Event-triggered Control Algorithm Research Articles

Robust adaptive event-triggered control for unmanned surface vessel–unmanned aerial vehicle: Application to sea–air synchronized lawn mowing search operation

This paper investigates a robust adaptive event-triggered control algorithm with a consideration of the synchronized lawn mowing search guidance for a heterogeneous system comprising both an unmanned surface vessel (USV) and an unmanned aerial vehicle (UAV). For ensuring a cooperative global search navigation of the USV–UAV, a synchronized lawn mowing search guidance is developed with two key elements: the L1-based virtual ship (L1VS) and L1-based virtual aerial (L1VA), where the former can generate the smooth reference path for a USV according to the search task scope, while the latter can program the reference path for a UAV on the basis of its search requirements (search speed, detected capacity and communication distance). In the control loop, a robust adaptive path following control law is designed by utilizing an event-triggered mechanism and a minimal learning parameter (MLP) technique. In particular, the event-triggered mechanism has a potential role for a reduction of the unnecessary transmission resource usage for the channel of the controller to the actuator. Besides, only one learning parameters on each channel by introducing the MLP technique, implying a lower computational burden. Through the Lyapunov theorem, the semi-global uniform ultimate bounded (SGUUB) stability properties of the proposed control system. Finally, two numerical simulations are carried out to evaluate the effectiveness of the proposed strategy and the advantages compared with existing techniques.

Station-keeping strategy in emergency mode for wave gliders considering power shortages

The wave glider (WG) is an autonom surface vehicle (ASV) that is driven only by waves without propellers, and it maintains the power consumption of the control system and various payloads through solar power generation. To cope with the power shortages caused by extreme weather and sudden failures, a station-keeping strategy in emergency mode for WGs is proposed in this paper, so that the WG can remain in the current area with extremely low power consumption. More specifically, according to the power consumption mechanism of the WG, a partition control method is designed to set different operating modes in different areas. Also, an event-triggered (ET) motion control algorithm is proposed to reduce the execution rate of the rudder, which introduces adjustment factors to dynamically adjust the trigger threshold to further cut down rudder steering power consumption. Additionally, an online heading prediction system is developed to predict the submerged glider heading (SGH) of the WG through the float heading (FH) when the rudder management system shuts down. Through simulation and experimental analysis, the strategy in emergency mode can achieve positioning control with extremely low power consumption.

Dual-channel event-triggered model-free adaptive iterative learning control under DoS attacks

This paper investigates a dual-channel event-triggered model-free adaptive iterative learning control problem for unknown nonlinear systems under periodic denial-of-service (DoS) attacks. Firstly, periodic DoS attacks on the measured output of the system are modelled using the Bernoulli distribution. Then, to minimise the utilisation of system bandwidth resources, a dual-channel event-triggered mechanism is designed for the sensor-to-controller and controller-to-actuator channels. Subsequently, a dual-channel event-triggered model-free adaptive iterative learning control algorithm is introduced. The convergence of the tracking error is proven in terms of mathematical expectation using Lyapunov stability theory. Finally, the effectiveness of the proposed algorithm is verified through two simulation cases.

Bipartite consensus for multi-agent systems over signed networks: A novel dynamic event-triggered mechanism

This paper is concerned with the problem of dynamic event-triggered bipartite consensus control for nonlinear multi-agent systems based on signed networks. A new dynamic event-triggered control mechanism is proposed, whereby an adjustment variable is introduced to dynamically schedule the triggering frequency, enabling the minimization of the triggering times while ensuring system performance. Based on the designed event-triggered control algorithm, sufficient conditions are derived to guarantee that bipartite consensus can be reached by the considered nonlinear multi-agent system. Furthermore, it is proved that Zeno behavior will not occur. Numerical examples are provided in this paper to verify the effectiveness of the proposed control algorithm. The simulation results reveal that, compared with the static event-triggered mechanism and the traditional dynamic event-triggered mechanism, the proposed event-triggered algorithm can further reduce the triggering times and enhance the flexibility of the event-triggered mechanism.

