Adaptive Event-triggered Control Research Articles

Adaptive Event-Triggered Finite-Time Control for Switched Nonlinear Systems

Adaptive Event-Triggered Finite-Time Control for Switched Nonlinear Systems

Reinforcement Learning-Based Adaptive Event-Triggered Fuzzy Control for Cyclic Switched Stochastic Nonlinear Systems With Actuator Faults

Reinforcement Learning-Based Adaptive Event-Triggered Fuzzy Control for Cyclic Switched Stochastic Nonlinear Systems With Actuator Faults

Secure Adaptive Event-Triggered Control for Cyber-Physical Power Systems Under Denial-of-Service Attacks.

Secure control for cyber-physical power systems (CPPSs) under cyber attacks is a challenging issue. Existing event-triggered control schemes are generally difficult to mitigate the impact of cyber attacks and improve communication efficiency simultaneously. To solve such two problems, this article studies secure adaptive event-triggered control for the CPPSs under energy-limited denial-of-service (DoS) attacks. A new DoS-dependent secure adaptive event-triggered mechanism (SAETM) is developed, where DoS attacks are taken into account when designing the trigger mechanisms. Sufficient conditions are derived to ensure the CPPSs to be uniformly ultimate boundedness stable, and the entering time when the state trajectories of the CPPSs are guaranteed to stay in the secure region is also given. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed control method.

Neural-network-based adaptive control of strict-feedback nonlinear systems with actuator faults: Event-triggered communications strategy

This article considers the neural network-based event-triggered adaptive control problem for a class of strict-feedback nonlinear impulsive systems with actuator faults and external disturbances. It is well known that reducing computational resources and carrier loads is a long-standing task in the field of adaptive control. However, the event-triggered scheme used in traditional research still consumes relatively more resources. To solve the problem, the control scheme in this paper only updates the controller and weight parameters at the event-triggered moments. Dedicated efforts are made to certificate the stability of the system through a new Lyapunov theory, in such a way that the system maintains its stability well even under uncertainties. Finally, two emulation examples are exploited to demonstrate the efficacy and applicability of the proposed approach.

Event-Triggered Adaptive Fuzzy Finite-Time Control for Steer-by-Wire Vehicle Systems

Event-Triggered Adaptive Fuzzy Finite-Time Control for Steer-by-Wire Vehicle Systems

Fixed-time adaptive event-triggered control for multiagent systems with full-state constraints

This paper investigates the fixed-time adaptive event-triggered control problem for a class of nonlinear multiagent systems with full-state constraints. The problem of full-state constraints is solved for multiagent systems by utilising a one-to-one nonlinear mapping. The design conditions of the controllers are more easily satisfied than the existing barrier Lyapunov functions strategy. A fixed-time adaptive control scheme is proposed for unconstrained systems that are converted from constrained systems. In contract with the existing finite-time control schemes that rely on the initial states, this restriction condition can be removed in this paper. An improved event-triggered mechanism with an adaptable switching threshold is designed to provide flexible switching between fixed threshold and variable threshold policies, resulting in reduced execution and sampling. It is proved via the Lyapunov stability method that all signals of the closed-loop systems are semi-global practical fixed-time stable. Finally, the effectiveness of the proposed control strategy is verified by some simulation results.

Adaptive terminal sliding mode control for USV-ROVs formation under deceptive attacks

This work investigates the cooperative formation control problem of unmanned surface vehicle-remotely operated vehicles (USV-ROVs) subject to uncertainties under deceptive attacks. In the control design, with the utilization of the desired formation as well as the geometric position between USV and ROVs, a geometric transformation approach is developed and a geometric constraint relationship of governing formation positions is derived. Under the terminal sliding mode control (TSMC) design framework, a novel terminal sliding surface is crafted to circumvent the singularity issue. To further bolster robustness, using the sliding mode damper concept, a variable damping reaching law is devised. To refrain from the effectiveness of attacks and uncertainties, the adaptive technique is integrated into the TSMC framework. To save the communication resources, an event-triggering mechanism is established between the distributed controller and ROVs. Then, an event-triggered adaptive finite-time cooperative formation control scheme is developed for the USV-ROVs. The Lyapunov theory analysis shows that the cooperative formation control issue of USV-ROVs is realized and the deceptive attack can be suppressed efficaciously. The simulation, comparison, and quantitative analysis demonstrate the relative effectiveness and superiority of the developed scheme.

A Multifilters Approach to Adaptive Event-Triggered Control of Uncertain Nonlinear Systems With Global Output Constraint.

