Formation Control Framework Research Articles

A Collision‐Free Pursuit‐Evasion Framework for Indirect Herding and Formation Control of Noncooperative UAVs

ABSTRACTIn this article, we aim to design a general framework for the pursuit‐evasion (PE) game of multiple UAVs. Based on the distance between the pursuers and the evader, the whole pursuit‐evasion process is decoupled into a seeking stage and a herding stage. A formation control method is proposed to make sure that the pursuers can find and drive the noncooperative evaders along a desired trajectory towards the designated target while maintaining a preset formation. In the meantime, an adaptive potential function is designed to achieve collision avoidance to both obstacles and other agents in the formation. The main contribution is that it combines formation control and collision avoidance in the problem of indirect herding, which is rarely addressed before. Also, the convergence and effectiveness of the designed controller for trajectory tracking and formation maintenance are proven and verified in the article. Simulation results in two and three dimensional space show that the proposed framework achieves the goal of coordinated herding and collision avoidance in a pursuit‐evasion scenario under uncertainties and external disturbances.

Collision-Free Formation Control for Heterogeneous Multiagent Systems Under DoS Attacks.

A safe time-varying formation (TVF) control framework is proposed in this article for heterogeneous multiagent systems under the constraints of denial of service (DoS) attacks, noncooperative dynamic obstacles, and input saturation. The framework integrates both the cyber-layer and physical-layer components to address the challenges posed by these adverse conditions. In the cyber-layer, a distributed resilient observer is provided based on a control Lyapunov function (CLF)-quadratic program (QP). This observer estimates a reference exosystem, effectively decoupling heterogeneous dynamics from unsafe networks and optimizing the system resilience against DoS attacks. At the physical-layer, for the first time, a collision-free TVF controller is presented based on the CLF-exponential control barrier function-QP. The controller guarantees high-order heterogeneous agents' operation safety under noncooperative obstacles and input saturation. The effectiveness and advantages of the proposed algorithms are verified through the comparative simulations and experiments conducted on a physical system comprising unmanned aerial vehicles and unmanned ground vehicles.

Dynamic Optimal Obstacle Avoidance Control of AUV Formation Based on MLoTFWA Algorithm

In addressing the optimal formation obstacle avoidance control problem for Autonomous Underwater Vehicles (AUVs) in environments with unknown and moving obstacles, this paper employs the Modified Fireworks Algorithm based on a Loser Elimination Mechanism (MLoTFWA) and constructs a Distributed Model Predictive Control (DMPC) framework to achieve obstacle avoidance for AUV formations. Initially, a prediction model is established, followed by feedback compensation to mitigate the effects of unknown perturbations. An appropriate fitness function is then formulated, and enhancements such as the loser elimination rule are introduced to optimize the fireworks algorithm. Additionally, the concept of an adaptive DMPC prediction window is proposed to conserve resources. The local and global stability of the DMPC formation control framework is theoretically proven. Simulations verify that the control system based on the DMPC framework ensures safe obstacle avoidance for the formation, maintains formation consistency, and achieves the shortest and smoothest path. The improved fireworks algorithm demonstrates superior performance compared with the original fireworks algorithm and other optimization algorithms. In testing, the improved fireworks algorithm exhibits better adaptability, higher average fitness, and best fitness, along with a significantly faster convergence speed. Compared with the ordinary fireworks algorithm, the convergence speed is reduced by 30%.

P-DRL: A Framework for Multi-UAVs Dynamic Formation Control under Operational Uncertainty and Unknown Environment

P-DRL: A Framework for Multi-UAVs Dynamic Formation Control under Operational Uncertainty and Unknown Environment

A deep learning optimized LQR method for enhanced formation control with embedded systems

To achieve higher accuracy throughout the formation control processes and enhance precision in dynamic environments, particularly for the formation control of follower vehicles with embedded systems, this paper proposes a method and framework for vehicle formation control. An Ackermann-model based Linear Quadratic Regulator (LQR) controller is developed for lateral distance control and a Proportional-Derivative (PD) controller for longitudinal distance control. To enhance the efficacy of the LQR controller, the Deep Deterministic Policy Gradient Derivative (DDPG) method is introduced into the control system. The DDPG networks are trained in a simulation environment and can subsequently predict LQR parameters in real-time experiments. The practical application of the method is showcased, and the concluding remarks emphasize the potential and superior performance of our proposed formation control approach by experimental comparison with other controllers. This method can be implemented in small vehicles that possess limited computational resources and is also suitable for scenarios requiring dynamic motion control with higher tracking accuracy and stability.

