical results involving a static resource allocation problem and a safe coordination problem for a multi-agent system demonstrate the efficiency and effectiveness of our proposed algorithms.

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and convert the underlying stochastic problem into a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical., Comment: Submitted to CDC24

This paper studies the controller synthesis problem for nonlinear control systems under linear temporal logic (LTL) specifications using zonotope techniques. A local-to-global control strategy is proposed for the desired specification expressed as an LTL formula. First, a novel approach is developed to divide the state space into finite zonotopes and constrained zonotopes, which are called cells and allowed to intersect with the neighbor cells. Second, from the intersection relation, a graph among all cells is generated to verify the realization of the accepting path for the LTL formula. The realization verification determines if there is a need for the control design, and also results in finite local LTL formulas. Third, once the accepting path is realized, a novel abstraction-based method is derived for the controller design. In particular, we only focus on the cells from the realization verification and approximate each cell thanks to properties of zonotopes. Based on local symbolic models and local LTL formulas, an iterative synthesis algorithm is proposed to design all local abstract controllers, whose existence and combination establish the global controller for the LTL formula. Finally, the proposed framework is illustrated via a path planning problem of mobile robots., Comment: 16 pages, 11 figures

This paper presents a finite-time topology identification method for complex dynamical networks. This method prevents the difficulty of verifying linear independence conditions and ensures the success of accurate topology identification. The topology identification scheme first renders the error dynamics between the networks and reference signals zero in finite time, and afterward, the topology is estimated by building an auxiliary network. The identification of topology is achieved once a relaxed excitation condition holds. The excitation condition is guaranteed by the proposed tracking control scheme. A finitetime topology identification and synchronization scheme for complex systems is further proposed where synchronization is realized by removing the exciting signals after the identification of the topology. At last, the simulation results verify the feasibility of the proposed method., Part of ISBN 9798350301243QC 20240222

In this paper, we show that under mild controllability assumptions a time-invariant Control Barrier Function (CBF) can be constructed based on predictions with a finite horizon. As a starting point, we require only a known subset of a control-invariant set where the latter set does not need to be explicitly known. We show that, based on ideas similar to the Hamilton-Jacobi reachability analysis, the knowledge on the subset of a control-invariant set allows us to obtain a time-invariant CBF for the time-invariant dynamics under consideration. We also provide a thorough analysis of the properties of the constructed CBF, we characterize the impact of the prediction horizon, and comment on the practical implementation. In the end, we relate our construction approach to Model Predictive Control (MPC). With a relevant application example, we demonstrate how our method is applied., Part of ISBN 9798350301243QC 20240222

In this work, we propose an extension of the pre-viously introduced Corridor Model Predictive Control scheme for high-order and distributed systems, with an application for on-orbit inspection. To this end, we leverage high order control barrier function (HOCBF) constraints as a suitable control approach to maintain each agent in the formation within a safe corridor from its reference trajectory. The recursive feasibility of the designed MPC scheme is tested numerically, while suitable modifications of the classical HOCBF constraint definition are introduced such that safety is guaranteed both in sampled and continuous time. The designed controller is validated through computer simulation in a realistic inspection scenario of the International Space Station., Part of ISBN: 979-835030124-3QC 20240223

The immune system response to pathogens is organized by a network of cells communicating through expression of a variety of proteins and signaling molecules. A high number of genes are involved in encoding these communicating agents, but the relatively low number of data points is a major challenge in modelling the gene expression response. In this work we propose a feature-selection approach based on gene expression distributions at the single-cell level that improves dynamics identification at the population level. We investigate common approaches to differential expression analysis and show that Earth Mover's Distance (EMD) is a relatively robust measure for gene selection as reflected by the coefficient of variation as well as accuracy of a naive Bayes classifier based on the selected genes. We ultimately propose the bootstrap standard deviation metric as an estimate of state uncertainty and show that statistically significant signals in pathogen response can be recovered in the reduced state space constructed with the selected genes., Part of ISBN 9798350301243QC 20240222

This paper proposes a novel control framework for handling (potentially coupled) multiple time-varying output constraints for uncertain nonlinear systems. First, it is shown that the satisfaction of multiple output constraints boils down to ensuring the positiveness of a scalar variable (the signed distance from the time-varying output-constrained set's boundary). Next, a single funnel constraint is designed properly, whose satisfaction ensures convergence to and invariance of the time-varying output-constrained set. Then a robust and low-complexity funnel-based feedback controller is designed em-ploying the prescribed performance control method. Finally, a simulation example clarifies and verifies the proposed approach., Part of ISBN: 979-8-3503-0124-3QC 20240228

We propose a new method for simultaneous synchronization and topology identification of a complex dynamical network that relies on the edge-agreement framework and on adaptive-control approaches by design of an auxiliary network. Our method guarantees the identification of the unknown topology and it guarantees that once the topology is identified the complex network achieves synchronization. Under our identification algorithm we are able to provide stability results for the estimation errors in the form of uniform semiglobal practical asymptotic stability. Finally, we demonstrate the effectiveness of our approach with an illustrating example., Part of ISBN 9798350301243QC 20240222

This paper addresses the problem of cooperative control of leader-follower multiagent systems under local signal temporal logic (STL) tasks in a distributed fashion, where the overall system is composed of several leader-follower subsystems with coupled dynamics. In this work, only the leaders know the related STL specifications and are designed to drive the followers in a way such that the STL specifications are globally satisfied. Under the local feasibility assumption, we propose a funnel-based control approach for each leader-follower subsystem such that the local STL specifications are achieved, which further implies the global satisfaction of all STL specifications. In order to enforce the satisfaction of the STL formulas, the funnel parameters are appropriately designed to prescribe certain transient behavior that constrains the closed-loop trajectories. The proposed approach is illustrated by a simulation example., QC 20240222Part of ISBN 9781713872344

In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n−dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace., QC 20240209Part of ISBN 9781713872344