This paper investigates the adaptive event-triggered control problem for aclass of nonlinear systems subject to periodic disturbances. To reduce thecommunication burden, a reliable relative threshold strategy is proposed.Fourier series expansion and radial basis function neural network arecombined into a function approximator to model suitable time-varyingdisturbed function of known periods in strict-feedback systems. By combiningthe Lyapunov stability theory and the backstepping technique, the proposedadaptive control approach ensures that all the signals in the closed-loopsystem are bounded, and the tracking error can be regulated to a compact setaround zero in finite time. Finally, simulation resultsare presented to verify the effectiveness of the theoretical results.

Adaptive event-triggered control for a class of nonlinear systemswith periodic disturbances

An adaptive fuzzy control strategy is proposed for a single-link flexible-joint robotic manipulator (SFRM) with prescribed performance, in which the unknown nonlinearity is identified by adopting the fuzzy-logic system. By designing a performance function, the transient performance of the control system is guaranteed. To stabilize the SFRM, a dynamic signal is applied to handle the unmodeled dynamics. To cut down the communication load of the channel, the event-triggered control law is developed based on the switching threshold strategy. The Lyapunov stability theory and backstepping technique are applied coordinately to design the control strategy. The semiglobally ultimately uniformly boundedness can be ensured for all signals in the closed-loop system. The designed control method can also guarantee that the tracking error can converge to a small neighborhood of zero within the prescribed performance boundaries. At the end of the article, two illustrative examples are shown to validate the designed event-triggered controller.

Adaptive Prescribed Performance Control of A Flexible-Joint Robotic Manipulator With Dynamic Uncertainties.

Deception attacks on event-triggered distributed consensus estimation for nonlinear systems

This article is concerned with the dynamic event-triggered-based adaptive output-feedback tracking control problem of nonlinear multiagent systems with time-varying input delay. By utilizing the approximation capability of neural network (NN), a low-gain nonlinear observer is first established to estimate the immeasurable states. To mitigate the effect of time-varying input delay, an auxiliary system with communication information is designed to generate the compensation signals. Then, a distributed adaptive composite NN dynamic surface control (DSC) strategy is proposed to acquire the satisfactory tracking accuracy, where the filter errors are compensated by the introduced serial-parallel estimation model. Moreover, an effective switching dynamic event-triggered mechanism is developed to determine the communication instants and reduce the update frequency of the controller. It is proven that the consensus tracking error converges to a residual set of the origin. Finally, simulation results are presented to demonstrate the effectiveness of the proposed composite NN DSC scheme. 

Observer-Based Dynamic Event-Triggered Control for Multiagent Systems With Time-Varying Delay

This paper considers the human-in-the-Ioop leader-following consensus control problem of multi-agent systems (MASs) with unknown matched nonlinear functions and actuator faults. It is assumed that a human operator controls the MASs via sending the command signal to a non-autonomous leader which generates the desired trajectory. Moreover, the leader's input is nonzero and not available to all followers. By using neural networks and fault estimators to approximate unknown nonlinear dynamics and identify the actuator faults, respectively, the neighborhood observer-based neural fault-tolerant controller with dynamic coupling gains is designed. It is proved that the state of each follower can synchronize with the leader's state under a directed graph and all signals in the closed-loop system are guaranteed to be cooperatively uniformly ultimately bounded. Finally, simulation results are presented for verifying the effectiveness of the proposed control method. 

Human-in-the-Loop Consensus Control for Nonlinear Multi-Agent Systems With Actuator Faults

This article considers the Nussbaum gain adaptive control issue for a type of nonlinear systems, in which some sophisticated and challenging problems, such as periodic disturbances, dead zone output, and unknown control direction are addressed. The Fourier series expansion and radial basis function neural network are incorporated into a function approximator to model time-varying-disturbed function with a known period in nonlinear systems. To deal with the problems of the dead zone output and unknown control direction, the Nussbaum-type function is recommended in the design of the control algorithm. Applying the Lyapunov stability theory and backstepping technique, the proposed control strategy ensures that the tracking error is pulled back to a small neighborhood of origin and all closed-loop signals are bounded. Finally, simulation results are presented to show the availability and validity of the analysis approach.

Approximation-Based Nussbaum Gain Adaptive Control of Nonlinear Systems With Periodic Disturbances

The state estimation problem of heterogeneous multi-agent systems with random transport protocol is investigated in this paper. Due to the dependency of the agent dynamics and the random sparse structure induced by the random transport protocol, the optimal state estimation design becomes complex and challenging. An optimal state estimator is successfully designed by the Hadamard product and gradient method. Based on the analysis of the matrix functions, a sufficient condition is established to guarantee that the average estimate error covariance is limited. Finally, a numerical example and a smart grid model are utilized to demonstrate the effectiveness of the deigned estimator.

An Optimal Estimation Framework of Multi-Agent Systems With Random Transport Protocol

A disturbance observer based intelligent control for nonstrict-feedback nonlinear systems

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Observer-Based Neural Control of N -Link Flexible-Joint Robots.

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