A Games-in-Games Framework for Task Allocation, Path Planning and Formation Control

A Games-in-Games Framework for Task Allocation, Path Planning and Formation Control

CIDC: A Flexible Formation Control Framework for Intelligent Connected Vehicles

CIDC: A Flexible Formation Control Framework for Intelligent Connected Vehicles

Fully Distributed Event-Triggered Formation Control for Multiple Quadrotors

This paper studies fully distributed formation control problems for multi-quadrotor systems with external disturbances via an event-triggered approach. For leader-follower multi-quadrotor systems, a fully distributed event-triggered formation control (DETFC) framework is proposed by utilizing an adaptive sliding-mode control approach, which does not rely on any global information of the network topology. The finite-time reachability of the sliding-mode surface can be guaranteed for the states of the nonlinear, coupled and underactuated system subject to external disturbances. Meanwhile, the zeno behavior of the proposed event-triggered protocol is proved to be excluded. A novel dynamic sliding-mode surface is designed to guarantee the formation performance as the quadrotor state trajectories move on the constructed sliding manifold. Via Lyapunov stability theory, sufficient conditions to ensure the formation results are derived for multi-quadrotor systems. Simulations and experiments are conducted to validate the effectiveness of the proposed control scheme.

Distributed Formation Control for a Multirobotic Fish System With Model-Based Event-Triggered Communication Mechanism

This paper confronts the formation control problem for a multi-robotic fish system with event-triggered communication mechanism. A three-dimensional (3-D) distributed formation control framework is proposed to drive the robotic fish agents to an anticipated configuration aligning with a moving target. In particular, a consensus-based formation control law is intended to realize the two-stage formation control process. Taking the energy-constrained occasions into consideration, the communication structure and event-triggered protocols are initially tailored. Meanwhile, the Lyapunov function is employed and the globally asymptotic stability of the proposed method is fully demonstrated. Afterwards, making use of the local measurements of triggering times, the unscented Kalman filter (UKF) is introduced and a novel model-based event-triggered mechanism is put forward to further mitigate otiose communication consumption. Finally, adequate simulations and experiments are carried out to verify the effectiveness and robustness of the proposed scheme. Thereby, the proposed formation control frame offers great potential for future practical marine operations of the underwater multi-agent systems.

Full-Field Vibration Response Estimation from Sparse Multi-Agent Automatic Mobile Sensors Using Formation Control Algorithm.

In structural vibration response sensing, mobile sensors offer outstanding benefits as they are not dedicated to a certain structure; they also possess the ability to acquire dense spatial information. Currently, most of the existing literature concerning mobile sensing involves human drivers manually driving through the bridges multiple times. While self-driving automated vehicles could serve for such studies, they might entail substantial costs when applied to structural health monitoring tasks. Therefore, in order to tackle this challenge, we introduce a formation control framework that facilitates automatic multi-agent mobile sensing. Notably, our findings demonstrate that the proposed formation control algorithm can effectively control the behavior of the multi-agent systems for structural response sensing purposes based on user choice. We leverage vibration data collected by these mobile sensors to estimate the full-field vibration response of the structure, utilizing a compressive sensing algorithm in the spatial domain. The task of estimating the full-field response can be represented as a spatiotemporal response matrix completion task, wherein the suite of multi-agent mobile sensors sparsely populates some of the matrix's elements. Subsequently, we deploy the compressive sensing technique to obtain the dense full-field vibration complete response of the structure and estimate the reconstruction accuracy. Results obtained from two different formations on a simply supported bridge are presented in this paper, and the high level of accuracy in reconstruction underscores the efficacy of our proposed framework. This multi-agent mobile sensing approach showcases the significant potential for automated structural response measurement, directly applicable to health monitoring and resilience assessment objectives.