Event-triggered controllers using contraction analysis

In this article, we introduce an event-triggered control method that relies on contraction analysis for linear time-varying systems (LTV) and then extend it to a certain class of nonlinear systems. Contraction analysis considers stability with respect to a nominal trajectory rather than an equilibrium point. If two neighbouring trajectories of a system are located in a contraction region, then they will tend to each other and to a nominal trajectory. In the event-triggered control algorithm that we introduce, we suggest to update the control law whenever the system trajectory is about to leave the contraction region. We show that such a scheduling of the control law guarantees system stability, and we show that a minimum inter-event time exists between consecutive updates of the control law. We also show how to place the system trajectory in a contraction region and demonstrate that the classical controllability assumption on LTV systems is enough to ensure the existence of the required transformation to perform that.

Memory-based event-triggered path-following control for a USV in the presence of DoS attack

This paper proposes a novel memory-based event-triggered path-following controller for unmanned surface vehicles (USVs) in the presence of denial of service (DoS) attacks and limited communication resources. In the guidance part, a double distance logical virtual ship (DDLVS) guidance law is developed to eliminate the accumulation error of the yaw angle velocity in the traditional LVS algorithm, and the USV will keep a smoother tracking performance for the curve reference path. In the control part, the memory-based event-triggered fusion control algorithm proposed in this paper incorporates the signal non-transmission phenomenon caused by DoS attacks into the event-triggered interval through a set of adaptive parameters. Consequently, the adverse affect caused by DoS attacks is solved in the process of designing the event-triggered control law. By combining the neural networks (NNs) and the dynamic surface control (DSC) techniques, the updating weights are significantly compressed. The proposed algorithm is with advantages of concise controller structure, low energy cost, and easy engineering implementation. All the error signals of the closed-loop system are proved to be semi-global uniform ultimate bound (SGUUB) by the Lyapunov analyses. Finally, through two numerical examples, including the comparative simulation and path following maneuver in Shuang island (an island distribute in Liaoning Dalian), are provided to verify the effectiveness of the proposed strategy.

Event-Triggered Predictive Control Algorithm for Multi-AUV Formation Modeling

Event-Triggered Predictive Control Algorithm for Multi-AUV Formation Modeling

Event-triggered adaptive control for quadrotor unmanned aerial vehicle with prescribed performance

This paper proposes an event-triggered adaptive control algorithm with prescribed performance for quadrotor unmanned aerial vehicles (UAVs). The dynamic uncertainty and unknown external interference of the system are compensated by the property of approximation in a radial basis function (RBF) neural network. Based on this, introduce a performance constraint function to ensure that the trajectory tracking error of quadrotor UAV converges to a preset area, thereby guaranteeing transient and steady-state performance. In addition, design an event-triggered adaptive controller with the combination of the event-triggered mechanism, reducing the controller’s update frequency. Simulation results demonstrate that this algorithm exhibits strong anti-interference properties and effectively resolves the flight control problem of quadrotor UAV.

Continuous and Periodic Event-Triggered Sliding-Mode Control for Path Following of Underactuated Surface Vehicles.

This article develops continuous and periodic event-triggered sliding-mode control (SMC) algorithms for path following of underactuated surface vehicles (USVs). Based on the SMC technology, a continuous path-following control law is designed. The upper bounds of quasi-sliding modes for path following of USVs are established for the first time. Subsequently, both continuous and periodic event-triggered mechanisms are considered and added into the proposed continuous SMC scheme. It is demonstrated that with appropriate selecting of control parameters, the use of hyperbolic tangent functions does not affect the boundary layer of quasi-sliding mode caused by event-triggered mechanisms. The proposed continuous and periodic event-triggered SMC strategies can make the sliding variables reach the quasi-sliding modes and stay in there. Moreover, energy consumption can be reduced. Stability analysis shows that the USV can follow a reference path by using the designed method. The simulation results show the effectiveness of the proposed control methods.

