Event-triggered-based adaptive dynamic programming for distributed formation control of multi-UAV

This article investigates the problem of path following for the underactuated unmanned surface vehicles (USVs) subject to state constraints. A useful control algorithm is proposed by combining the backstepping technique, adaptive dynamic programming (ADP), and the event-triggered mechanism. The presented approach consists of three modules: guidance law, dynamic controller, and event triggering. First, to deal with the "singularity" problem, the guidance-based path-following (GBPF) principle is introduced in the guidance law loop. In contrast to the traditional barrier Lyapunov function (BLF) method, this article converts the USV's constraint model to a class of nonlinear systems without state constraints by introducing a nonlinear mapping. The control signal generated by the dynamic controller module consists of a backstepping-based feedforward control signal and an ADP-based approximate optimal feedback control signal. Therefore, the presented scheme can guarantee the approximate optimal performance. To approximate the cost function and its partial derivative, a critic neural network (NN) is constructed. By considering the event-triggered condition, the dynamic controller is further improved. Compared with traditional time-triggered control methods, the proposed approach can greatly reduce communication and computational burdens. This article proves that the closed-loop system is stable, and the simulation results and experimental validation are given to illustrate the effectiveness of the proposed approach.

Multi-agent formation control is an important part of distributed perception and cooperation, which is convenient to complete various complex tasks and would be a key research direction in the future. This paper reviews the corresponding problems of formation control and the existing centralized and distributed formation control strategies. In particular, we discuss four types of distributed formation control methods based on position and displacement in the global coordinate system and distance and bearing in the nonglobal coordinate system, respectively. Moreover, this paper analyzes affine formation which does not require the global coordinate system. Combined with the current practical applications of multi-agent systems, the latest research for the formation control of the unmanned aerial vehicle (UAV), unmanned ground vehicle (UGV), unmanned surface vehicle (USV) and autonomous underwater vehicle (AUV) is given. Finally, the challenges and opportunities in this burgeoning field are discussed.

In this paper, a finite-time controller is proposed for the quadrotor aircraft to achieve hovering control in a finite time. The design of controller is mainly divided into two steps. Firstly, a saturated finite-time position controller is designed such that the position of quadrotor aircraft can reach any desired position in a finite time. Secondly, a finite-time attitude tracking controller is designed, which can guarantee that the attitude of quadrotor aircraft converges to the desired attitude in a finite time. By homogenous system theory and Lyapunov theory, the finite-time stability of the closed-loop systems is given through rigorous mathematical proofs. Finally, numerical simulations are given to show that the proposed algorithm has a faster convergence performance and a stronger disturbance rejection performance by comparing to the PD control algorithm.

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

In view of the marine surface vehicles are subject to unknown time-varying external disturbance and its velocities are not available for feedback, a robust finite-time output feedback control scheme is presented for multiple marine surface vehicles to achieve precise formation control in this paper. Without neglecting the first time derivative of disturbance, a novel finite-time extended state observer (FTESO) is proposed to precisely estimate the external disturbance and velocity information in finite time. Based on the outputs of FTESO, the distributed formation controller is designed to guarantee that a group of marine surface vehicles can track the time-varying virtual leader in finite time with precise tracking performance. The errors of the proposed FTESO and formation controller can be guaranteed to converge to zero in finite time by using the homogeneous method and the Lyapunov theory. Numerical simulations are presented to illustrate the effectiveness of the proposed finite-time output feedback formation control scheme.

In this work, the flexible performance-based control scheme is presented for the networked uncertain mechanical system under the quasi-periodic denial-of-service (DoS) attack and the appointed event-triggering (ET) mechanism. This scheme features the capability of releasing and recovering the prescribed performance in light of the activation and sleeping of the DoS attack. These functionalities attribute to the introduction of a self-regulation auxiliary system that is developed based on the theories of the positive system and the finite-time stability. Furthermore, the neural network (NN) is utilized to approximate the system uncertainty, and the noncertainty-equivalent structure is adopted to construct the update law of the NN. Then, the influence of the quasi-periodic DoS attack and the appointed ET mechanism on uncertainty approximation are totally isolated. Based on the Lyapunov function method and the invariant set theory, the boundedness of the closed-loop system is theoretically proved. Finally, both the numerical and experimental illustrations are presented to demonstrate the effectiveness and the benefits of the developed control scheme.

Due to its high manoeuvrability, portable size, high payload capability and good expandability, Unmanned Aerial Vehicle (UAV) is capable of accomplishing varieties of tough missions. In particular, cooperative multiple UAVs can significantly increase the mission efficiency and effectiveness. In some applications with complex environments such as narrow canyon, distributed formation with adjustable shape is required. Moreover, a reliable autopilot safety system is mandatory so that the flight does not pose threats to the surrounding environment. In this research, distributed formation control of multiple UAVs with built-in comprehensive safety features for operation in complex environment is studied. Distributed formation control has been studied in an existing work by integrating proportional control into its path planner. There exists, however, steady state errors when an external reference velocity is applied to a certain UAV. In order to improve the formation control and eliminate the steady-state error, a proportional-integral (PI) controller is proposed. It is shown that the proposed distributed PI controller can stabilize the system and achieve the zero steady-state error. Numerical and hardware in the loop (HIL) simulations and flight tests are conducted to verify the effectiveness of the proposed approach. Since distributed formation is controlled by an on-board autopilot, failures may happen due to system or hardware faults. Hence, safety and robustness of the on-board autopilot become very important. Therefore a comprehensive approach on the implementation of safety operation for multiple UAVs formation in outdoor environments is proposed. A pre-flight UAV status inspection schedule and a hierarchical three layers in-flight safety architecture are developed based on a customized risk assessment matrix.