Hierarchical Structure-Based Fixed-Time Optimal Fault-Tolerant Time-Varying Output Formation Control for Heterogeneous Multiagent Systems

This article studies the issue of distributed hierarchical fixed-time optimal fault-tolerant output formation for heterogeneous multiagent systems (MASs). A two-layer formation control framework is proposed by using distributed optimization and cooperative fault-tolerant output regulation approaches. The upper layer includes a virtual system and a distributed fixed-time optimization control algorithm to produce a global optimal reference signal, which minimizes the global objective function in a fixed time. The lower layer includes an actual agent system and an adaptive fixed-time fault-tolerant tracking control protocol to compensate for the actuator faults and ensure the fixed-time tracking of the optimal formation trajectory composed of the optimal reference signal and the expected time-varying formation vector. Different from the relevant works, this framework can avoid the phenomenon of fault propagation between neighboring agents by the interaction network. Furthermore, the convergence time of the formation error is not relied on the initial conditions of MASs. Finally, two simulation experiments are designed to prove the effectiveness of the developed theoretics.

Framework for formation control of jet-propelled unmanned surface vehicles

This study focuses on the research of spray-pump type unmanned surface vehicles (USVs). Based on the analysis of the dynamics and motion characteristics of USVs, a method combining Dubins curves and the particle swarm optimization algorithm is proposed to find the optimal or suboptimal solution for the formation path. The research goal is to switch multiple USVs from an unordered state to a formation state, taking the speed of USVs and the formation endpoint as dynamic variables and integrating Dubins curve theory into the particle swarm optimization algorithm. A theoretical framework for the formation of spray-pump type USVs is proposed. Finally, the formation of three USVs is realized in a simulation platform.

Continuous Fixed-Time Fault-Tolerant Formation Control for Heterogeneous Multiagent Systems Under Fixed and Switching Topologies

This article proposes a novel continuous fixed-time fault-tolerant formation control framework for heterogeneous multiagent systems (HMASs) involving unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and model uncertainties under fixed and switching topologies. Firstly, to estimate the information of actuator faults and uncertainties for each follower, a fixed-time observer is constructed. Then, an integral fixed-time sliding mode surface is designed such that the merits of high robustness, exact transient response and fixed-time convergence can be achieved. Subsequently, based on the integration of disturbance observer, high-order sliding mode and backstepping technologies, distributed and decentralized continuous fixed-time formation tracking control schemes under actuator faults are developed of the HMASs in the X-Y axis and Z axis, respectively. The proposed algorithm can not only handle time-varying formation in a timely manner, but also provide continuous control action under fixed and switching topologies. It is theoretically proved that the designed protocol can realize the expected fixed-time formation missions by employing the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the designed fixed-time control algorithm.

Formation Control of Unmanned Surface Vehicles Using Fixed-Time Non-Singular Terminal Sliding Mode Strategy

Unmanned surface vehicles (USVs) have been widely applied in the fields of marine hydrological exploration, marine resource exploration, area search, target detection, and military operations. In order to meet the demand of a complex ocean environment, USVs are frequently grouped together to improve the reliability of mission accomplishment. In this paper, a fixed-time control strategy, combined with a non-singular terminal sliding mode, is proposed for the formation control of USVs under complex external disturbances and system uncertainties. The main contributions of this paper are: (1) the leader–follower formation control framework is divided into a tracking control subsystem and a formation control subsystem. A new fixed-time non-singular terminal sliding mode (FTNTSM) strategy is developed for the tracking control subsystem, which dramatically increases the convergence rate and ensures closed-loop fixed-time stability; (2) a finite-time uncertain observer (FUO) is designed to observe lumped uncertainty items, which greatly increase the stability and robustness of the formation system; (3) the FUO-based fixed-time formation control (FUOFT-FC) strategy is designed for the formation control subsystem, which ensures the fast and stable formation of USVs. Fixed-time convergence of the formation system is established by Lyapunov stability analysis. Rigorous simulation and comparative studies demonstrate that the proposed method is superior to the state-of-the-art methods.

Soft formation control for unmanned surface vehicles under environmental disturbance using multi-task reinforcement learning

This paper proposes a distributed soft formation collision avoidance strategy to address the problem of formation obstacle avoidance in complex scenario under the environment interference where unmanned surface vehicles (USVs) are restricted in observation and possess no global reference frame for localisation. A multi-task training framework for formation control is also developed based on the motion characteristics of USVs and the leader–follower method. The soft actor–critic (SAC) reinforcement learning algorithm is adapted to construct agents. With elaborated auxiliary tasks, the soft formation algorithm demonstrates strong resilience to disturbances caused by unknown environmental loads or obstacle avoidance needs with only partial information about the environmental state, thus maintaining the formation shape. The proposed trajectory communication system and policy sharing mechanism allow USVs to approach the destination and avoid collisions whilst maintaining a noncompact formation that can be broken up temporarily to improve obstacle avoidance and can be restored quickly when the obstacle is avoided. Trained agents can be applied to different formations of varying sizes. Simulation results demonstrate the feasibility and effectiveness of the proposed method in different complex sea scenarios and its robustness to unknown environmental disturbances.

