Cooperative Control for Multiple Air Vehicles

In this paper, we study cooperative control problems with a multi-UAV system. Specifically, we propose a c consensus-based cooperative formation control strategy with collision-avoidance capability for a group of multiple unmanned aerial vehicles (UAVs). To cooperatively fly in formation, a consensus-based algorithm and a leader-follower structure are applied to the UAVs. Collisions among the UAVs can occur while they are flying with the cooperative control algorithm. Therefore, we describe how the cooperatively controlled UAVs avoid collisions while they are flying to desired position for formation. The collision-avoidance strategy is based on an artificial potential approach. The convergence is guaranteed even when the cooperative formation control algorithm and the collision-avoidance control algorithm are simultaneously applied to the quadrotors. Finally, the proposed approach is validated by some simulations.

: Unmanned aerial vehicles (UAVs) are critical components of the future naval forces. UAV control and monitoring with autonomous operation will become an absolute necessity and adaptive cooperation of vehicles is the only practical alternative. The objective of this project is to develop and evaluate new methodologies for cooperative (formation) control of multiple unmanned air vehicles. The goal is to have multiple UAVs working together as a group. Instead of separately assigning distinct tasks to each vehicle, the operator would assign tasks to the UAV group, which then determines the best way to accomplish each task, freeing the operator to maintain surveillance over the entire operation. In this project we investigated Path Tracking and obstacle avoidance of UAVs using fuzzy logic method. Algorithms for close formation control of multi-UAVs are developed and simulated. We also investigated fault-tolerant control of single UAVs by neuro-adaptive method. Detailed description of this method is provided in this document. The project has supported S graduate students with 9 technical papers published.

: Recent years have seen rapidly growing interest in the development of networks of multiple unmanned aerial vehicles (UAVs), as aerial sensor networks for the purpose of coordinated monitoring, surveillance, and rapid emergency response. This has triggered a great deal of research in higher levels of planning and control, including collaborative sensing and exploration, synchronized motion planning, and formation or cooperative control. In this paper, we describe our recently developed experimental testbed at the University of Pennsylvania, which consists of multiple, fixed-wing UAVs. We describe the system architecture, software and hardware components, and overall system integration. We then derive high-fidelity models that are validated with hardware-in-the-loop simulations and actual experiments. Our models are hybrid, capturing not only the physical dynamics of the aircraft, but also the mode switching logic that supervises lower level controllers. We conclude with a description of cooperative control experiments involving two fixed-wing UAVs.

Cooperative formation control of unmanned ground vehicles (UGVs) has become one of the important research hotspots in the application of UGV and attracted more and more attention in the military and civil fields. Compared with traditional formation control algorithms, reinforcement-learning-based algorithms can provide a new solution with a lower complexity for real-time formation control by equipping UGVs with artificial intelligence. Therefore, in this paper, a distributed deep-reinforcement-learning-based cooperative formation control algorithm is proposed to solve the navigation, maintenance, and obstacle avoidance tasks of UGV formations. More importantly, the hierarchical triangular formation structure and the newly designed Markov decision process for UGV formations of leader and follower attributes make the control strategy learned by the algorithm reusable, so that the formation can arbitrarily increase the number of UGVs and realize a more flexible expansion. The effectiveness and scalability of the algorithm is verified by formation simulation experiments of different scales.

We introduce a mathematical model for the study of cooperative control problems for multiple autonomous air vehicles (AAVs) connected via a communication network. We propose a cooperative control strategy based on task-load balancing that seeks to ensure that no vehicle is underutilized and we show how to characterize task-load balancing as a stability property. Then, using Lyapunov stability analysis, we provide conditions under which task-load balancing is achieved even in the presence of communication delays. Finally, we investigate performance properties of the cooperative controller using Monte Carlo simulations. This shows the benefits of cooperation and the effects of network delays and communication topology on performance

This paper is concerned with the formation control of multiple fight vehicles, and by introducing the energy states, an energy-based cooperative method is proposed. The fight vehicle system is modelled with the communication topology and formation description. Then, the cooperative formation strategy is discussed with collision avoidance. It provides the formation commands which are transformed into the reference signals of each vehicle. Based on theses analysis, the energy-based controller is designed to accomplish the tracking of the commands. Compared to most conventional methods, it does not require the dynamics of velocity and flight path angle to be separated into two subsystems, but integrate them to regulate the energy of the vehicle to achieve stable flight and perform the desired formation. Simulation is conducted to illustrate the effectiveness of the proposed method.

The dynamic event‐triggered cooperative formation control problem for unmanned aerial vehicles (UAVs) subject to time‐varying disturbances is studied. A single UAV is described by a second‐order kinematical model and the desired formation is constructed by utilising the leader–follower framework, in which the leader UAV provides the desired trajectory for each follower. Unknown external disturbances are assumed to be composed of multiple frequency waves and appropriate disturbance observers are designed. A dynamic event‐triggered mechanism is proposed with its triggering threshold being dynamically adjustable rather than a fixed constant, which can reduce data transmissions more effectively and meanwhile avoid the Zeno behaviour. Then a distributed event‐triggered control scheme is developed for the UAV formation and two sufficient conditions on asymptotical stability of the resulting closed loop system are derived. Furthermore, controller gains, observer gains, and triggering parameters can be jointly obtained by solving the desired linear matrix inequality (LMI). Finally, a simulated example is given to illustrate the obtained results.

