Connectivity Preservation Research Articles

Prescribed performance formation control for second-order multi-agent systems with connectivity and collision constraints

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.

Collision avoidance and connectivity preservation using asymmetric barrier Lyapunov function with time-varying distance-constraints

Collision avoidance and connectivity preservation are two contrasting issues in multi-agent formation control problems. This paper proposes a distributed control design methodology to stabilize a desired formation shape in a multi-agent system while incorporating these two factors simultaneously. Contrary to a simplifying point mass assumption, we consider that agents in the group are characterized by a circular disk-type structure of different radii. Collision avoidance and connectivity preservation in the group are handled by applying time-varying constraints on the inter-center distances among neighboring agents. The concept of asymmetric time-varying barrier Lyapunov function is exploited to derive the stabilizing distributed control law, under a mild assumption on the initial conditions and a feasibility condition relating dimensions of the desired formation with the lower and upper limits of the barrier functions. We also obtain analytical bounds on the various post-design signals and illustrate the results through a simulation example. Finally, the effect of input saturation is discussed, and a comparison with the sensing-region-dependent potential function-based control design approach from the existing literature is provided.

Adaptive Fuzzy Control for Heterogeneous Vehicular Platoon Systems With Collision Avoidance and Connectivity Preservation

Adaptive Fuzzy Control for Heterogeneous Vehicular Platoon Systems With Collision Avoidance and Connectivity Preservation

Reinforcement-learning-based predefined-time relative orbit-attitude control for spacecraft formation flying with connectivity preserving

In this paper, the spacecraft formation control problem is investigated under the undirected tree communication topology subjected to connectivity preserving and collision avoidance constraint. To implement the connectivity preservation and collision prevention of initially connected spacecrafts without employing any potential functions, a novel appointed-time performance function is developed to impose restrictions on the relative orbit distance of the adjacent spacecrafts. Moreover, this function can also characterise the upper boundary of the convergence time and steady-state error explicitly. Then a reinforcement-learning-based relative orbit-attitude control scheme is proposed to propel the formation error to the vicinity of the origin. An actor-critic neural network architecture is utilised to online compensate the system uncertainties, in which the critic neural network is introduced to improve the approximate performance of the actor neural network for the unknown uncertainties by evaluating the consensus performance of the formation system. In addition, an auxiliary system is devised to cope with the input saturation constraint. By using the Lyapunov stability theory, the stability and convergence of the closed-loop system are analysed. Finally numerical simulations are conducted to illustrate the feasibility and effectiveness of the proposed formation control scheme.

Intelligent finite-time formation control for flapping wing micro aerial vehicles

This paper investigates the intelligent finite time formation control for multiple Flapping wing micro aerial vehicles (FWMAVs) system. Firstly, the translational and the rotational attitude motion equations are proposed based on the Lagrangian equation for FWMAVs. The motion system is decouple into an internal and an external dual loop subsystems. An adaptive neural network estimation algorithm is proposed based on the internal and external double loop system of the coupled model to effectively estimate the uncertainties and the external disturbances of the model. In addition, two effective intelligent control protocols are presented for the translational and the rotational attitude motion subsystem, respectively, by utilizing potential energy function, generalized inverse matrix, and finite-time stability. The main contribution of this paper is the case that, four control objectives are achieved for multiple FWMAVs system, including the estimation of uncertainties, collision avoidance, connectivity preservation, and finite time convergence. Finally, a simulation example of formation tracking control is given by using matlab software in the numerical simulation part, and the effectiveness of the obtained results and the superiority of the control protocol are verified.

Collision-Free and Connectivity-Preserving Formation Control of Nonlinear Multi-Agent Systems With External Disturbances

A collision-free and connectivity-preserving formation control issue is addressed for a class of nonlinear multi-agent systems (MASs) with external disturbances. The control objective of this paper is divided into two aspects. One is formation control for collision avoidance and connectivity preservation among a leader and communication-connected followers, and the other is obstacle avoidance between followers and potential obstacles including environmental obstacles and communication-connectionless followers. A tangent-type formation error transformation function is introduced for the first topic. With this transformation, an error transformation equation is developed, and the first topic is transformed into an error stabilization issue. Then, an improved continuous differentiable potential function is constructed for the second topic, and the partial derivative of this improved potential function is smooth and continuous as obstacles enter or exit detection ranges. A predictor-based neural network disturbance observer (PNNDO) is developed for generalized disturbances. Prediction errors are introduced to update NNs' weights, and this replacement avoids phenomenon of high-frequency oscillations with overlarge adaptive gains. With a normalized learning method, the number of NNs' learning parameters is also reduced. With the help of the Lyapunov stability, it is proved that the control objective is achieved. The effectiveness of the control strategy is verified by a group of quadrotors.