This article focuses on the problem of adaptive event-triggered output feedback control for a class of uncertain nonlinear systems under the output constraint. Different from the existing works, the time-varying parameters and the global output constraint are taken into account. First, by means of multifilters, the unmeasurable state variables are reconstructed, under which the unknown time-varying parameters and sensor sensitivity are transformed into the estimation problem of unknown parameters. Second, based on a barrier function, a novel constraint algorithm is established to make the output enter into asymmetric time-varying constraint boundaries, which is independent of the initial value of the output. To avoid continuous sampling of the controller, an event-triggered mechanism is proposed without the Zeno phenomenon. By means of the Lyapunov stability theory, it is strictly proved that the output enters into the pregiven asymmetric constraint boundaries, and never exceeds. Finally, the validity of our proposed control algorithm is illustrated by a numerical simulation.

Event-Triggered Adaptive Finite-Time Containment Control for Fractional-Order Nonlinear Multiagent Systems.

This article investigates the finite-time containment control problem for fractional-order nonlinear multiagent systems (FOMASs) with event-triggered inputs. First, a new Lyapunov stability lemma is developed, which provides a basic approach for the completely unknown nonlinear fractional-order system to realize finite-time convergence. Considering the containment control for FOMASs, the restricted assumption that the derivatives of leaders' trajectories are known to the followers is removed, and only the boundedness of derivatives is required in this article, whose upper bound need not be known. To reduce the burden of communication, an event-triggered condition consisting of the control input and a decreasing function related to the containment errors is devised, which can provide more design freedom to balance the system performance and communication resources. According to the proposed fractional-order finite-time convergence lemma, a distributed adaptive containment control scheme is developed, such that each follower can be steered to the convex hull spanned by the leaders in finite time. Simulation examples further demonstrate the effectiveness of our proposed method.

Data-Driven Event-Triggered Adaptive Dynamic Programming Control for Nonlinear Systems With Input Saturation.

This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

Formation control of UAV–USV based on distributed event-triggered adaptive MPC with virtual trajectory restriction

This paper studies the formation and trajectory tracking problem of unmanned aerial vehicle-unmanned surface vehicle (UAV–USV). A distributed event-triggered adaptive model predictive control (DEAMPC) method is constructed for the formation control by using information of the adjacent vehicles. First, the preset formation equation and parameter change are incorporated into the construction process of the cost function to achieve the formation control. Second, in order to reduce the calculation times of the optimization problem, an event-triggered mechanism is proposed. Finally, the virtual trajectory restriction is designed to deal with the condition of interrupted communication network. Moreover, some simulation results are given to illustrate the advantages and effectiveness of all the above methods.

Adaptive Event-triggered Cooperative Tracking Control Under Full-state Constraints Based on Nonlinear Time-varying Multi-agent Systems

Adaptive Event-triggered Cooperative Tracking Control Under Full-state Constraints Based on Nonlinear Time-varying Multi-agent Systems

Event-triggered adaptive neural fault-tolerant control for a 2-DOF helicopter system with prescribed performance

In this paper, we focus on an event-triggered adaptive neural fault-tolerant control for a two-degree of freedom (2-DOF) helicopter system with actuator failures. In this design, a radial basis function neural network (NN) is exploited to estimate the uncertainty terms present in the system. A new error transformation technique is employed to make the tracking error of the system satisfy a prescribed performance function. Then, an event triggering mechanism is introduced to reduce the communication burden of the system. Smoothing functions and bounded estimation methods are then used to compensate for actuator failures and measurement errors. The helicopter system is proved to be consistently bounded by the direct method of Lyapunov functions. Finally, simulation and experimental results verify the effectiveness of the control strategy.

Event-triggered adaptive control for nonlinear systems using time-receding horizons without initial dependence

This paper investigates the full-state constraint event-triggered adaptive control for a class of uncertain strict-feedback systems. The lack of information on the coupling dynamics of virtual variables in backstepping increases the complexity of feedback design. Given this, the requirements of shaping system performance constraints, eliminating initial dependence, and reducing data transfer costs together give rise to an interesting and challenging problem. Constructing the time-receding horizon (TRH) and stitching it with the quadratic Lyapunov function (QLF) is the key to constrained tracking. Specifying TRHs as a set of smooth bounds with fixed-time convergence and forcing the system to stabilize within the constrained region before the prescribed settling time provide a sufficient condition for practical finite-time stability (PFS). For relaxing the initial dependence, a tuning function is designed to match the performance constraints under arbitrary system initial conditions. A dual-channel event-triggered mechanism (ETM) is developed to automatically adjust the controller and estimator data flow updates with less transmission burden. By combining a specific inequality with backstepping, uncertainties are overcome without the “complexity explosion” in recursion steps. Finally, simulations demonstrate the effectiveness of the proposed method.