Learning-Based Multi-Robot Formation Control With Obstacle Avoidance

Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system’s ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning is proposed. The framework consists of two layers, namely the execution layer and the decision-making layer. The execution layer enables the robot to approach its target position and avoid collision with other robots and obstacles through a deep network trained by a reinforcement learning method. The decision-making layer organizes all robots into a formation through a new leader–follower configuration and provides target positions to the leader and followers. The leader’s target position is kept unchanged, while the follower’s target position is changed according to the situation it encounters. In addition, to operate more effectively in environments with different levels of complexity, a hybrid switching control strategy is proposed. The simulation results demonstrate that our proposed formation control framework enables the robots to adjust formation independently to pass through obstacle areas and can be generalized to different scenarios with unknown obstacles and varying number of robots.

Leaderless maneuver guidance and event-triggered formation control for distributed multi-space-robot systems

In this paper, the distributed displacement-based formation and leaderless maneuver guidance control problems of multi-space-robot systems are investigated by introducing event-triggered control update mechanism. A distributed formation and leaderless maneuver guidance control framework is first constructed, which includes two parallel controllers, namely an improved linear quadratic regulator and a distributed consensus-based formation controller. By applying this control framework, the desired formation configuration of multi-space-robot systems can be achieved and the center of leaderless formation can converge to the target point globally. Second, a pull-based event triggering mechanism is introduced to the distributed formation controller, which makes the control update independent of the events of neighboring robots, avoids unnecessary control updates, and saves the extremely limited energy of space robots. Furthermore, the potential Zeno behaviors have been excluded by proving a positive lower bound for the inter-event times. Finally, numerical simulation verifies the effectiveness of the theoretical results.

A Visual Predictive Control Framework for Robust and Constrained Multi-Agent Formation Control

A Visual Predictive Control Framework for Robust and Constrained Multi-Agent Formation Control

Hybrid formation control framework for solar-powered quadrotors via adaptive fission pigeon-inspired optimization

A formation control protocol of solar-powered unmanned aerial vehicles (SUAVs) has been studied in this paper. To improve the formation quality of the SUAVs swarm, a formation control protocol of behavior-based structure in the horizontal plane is designed, and a new obstacle avoidance strategy is proposed for safe and high-quality formation when crossing obstacles. The difference of charge and discharge power of the SUAV is considered in the design of the consensus protocol for energy management in the vertical direction, which can be considered as bounded random disturbances added to the controller. By means of potential function and Lyapunov function, the stability of the closed-loop system in the horizontal plane and vertical direction is analyzed respectively. In addition, adaptive fission pigeon-inspired optimization (AFPIO) is developed to select the element values of the adjacency matrix for the SUAVs swarm. Finally, numerical simulations under different conditions are carried out to verify the excellent formation quality and energy management effect of the proposed method.

Formation control for unmanned surface vessels: A game‐theoretic approach

AbstractTwo types of formation control problems for a group of unmanned surface vessels (USVs) are investigated by using a game‐theoretic approach. In the two formation frameworks, the USVs have the group formation objective of minimizing the formation errors with their local neighbors. Different from the existing formation control schemes for USVs with only the group objective, the formation control framework in this paper takes into account the individual objectives of the USVs in addition to the collaborative formation requirement for the USVs. Specifically, each USV in the first formation framework has the individual objective of aiming to be located at a given static reference target according to its own interests. And the USVs in the second formation framework have the individual objectives of following a given time‐varying reference target. Considering that the individual objectives of the USVs may conflict with the group formation objective, the formation problem of USVs is transformed to a noncooperative game among multiple USVs. Two controllers based on the Nash equilibrium seeking strategy are designed to solve the formation problem of USVs with conflicting objectives. It is proved that the USVs' states form an equilibrium formation pattern driven by the designed controllers. Simulations are performed to verify the theoretical results.