Event-driven fully distributed optimal coordinated control for Euler–Lagrange multi-agent systems with connectivity preservation

This paper studies the distributed optimal coordinated control problem for Euler–Lagrange multi-agent systems with connectivity preservation. The aim is to force agents to achieve the optimal solution minimizing the sum of the local objective functions while guaranteeing the connectivity of the communication graph. For practical purposes, the gradient vector of the local objective function is allowed to use only at the real-time generalized position instead of at the auxiliary system state. To make the control parameters independent of the global information and guarantee the fully distributed manner of controller, the adaptive control is introduced to update the coupling weights of the relative states among neighbors. Moreover, to reduce the resource for control updates, the event-driven communication is employed for the updates of both the relative states and the gradient of the connectivity-preserving potential function. Based on the Lyapunov analysis framework, it is proved that agents can converge to the optimal solution with connectivity preservation and Zeno behavior is excluded for the two event-triggering conditions. Finally, the effectiveness of the proposed method is verified by a numerical simulation example.

Robust distributed formation control with prescribed performance for nonlinear multi‐agent systems subjected to compound disturbances

SummaryThis article is concerned with the distributed leader‐following formation control for nonlinear multi‐agent systems (MASs) subject to compound disturbances. Three aspects of the control objective: formation control, collision avoidance and connectivity preservation are achieved by the designed controller composed of three control laws. The formation control law is developed based on the barrier Lyapunov function and the backstepping method, which can prescribe the performance of the close‐loop system to some extent. Within our scheme, an adaptive finite‐time disturbance observer is used to enhance the robustness of the control system against compound disturbances including parameter perturbations, actuator bias faults and external disturbances. Meanwhile, a nonlinear tracking differentiator is introduced for each follower to cancel complex differential calculations and the requirement for the leader's acceleration information. Furthermore, the auxiliary control laws for collision avoidance and connectivity preservation are designed based on the artificial potential field method, which plays an important role during the formation shaping. Finally, complete stability analysis and vivid numerical simulation are carried out.

Connectivity Preservation and Collision Avoidance in Multi-Agent Systems Using Model Predictive Control

This paper presents an innovative predictive control scheme based on a potential field in order to maintain the connectivity of a flock of agents in a leader-follower configuration with dynamic topology. We consider a group of agents navigating in an environment with obstacles towards their target. The followers avoid collisions with each other and obstacles without using any communication links. It is also required to maintain connectivity with the leader, which is unidentified to the followers. The potential field is dynamically updated by introducing time-varying weighted links between the followers to preserve connectivity as we assume only the leader knows the target position. The values of these weights are adjusted continuously according to agents' trajectories by which the critical neighbours of each agent are determined. The superior performance of the proposed predictive controller for navigation of agents to quickly reach their target is demonstrated comparatively by simulation.

Distributed optimal formation tracking control based on reinforcement learning for underactuated AUVs with asymmetric constraints

This paper investigates the distributed optimal formation tracking control problem based on backstepping technique and reinforcement learning for multiple underactuated autonomous underwater vehicles (AUVs). Based on the graph theory, we propose a virtual distributed formation tracking controller in the kinematics model while the barrier Lyapunov function is utilized to make sure the connectivity preservation and the collision avoidance. An optimal controller based on the reinforcement learning(RL) is designed to minimize a cost function in the dynamic motion, and critic–actor neural networks (NNs) are further applied for online implementation of the reinforcement learning algorithm. As a result, the optimal control design for the underactuated AUVs with the uncertain Hydrodynamic can be online realized. The command filter is adopted to solve the issue of the explosion of complexity. The simulation results are given to confirm the validity of the proposed method.

Fixed-time connectivity-preserving consensus of periodically disturbed nonlinear multi-agent systems with limited communication ranges

This paper concentrates on the fixed-time consensus control problem with preserved connectivity for periodically disturbed nonlinear multi-agent systems with limited communication ranges. The dynamics model of the considered multi-agent systems is general, in which the periodically time-varying disturbance appears in the unknown system function in a nonlinear fashion. The Fourier series expansion-radial basis function neural network-based approximator is incorporated to describe the unknown disturbed functions. In consideration of the limited communication ranges, a unified error transformation is utilized to preserve the initial connectivity. Then, via the backstepping method, a consensus control scheme is recursively constructed to guarantee that the consensus errors fall into a small region around the origin in fixed time, and the connectivity preservation is ensured simultaneously. In simulation part, the consensus trajectories, consensus errors and the results of connectivity preservation are provided. The simulation results demonstrate the capability of the proposed control strategy in achieving connectivity-preserving consensus in fixed time.