Event-triggered adaptive leaderless consensus control for nonlinear multi-agent systems with unknown dead-zones and output constraints

This article investigates the event-triggered adaptive control problem for a class of leaderless multi-agent systems with output constraints and unknown dead-zones. The adaptive updating laws are designed to eliminate the effects of unknown dead-zones by employing adaptive backstepping technique. The barrier Lyapunov function (BLF) is introduced to guarantee that the output constraints are not violated. Then, a generalized relative threshold is proposed by utilizing the neighboring controller errors to alleviate the computation burden dramatically. It is further shown that all the closed-loop signals are uniformly bounded and that the consensus tracking errors asymptotically tend to zero. A simulation example verifies the validation of the developed control strategy.

Event-triggered adaptive pose tracking control of rigid cellularized spacecraft based on twistors

Event-triggered adaptive pose tracking control of rigid cellularized spacecraft based on twistors

Event-triggered adaptive command-filtered trajectory tracking control for underactuated surface vessels based on multivariate finite-time disturbance observer under actuator faults and input saturation

This paper is aiming at enabling the underactuated surface vessels (USVs) to complete the tracking task with high precision and fast convergence under the influence of unknown external interference, dynamic uncertainty, input saturation, limited communication resources, and actuator failure. Specifically, a trajectory tracking control scheme is designed using virtual control switching, robust self-adaptation, finite-time, event-triggered, and disturbance compensation techniques. The norm calculation is performed on the lateral and longitudinal errors of the underactuated USVs, and the virtual guidance direction of the system is obtained through virtual control conversion. The hyperbolic tangent function is introduced and combined with adaptive technology to compensate the dynamic uncertainty of the system. Through the multivariate finite-time disturbance observer (MFTDO), the unknown disturbance and the bias fault factor of the system are compensated. The tracking performance of the system is further improved using the finite-time technology and combined with the event-triggered technology to reduce the update frequency of the controller signal. Using Lyapunov stability theory, a detailed stability analysis is provided for the control scheme. Finally, the effectiveness of the control design scheme is verified by simulation.

Event-triggered finite-time neural control for uncertain nonlinear systems with unknown disturbances and its application in SVC

In this article, an event-triggered finite-time neural control strategy is proposed for nonlinear power systems with unknown disturbances and static var compensator (SVC). We first transform the power system with SVC into a three-dimensional uncertain nonlinear system and then extend it to an [Formula: see text]-dimensional uncertain nonlinear system. The disturbance observer is established to estimate external disturbances and the unknown nonlinear terms are approximated by the radial basis function neural networks. Moreover, to avoid the complexity explosion problem in the traditional backstepping method, the command filtering technique is adopted, and the error caused by the command filters is compensated. The adaptive event-triggered finite-time controller ensures that all signals are bounded in finite time and excludes Zeno phenomena. In the end, the simulation for the two-area interconnected power system with SVC is presented to verify the availability and feasibility of the proposed approach.

Event-Triggered Adaptive Control for a Class of Nonlinear Systems with Dead-Zone Input

In this paper, the event-triggered control problem is investigated using backstepping techniques for nonlinear systems with dead-zone input. The external disturbance and unknown parameters are also considered in the controller’s design. It is well known that errors in input signal measurements are inevitable. In event-triggered control, such errors will directly affect whether the control signal is updated. This measurement error can be seen in the form of interference to the threshold. Therefore, unlike traditional event-triggered control, the existence of threshold disturbance is considered in the controller’s design. The proposed controller can not only compensate for the uncertainties caused by external disturbance and unknown parameters but can also suppress the unknown effects caused by threshold interference. In addition, to obtain a continuous controller, a smooth function is constructed to approximate the discontinuous sign function. In this way, Zeno behavior is successfully avoided. The boundedness of all signals and the tracking performance of the system can be guaranteed by the proposed control scheme. Numerical simulation and actual system simulation demonstrate the effectiveness of the proposed control scheme. The comparative simulation results also verify this event-triggered controller’s advantages, including better tracking performance and fewer trigger times.

Event-Triggered Adaptive Neural Impedance Control of Robotic Systems.

This article presents an event-triggered adaptive neural impedance control (ETANIC) scheme for robotic systems, where the combination of impedance control (IC) and event-triggered mechanism can significantly reduce the computational burden and the communication cost under the premise of ensuring the stability and tracking performances of the robotic systems. The IC is used to achieve the compliant behavior of the robotic systems in response to the environment. The uncertainties of the robotic systems are estimated by the radial basis function neural network (RBFNN), and the update laws for RBFNN are derived from the designed Lyapunov function. The stability of the whole closed-loop control system is analyzed by the Lyapunov theory, and the event-triggered conditions are designed to avoid the Zeno behavior. The numerical simulation and experimental tests demonstrate that the proposed ETANIC scheme can achieve better efficiency for controlling the robotic systems to perform the interaction tasks with the environment in comparison to the adaptive neural IC (ANIC).