This paper considers cooperative formation control on networked multi-agent systems, in which the mobile agents have limited resources. Two event-based strategies are introduced to reduce resource utilization and control actuation in formation control. In one strategy, the event trigger function is designed to use system state, whereas in the other, it is designed to use control input. Theoretical and experimental analyses of the pros and cons of the two strategies are given. In addition, the stabilities of the two event-triggered formation control laws are discussed. The results of simulations conducted confirm the feasibility and effectiveness of the proposed methods.

Robust finite-time cooperative formation control of UGV-UAV with model uncertainties and actuator faults

In order to study the group cooperative control method of multiple intelligent networked vehicles, the multiple intelligent networked vehicles can move in the form of a fleet. Based on the leader–follower method, the paper optimizes the control effect of the leader–follower method by solving the error transmission phenomenon in the leader–follower method. In this paper, the modeling of the multiple intelligent connected vehicle adopts the vehicle dynamics model and the Magic Formula/Swift Magic tire model, and adopts the model predictive control (MPC) dynamics trajectory tracking controller for control. Through the CarSim–Simulink multi-vehicle dynamics co-simulation platform established in this paper, the group cooperative control experiments of multiple intelligent networked vehicles under different working conditions were carried out for simulation verification. The analysis results show that the maximum average error of the proposed method decreases from 8.802 to 0.094 in the case of straight line and 0.669 to 0.379 in the case of curve tracking, which proves that the method can effectively reduce the transmission of errors.

This paper considers the cooperative control problem for the unmanned ground vehicle swarm system with cyber interference. Each vehicle in the system is intelligent and connected with each other via networks. The cyber security issue is inevitable and vital in the control design for the swarm system. Taking account of the cyber interference and system uncertainty, we explore the cooperative control for the unmanned ground vehicle swarm system. In order to describe the behaviors of mutual attraction, repulsion and overall trajectory tracking in a mathematical manner, we abstract the integrated potential function, thereby the kinematic model. Treating the mathematical model as the system constraint and decomposing the system uncertainty, we propose a class of cooperative adaptive robust controls to assure that all the unmanned ground vehicles follow the constraint to move. Four major system performances are achieved: (i) global stability (i.e., uniform boundedness and uniform ultimate boundedness), (ii) compact formation, (iii) cooperative hunting, (iv) trajectory tracking.

According to the characteristics of cooperative motion control of humanoid robot, a cooperative motion model of two humanoid robot in ball handling was deduced and a control method based on second order cone principle was introduced to maintain the stability in movement for multi-robot. By deducing constraints of stability in cooperative movement, an equation of SOCP was constructed, and the stability control problem was converted for solving equations of second order cone. Finally, the effectiveness of the proposed method is verified by simulation and experiment.

Cooperative control for multiple vehicles is a promising technology with the capability to improve traffic efficiency and fuel savings. Given its potential for both commercial and military applications, multiple unmanned vehicle formation has attracted considerable attention recently. In this paper, the use of formation control for connected and autonomous vehicles was explored and a novel distributed formation control approach was proposed. To begin, the evolution mechanism of multi-lane formation was investigated and a formation transition model based on a finite state machine was constructed. A bi-level formation control scheme was then proposed; this framework’s upper and lower levels were used to perform trajectory planning and MPC-based control, respectively. A novel trajectory planning approach was constructed by combining the distributed consensus algorithm and the potential field method. Moreover, additional acceleration constraints were imposed on the trajectory planning algorithm. Finally, three scenarios were designed to validate the proposed formation control algorithm using Webots. The results illustrate that a formation deployed with the proposed formation control algorithm can handle abnormal situations and realize consensus within 12s.

This article presents a nearly optimal solution to the cooperative formation control problem for large-scale multiagent system (MAS). First, multigroup technique is widely used for the decomposition of the large-scale problem, but there is no consensus between different subgroups. Inspired by the hierarchical structure applied in the MAS, a hierarchical leader-following formation control structure with multigroup technique is constructed, where two layers and three types of agents are designed. Second, adaptive dynamic programming technique is conformed to the optimal formation control problem by the establishment of performance index function. Based on the traditional generalized policy iteration (PI) algorithm, the multistep generalized policy iteration (MsGPI) is developed with the modification of policy evaluation. The novel algorithm not only inherits the advantages of high convergence speed and low computational complexity in the generalized PI algorithm but also further accelerates the convergence speed and reduces run time. Besides, the stability analysis, convergence analysis, and optimality analysis are given for the proposed multistep PI algorithm. Afterward, a neural network-based actor-critic structure is built for approximating the iterative control policies and value functions. Finally, a large-scale formation control problem is provided to demonstrate the performance of our developed hierarchical leader-following formation control structure and MsGPI algorithm.

This paper considers formation control of a group of wheeled mobile robots with uncertainty. Decentralized cooperative robust controllers are proposed in two steps. In the first step, cooperative control laws are proposed for multiple kinematic systems with the aid of results from graph theory such that a group of robots comes into a desired formation. In the second step, cooperative robust control laws for multiple uncertain dynamic systems are proposed with the aid of backstepping techniques and the passivity properties of the dynamic systems such that multiple robots comes into a desired formation. Since communication delay is inevitable in cooperative control, its effect on the proposed controllers is analyzed. Simulation results show the effectiveness of the proposed controllers.