Event-triggered anti-disturbance control for aerial recovery drogue stabilization with guaranteed transient performance constraints

Aiming at the drogue docking control problem in aerial recovery, this paper proposes a performance event-triggered control algorithm based on disturbance observer to stabilize the flexibly towed aerial recovery drogue subject to the unknown airflows, cable towing tensions, limited computation capacity, and actuating power resources with guaranteed transient performance. Firstly, the control-oriented 6 degrees of freedom (DOF) affine nonlinear dynamics of the flexible cable-towed drogue are formulated. Then, the event-triggered extended state observers (ETESO) are established for the trajectory and attitude subsystems by utilizing intermittently measured state, to accurately estimate the lumped disturbances caused by the unmeasurable cable tension and unknown airflow disturbance while reducing the computation cost from the sensor to the observer. Moreover, to constrain the trajectory tracking error within the guaranteed transient performance constraint with any small overshoot, an ETESO-based event-triggered control algorithm for drogue was established to ensure the specified transient performance; finally, the closed-loop stability is discussed using Lyapunov analysis. The simulation results confirmed that this method can not only constrain the drogue within the expected small error range but also effectively reduce computational costs and resource occupation, with the best control effect.

LPVS guidance and adaptive event-triggered control for an underactuated surface vessel with the prevention of obstacle’s vicious maneuvering

This paper investigates the adaptive neural event-triggered control for an underactuated surface vessel (USV), considering constraints of the obstacle’s vicious maneuvering and the limited communication channel. In the algorithm, a novel logical phantom virtual ship (LPVS) guidance principle is developed to generate the global path following reference and the obstacle avoidance order in the simulation results, where the corresponding operation comply to the suggestion in international regulations for prevention collision at sea (COLREGs). The improved design of velocity obstacle (VO) method can guarantee its predictive capability to prevent the obstacle’s vicious maneuvering. As for the control module, the adaptive event-triggered control algorithm is proposed by employing the robust neural damping technique and the input event-triggered mechanism. And the derived adaptive law can effectively solve perturbations from the gain uncertainty and the external disturbances. Through the theoretical analysis, all signals of the closed-loop control system are with the semi-globally uniform ultimate bounded (SGUUB) stability. The simulation experiments have been presented to verify the obstacle avoidance effectiveness and the burden-some superiority of the algorithm.

Collision avoidance in target encirclement and tracking of unmanned aerial vehicles under a dynamic event-triggered formation control

This paper addresses the dynamic event-triggered fixed-time distributed control problem of unmanned aerial vehicle (UAV) formation for target encirclement and tracking with collision avoidance. A fixed-time radial basis function (RBF) neural network (NN) adaptive disturbance observer is designed for faster estimation and compensation of the lumped disturbances from the UAV formation system. By extending the lemmas of interconnected systems and fixed-time, a novel fixed-time interconnected system lemma is proposed. Then, a dynamic event-triggered control algorithm is proposed to establish the stability of the complex closed-loop interconnected system for target encirclement and formation tracking in fixed time and to demonstrate that the convergence time of the system is independent of the initial state. Subsequently, a risk avoidance mechanism is incorporated into the event-triggered fixed-time control framework to avoid collisions within the UAV formation. The proposed control strategy has been rigorously mathematically demonstrated. Finally, using a visual simulation platform, several simulations are presented to illustrate the remarkable performance and superiority of the proposed control method.

Decentralized optimal control of large-scale partially unknown nonlinear mismatched interconnected systems based on dynamic event-triggered control

In this paper, a novel decentralized control method is proposed for nonlinear mismatched large-scale interconnected systems subjected to partially unknown dynamics by designing auxiliary control for each subsystem. It is demonstrated that the control sequence consisting of the optimal control policies of auxiliary control can stabilize the system asymptotically, leading to decentralized control of the large-scale system. An integral reinforcement learning (IRL) method is firstly proposed, replacing the traditional policy iterative algorithm to analyze the optimal control problem of each edge subsystem with partially unknown dynamics. After that, the edge-based dynamic event-triggered control algorithm is proposed based on the static event-triggered control method, and an internal dynamic variable characterized by a first-order filter is defined. A single critic neural network (NN) is then designed to learn the approximate optimal control strategy under the dynamic event-triggered mechanism adaptively. The stability analysis is proposed to demonstrate that the state of the event-based pulse system is ultimately uniformly bounded (UUB) and the Zeno behavior is eliminated successfully. Finally, the effectiveness of the proposed algorithm is verified by two simulation examples to realize decentralized control of mismatched large-scale systems.