Adaptive platoon finite-time control for marine surface vehicles with connectivity preservation and collision avoidance

This paper addresses finite-time platoon formation control scheme for vessel train problem subject to modeling uncertainties and external disturbances. The line-of-sight range and angle constraints are proposed to satisfy the collision avoidance and connectivity maintenance. Barrier Lyapunov function is introduced in the kinematic design such that each vehicle follows its leader within the required distance. To compensate for the uncertain model dynamics, the adaptive scheme is incorporated into the decentralized design to estimate nonlinear functions. Furthermore, a disturbance observer is developed to handle time-varying external disturbances. All closed-loop states are guaranteed bounded by Lyapunov theory, and the errors can be regulated to the small neighborhood around zero in finite time. Finally, simulation results are presented to illustrate the effectiveness of the proposed approach with a group of marine surface vessels.

Cost-effective distributed FTFC for uncertain nonholonomic mobile robot fleet with collision avoidance and connectivity preservation

In this paper, the fault-tolerant formation control (FTFC) problem is investigated for a group of uncertain nonholonomic mobile robots with limited communication ranges and unpredicted actuator faults, where the communication between the robots is in a directed one-to-one way. In order to guarantee the connectivity preservation and collision avoidance among the robots, some properly chosen performance functions are incorporated into the controller to per-assign the asymmetrical bounds for relative distance and bearing angle between each pair of adjacent mobile robots. Particularly, the resultant control scheme remains at a cost-effective level because its design does not use any velocity information from neighbors, any prior knowledge of system nonlinearities or any nonlinear approximator to account for them despite the presence of modeling uncertainties, unknown external disturbances, and unexpected actuator faults. Meanwhile, each follower is derived to track the leader with the tracking errors regarding relative distance and bearing angle subject to prescribed transient and steady-state performance guarantees, respectively. Moreover, all the closed-loop signals are ensured to be ultimately uniformly bounded. Finally, a numerical example is simulated to verify the effectiveness of this methodology.

Connectivity-preserving rendezvous of heterogeneous multi-agent systems under denial-of-Service attacks

This article investigates the rendezvous problem of heterogeneous multi-agent systems against Denial-of-Service attacks with preserving initial connectivity under a dynamic communication topology. The algorithm of resisting Denial-of-Service attack is first introduced to connectivity-preserving rendezvous problem of heterogeneous multi-agent systems. First of all, in order to observe the information of the leader, the distributed observers are designed to estimate the state of the leader with the communication network in the presence of Denial-of-Service attack by adaptive algorithm. Then, a switching system model is constructed by taking into account the influence of Denial-of-Service attacks. By means of the obtained model combined with a artificial potential function technique, the proposed distributed control algorithm allows all agents to accomplish rendezvous assignment with connectivity preservation as well as resisting Denial-of-Service attacks. Finally, detailed simulation validates the effectiveness of the proposed distributed observer and controller algorithm.

Connectivity Preservation for Flocking Control of Multi-Agent Systems under Deception Attacks on Velocity⋆

This paper studies the multi-task problem of connectivity preservation and flocking control for multi-agent systems with double-integrator dynamics under deception attacks on velocity. All agents are assumed to be able to measure the relative position of their neighbors and communicate their own velocity with the neighbors. First, a connectivity controller is designed to preserve the local connectivity of the communication graph under deception attacks. Then, a sufficient condition is proposed such that we can design an asymptotically flocking controller for the multi-agent system. Finally, several discussions on the combination of connectivity and flocking tasks to a connectivity-preserving flocking multi-task problem are proposed. Simulation results are provided to support the theoretical analysis.

Connectivity-Preserving Formation Tracking for Multiple Double Integrators by a Self-Tuning Adaptive Distributed Observer

In this letter, we study the distributed formation tracking problem for multiple double-integrator systems with connectivity preservation over a state-dependent communication network. In particular, we employ an adaptive distributed observer for the leader system that can estimate both the state and the system matrix of the leader. As a result, unlike the existing results, we do not require all vehicles to know the system matrix of the leader. Furthermore, the adaptive distributed observer incorporates a self-tuning dynamic observer gain, which eliminates the need of computing the observer gain in advance. The effectiveness of our approach is illustrated by an example.