We present a distributed control strategy for a team of fixed-wing Unmanned Aerial Vehicles (UAVs), such that they achieve a desired formation and travel along a desired direction at a constant altitude. We describe the motion of UAVs with the kinematic unicycle model, and impose the constraints that the airspeed and turning rate of UAVs must satisfy some practical bounds. Based on this model, a control strategy is proposed, and local convergence of the team to the desired formation and travel direction is proved. The control direction returned by our strategy is well-suited as a high-level motion planning input to a low-level UAV autopilot, which can compensate for the aerodynamics, wind effects, disturbances, etc., that are not accounted for in the unicycle model. The proposed strategy is fully distributed and can be implemented using relative position measurements acquired by UAVs in their local coordinate frames. Furthermore, UAVs do not need to communicate. Simulations are provided to typify the proposed strategy.

This article investigates the problem of attitude tracking control for spacecraft with limited communication, unknown system parameters, and external disturbances. An adaptive control scheme with an event-triggered mechanism (ETM) is proposed to alleviate the communication burden. Radial Basis Function Neural Network (RBFNN) estimation model is developed to provide the input signals for the control module in this control scheme. Estimated attitude information of the spacecraft generated from the estimation model will only be transmitted to the control module at the instants when the ETM is violated. The neural network (NN) and the estimation model will be updated complying with an adaptive algorithm at the discrete triggering instants. It’s substantiated that all the errors of attitude tracking converge towards corresponding residuals and there are no accumulated triggering instants. Numerical simulation also demonstrates the effectiveness of the proposed control method.

Adaptive neural network based quadrotor UAV formation control under external disturbances

This article investigates the adaptive fuzzy distributed optimal event-triggered (ET) control problem for high-order nonlinear multiagent (NMA) system under weight-unbalanced directed graph. An optimal signal generator with ET mechanism is first established to estimate optimization point and optimal network channel utilization between agents and neighbors. Second, since the ET mechanism is introduced in optimal signal generator, the first-order derivative of optimal virtual signals is not continuous at the trigger instant, a high-order filter is established to estimate high-order derivatives of optimal virtual signals. Based on the optimal signal generator with ET mechanism and high-order filter, an adaptive fuzzy distributed optimal ET control scheme is proposed by backstepping control design technology. It is shown that controlled NMA system is asymptotically stable and the global cost function is minimized. Finally, we apply the proposed distributed optimal ET control approach to marine surface vehicle systems, the simulation and comparison results verify its effectiveness.

Event-triggered adaptive optimal tracking control for nonlinear stochastic systems with dynamic state constraints

This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which relies on system states only and is unrelated to virtual control laws. In this case, the high-order neural networks (NNs) are used to approximate the ideal control input (but not the virtual control laws), and then the corresponding adaptive neural controller is developed under the ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, is designed to guarantee the uniformly ultimate boundedness (UUB) of NN weight estimates for all sampling times. In virtue of the bounded NN weight estimates and a dead-zone operator, the ET condition together with an adaptive ET threshold coefficient is constructed to guarantee the UUB of the closed-loop networked control system through the Lyapunov stability theory, thereby largely easing the network communication load. The proposed ET condition is easy to implement because of the avoidance of: 1) the use of the intermediate ET conditions in the backstepping procedure; 2) the computation of virtual control laws; and 3) the redundant triggering of events when the system states converge to a desired region. The validity of the presented scheme is demonstrated by simulation results.

In this paper, the distributed optimization problem for multi-agent system (MAS) formation control of unmanned aerial vehicles (UAVs) is suggested here. The situation that the internal states of a single UAV can be made fully available was aimed at, the internal optimal control law of a single UAV is designed using the optimal control theory. To cope with the obstacle that each agent in the system can only communicate with its neighbor agents, the distributed formation control law of the system is introduced based on the communication topology of the system, and further the stability of the system is analyzed in helps of graph theory. The validity of the suggested scheme is verified by both the numerical simulation and UAV platform.

This paper primarily addresses the design of distributed optimal cooperative controllers and the utilization of a reinforcement learning (RL)-based event-triggered mechanism for multi-agent systems (MASs) with unknown dynamics. By setting an extra compensator, the augmented system is constructed to overcome the dependence for system dynamics. Then, to address the issue of computational burden, we utilize an event-triggered mechanism based on reinforcement learning (RL) and neural networks (NNs) to implement the adaptive dynamic programming (ADP) algorithm. Additionally, we take into consideration the trade-off between computational burden and achieving consensus control by introducing a weighting factor in the reward design for MASs. With this reward design, we present an algorithm based on the deep deterministic policy gradient (DDPG) algorithm to learn the event-triggered condition for MASs and achieve a balance between these two factors. The event-triggered mechanism of our algorithm can also identify constraints such as time limitations or computational resource restrictions, aiming to achieve consensus control without violating these constraints. We demonstrate the absence of Zeno behavior and the uniform ultimate boundedness (UUB) of both local consensus error and weight estimation error. Finally, simulation results illustrate the effectiveness of the control algorithm and the weighting factor.