Event-triggered critic learning impedance control of lower limb exoskeleton robots in interactive environments

In this paper, we present an event-triggered critic learning impedance control algorithm for a lower limb rehabilitation exoskeleton robot in an interactive environment, where the control objective is specified by a desired impedance model. In comparison to many other traditional impedance controller design algorithms, in this paper, the impedance control problem is transformed into an optimal control problem. Firstly, the interactive environment accounts for the interaction between the exoskeleton, the human, and the environment, and is modeled by a linear time-invariant exogenous system. Secondly, in contrast to time-triggered control design mechanisms, the event-triggered controller is updated only when the system states deviate from prescribed threshold values. To obtain the event-triggered optimal controller, a critic neural network is developed through the framework of reinforcement learning. A modified gradient descent method is introduced to update the weights of the critic network with an additional stable term employed to eliminate the need for an initial admissible control. Meanwhile, with the simultaneous application of historical and transient state data to the critic neural network, the persistent excitation conditions are relaxed. The Lyapunov method is used to rigorously demonstrate the stability of the overall system. Finally, the effectiveness of the proposed algorithm is demonstrated via simulation.

Finite-time adaptive event-triggered control for USV with COLREGS-compliant collision avoidance mechanism

This paper focuses on the real time intelligent navigation scheme for unmanned surface vehicle (USV) subject to collision avoidance regulations. The proposed approach is formulated as two segments: the real time guidance principle with COLREGS-compliant collision avoidance mechanism and the finite-time adaptive event-triggered control algorithm. The pointed improvement for the artificial potential field (APF) can guarantee USV possess the excellent avoidance function in accordance with COLREGS regulations. Especially, when the obstacle makes dangerous actions, the emergency obstacle avoidance operation (EOAO) is implemented according to the real time position of obstacles. Based on the proposed guidance principle, a finite-time robust neural control algorithm is developed by fusion of the finite-time stability theory and event-triggered mechanism. The intermittent computing and communication can effectively reduce the energy and resource consumption of the control system. Moreover, the finite-time adaptive controller design contributes to smaller computation load and facilitates the implementation of the algorithm in ocean engineering. The semi global finite-time uniformly boundedness (SGFTUB) of the closed-loop system is guaranteed through the Lyapunov theory. Finally, the numerical simulation and comparison experiment are illustrated to verify the guidance and control performance of the proposed algorithm.

Bipartite synchronization for cooperative-competitive neural networks with reaction–diffusion terms via dual event-triggered mechanism

The pinning-like bipartite synchronization is investigated for reaction–diffusion neural networks with cooperative-competitive interactions in this paper. First, a dural event-triggered control algorithm based on the time–space sampled-data scheme is employed to further decrease the transmission resources’ consumption. Then, some sufficient conditions that guarantee the bipartite synchronization for the target neural networks with the signed graph are obtained by virtue of the Lyapunov method, Halanay’s inequalities, and the pinning control technique. Moreover, new weighted integral inequalities are introduced to get higher upper bounds than what traditional inequality produces. Finally, a numerical simulation result is given to validate the advantages of the proposed method for realizing bipartite synchronization.