Deep-Reinforcement-Learning-Based Multitarget Coverage With Connectivity Guaranteed

Deriving a distributed, time-efficient, and connectivity-guaranteed coverage policy in multitarget environment poses huge challenges for a multirobot team with limited coverage and limited communication. In particular, the robot team needs to cover multiple targets while preserving connectivity. In this article, a novel deep-reinforcement-learning-based approach is proposed to take both multitarget coverage and connectivity preservation into account simultaneously, which consists of four parts: a hierarchical observation attention representation, an interaction attention representation, a two-stage policy learning, and a connectivity-guaranteed policy filtering. The hierarchical observation attention representation is designed for each robot to extract the latent features of the relations from its neighboring robots and the targets. To promote the cooperation behavior among the robots, the interaction attention representation is designed for each robot to aggregate information from its neighboring robots. Moreover, to speed up the training process and improve the performance of the learned policy, the two-stage policy learning is presented using two reward functions based on algebraic connectivity and coverage rate. Furthermore, the learned policy is filtered to strictly guarantee the connectivity based on a model of connectivity maintenance. Finally, the effectiveness of the proposed method is validated by numerous simulations. Besides, our method is further deployed to an experimental platform based on quadrotor unmanned aerial vehicles and omnidirectional vehicles. The experiments illustrate the practicability of the proposed method.

Cooperative Target Fencing for a General Target with Connectivity Preservation

In this paper, we study the cooperative target-fencing problem for an n-dimensional target with a general trajectory. The goal is to fence the given target into the convex hull formed by a group of n-dimensional vehicles which are modeled by double-integrator systems. We propose a control law to accomplish this task while avoiding collisions among the vehicles and preserving the connectivity of the communication network. In particular, our approach can handle a target whose position trajectory is any twice continuously differentiable function. Moreover, our result does not rely on the velocity measurements of the vehicles. Thus, it is more practical compared with the existing approaches in the literature. The effectiveness of the proposed method is illustrated by a numerical example.

Adaptive Appointed-Time Consensus Control of Networked Euler–Lagrange Systems With Connectivity Preservation

With consideration of motion control performance and efficient information communication, the synchronization problem on communication connectivity preservation and guaranteed consensus performance for networked mechanical systems has attracted considerable attention in recent years. Different from the existing works, this article investigates a brand-new appointed-time consensus control approach for uncertain networked Euler-Lagrange systems on a directed graph via exploring the prescribed performance control structure. First, a two-layer prescribed performance envelope is formulated via using an appointed-time convergent function for position-related and velocity-related consensus errors, respectively. Then, a simple state-feedback virtual controller with online adaptive performance adjustment is developed to preserve the communication connectivity. Moreover, to guarantee the velocity consensus of the networked systems and improve the position consensus accuracy, an appointed-time adaptive controller is designed by applying the norm inequality to the system uncertainties and external disturbances. Compared to the existing consensus control approaches, the prime advantage of the proposed one is that the constraints generated from the communication ranges are approximated by a time-varying contractive performance envelope, wherein, the appointed-time convergence and steady-state tracking accuracy are preassigned a priori. Meanwhile, no repeated logarithmic error transformations are required in the relevant controller design, which implies that the complexity of the devised control laws has decreased dramatically. Finally, two groups of illustrative examples are organized to validate the effectiveness of the proposed consensus control approach.

Connectivity Preserving Formation Stabilization in an Obstacle-Cluttered Environment in the Presence of Time-Varying Communication Delays

This technical article addresses the formation stabilization problem for multiagent systems (MASs) composed of dynamical agents moving within an obstacle-cluttered environment and sharing information via nonideal wireless communication networks. A novel distributed cooperative navigation function based control strategy is proposed, which drives the MAS to a desired formation without any collision while counteracting the presence of unavoidable communication impairments originated by the wireless network. By recasting the formation stabilization problem into a consensus one and by combining the Lyapunov stability theory with Halanay’s lemma, uniformly ultimately bounded stability of the whole delayed closed-loop system is proved. In the special case of an obstacle-free environment, our approach guarantees exponential stability of the closed-loop networked system. The stability analysis also provides an estimation of the delay upper bound and allows to evaluate the stability margins with respect to the latencies that can be observed in practical application scenarios. Theoretical derivations are verified through nontrivial simulations.