Adaptive Boundary Vibration Control and Angle Tracking Consensus of Networked Flexible Timoshenko Manipulator Systems

The adaptive boundary vibration control and leader–follower angle tracking consensus problem of networked flexible Timoshenko manipulator systems in the case of parametric uncertainties and external disturbances are investigated. To mitigate the impacts of external disturbances and parameter uncertainties, we employ the adaptive tuning techniques to design some suitable adaptive terms. Then with network topology is connected, we propose some novel distributed adaptive continuous-time boundary control algorithms. Based on a novel combination of Lyapunov-based control and Barbalat’s lemma, the asymptotic control performances can be achieved. Besides, some novel distributed adaptive event-triggered boundary control algorithms are also proposed for the undisturbed Timoshenko manipulators. The advantage of the considered event-triggered algorithms is that they can avoid continuous updates of the controller and thereby can effectively save control resources. Moreover, Zeno behaviors are excluded for all agents to ensure the practicality of digital sampling. Finally, we illustrate the feasibility of the proposed control algorithms through numerical simulation examples.

Event-triggered near-optimal tracking control based on adaptive dynamic programming for discrete-time systems

Frequent state monitoring and controller updates can enhance the precision of tracking control, while simultaneously overburdening the communication network transmission. In this paper, For the purpose of saving communication costs, we propose event-triggered control algorithms for the optimal tracking control problem. First, we reconstruct the discrete-time nonlinear system into a converted system. Then, the adaptive dynamic programming algorithm is employed to find the optimal controller off-line, and the event-triggered scheme is used to reduce the communication costs online. Novel triggering conditions with fewer assumptions are designed to implement the event-triggered scheme. Different from existing works, the event-triggered scheme can be introduced not only into the converted system but also into the actual system, which is more practical because the actual controller is what one can only access in practice. In addition, with the developed algorithms, the tracking error can be proved to be stable at the origin, in other words, the actual system can be guaranteed to track the desired trajectory. Algorithms developed in this paper are implemented by three neural networks, the model network, the action network and the critic network. Finally, examples are presented to verify the effectiveness and rationality of the algorithms.

A dynamic event-triggered network control algorithm combined with gradient-sharing asynchronous advantage actor-critic strategy

In the existing memory event-triggered control (METC) algorithms, the threshold parameters and memory weights are fixed, reducing the system’s adaptability. In this paper, a new intelligent dynamic METC algorithm is proposed to reduce the amount of transmission data and decrease the communication burden in networked control systems. The proposed dynamic METC mechanism applies the gradient-sharing asynchronous advantage actor-critic (A3C-GS) learning algorithm to optimized memory event-triggered function of memory weights and threshold parameters. A time-varying delay system model for dynamic METC is developed by incorporating the A3C-GS for the networked control system with communication delays. Then, by solving two linear matrix inequalities (LMIs), the controller gain parameters of the networked control systems are derived. Finally, the performance of the proposed new METC algorithm is compared with the current three event-triggered control methods. Simulation results show that the proposed intelligent event-triggered algorithm reduces the number of triggers by about 40% compared with the traditional event-triggered algorithm under the pregiven simulation time. Thus, the effectiveness of the main results is verified.

Hybrid threshold event-triggered control for sail-assisted USV via the nonlinear modified LVS guidance

This paper presents an adaptive event-triggered control algorithm for sail-assisted unmanned surface vehicle (USV) under nonlinear modified logic virtual ship (LVS) guidance, which addresses the issues of communication load and input saturation constraint. In guidance module, the reference signal generated by LVS guidance is further processed by nonlinear modification rules. That can effectively remove the abrupt phenomenon of actuators. In the control module, a robust adaptive control technique for sail-assisted USV is proposed by fusing adaptive compensation technology and hybrid threshold event-triggered. Unnecessary transmission is reduced between controller and actuator by constructing hybrid threshold event-triggered mechanism. Different from most existing event-triggered mechanism with fixed threshold parameters, threshold parameters in hybrid threshold event-triggered are dynamically adjusted according to the demands of the controller. Furthermore, the compensation effect of sail on the dynamic mechanism is considered. Adaptive parameters of rudder and propeller are constructed to improve the robustness of the closed-loop system. Finally, the theoretical stability of the proposed method is proved by Lyapunov stability theorem, and the control performance is evaluated by simulation platform, which verifies that the scheme has reliable tracking accuracy under time-varying disturbances.