Model-Based Edge-Event-Triggered Containment Control Under Directed Topologies

Asymptotic adaptive output feedback event-triggered control of uncertain strict-feedback nonlinear systems with sensors triggering

In this paper, event-triggered output feedback control of a class of high-order nonlinear strict-feedback systems with parametric uncertainties is investigated, in which both of the controller and the parameter estimator are triggered based on a set of event-triggered conditions. Firstly a new one-step control design framework is proposed for the strict-feedback nonlinear systems, therefore both expressions of the controller and the parameter estimate laws are much more simple than those of the recursive design approaches such as backstepping control. Secondly observers are designed to estimate the unknown states, and a set of event-triggering mechanism is proposed for the sensors such that the states are transmitted through the communication network only at the triggering points. The estimated parameter is obtained without real-time integration due to the event-triggered estimator. It is proved that our proposed control law guarantees the closed-loop system is globally bounded and the system output converges to zero asymptotically. It is also proved that the Zeno behavior is excluded. Simulation results demonstrate the effectiveness of the proposed control scheme.

The Bipartite Edge-Based Event-Triggered Output Tracking of Heterogeneous Linear Multiagent Systems

This article focuses on the bipartite output tracking control for heterogeneous linear multiagent systems under the asynchronous edge-based event-triggered transmission mechanism. First, the distributed bipartite edge-based event-triggered compensator is established to estimate the state of the exosystem. The estimated state of the compensator is the same as the state of the exosystem in modulus and opposite in sign because of the existence of antagonistic communications. To be independent of the topology information, the adaptive compensator with an edge-based event-triggered mechanism is then established. And the observer is proposed to recover the unmeasurable system states. Then, the distributed control scheme based on the compensator and the observer is designed to address the bipartite output tracking problem. Moreover, the results in the signed fixed graph are extended to signed switching graphs. The Zeno behavior of each edge is ruled out. Finally, two numerical examples, one application example and one comparison example, are given to demonstrate the feasibility of the main theoretical findings.

Adaptive model-based dynamic event-triggered output feedback control of a robotic manipulator with disturbance

This paper focuses on the stable tracking control of the manipulator with constrained communication, unmeasurable velocity, and nonlinear uncertainties. An NN observer-depended output feedback scheme in the discrete-time domain is developed by virtue of the model-based dynamic event-triggered backstepping technique in the channel of sensor to controller. For generalizing the zero-order-holder (ZOH) implementation, a plant model is built to approximate the triggered states in the time flow, and according to which, the control law is fabricated. Based on model-based error events, we construct a dead-zone triggered condition with a dynamically adjustable threshold, making the threshold evolve with the system performance, to achieve flexible communication scheduling and avoid the accumulation of triggers in small tracking errors. The internal and external nonlinear uncertainties are online compensated by the neural network, and the aperiodic adaptive law is derived in the sense of control stability to save the computation. Finally, the conditions for semi-global ultimate uniform bounded (SGUUB) of all variables are given via impulse Lyapunov analysis, and a positive lower bound in the time interval between consecutive executions to guarantee the Zeno free behavior is obtained. Simulations are conducted on a three-link manipulator to illustrate the effectiveness of our method.

Distributed Adaptive Fuzzy Event-Triggered Containment Control of Nonlinear Strict-Feedback Systems

In this paper, the adaptive fuzzy event-triggered containment control problem is addressed for uncertain nonlinear strict-feedback systems guided by multiple leaders. A novel distributed adaptive fuzzy event-triggered containment control is designed only using the information of the individual follower and its neighbors. Moreover, a distributed event-trigger condition with an adjustable threshold is developed simultaneously. The designed containment control law is updated in an aperiodic manner, only when event-triggered errors exceed tolerable thresholds. It is proved that the uniformly ultimately bounded containment control can be achieved, and there is no Zeno behavior exhibited by applying the proposed control scheme. Simulation studies are outlined to illustrate the effectiveness of the theoretical results and the advantages of the event-triggered containment control proposed in this paper.

DDS-Based Containment Control of Multiple UAV Systems

In this paper, we present a robust containment control design for multi Unmanned Aerial Vehicle Systems (UAVs) based on the Data Distribution Service (DDS) middleware and L 1 adaptive controller. The Data Distribution Service middleware, L 1 adaptive controller and graph theory technique are utilized for the navigation of the UAVs. The L 1 controller is utilized as a local controller for each UAVs and the graph theory approach is utilized to constitute the followers inside their leaders. Finally, the DDS Middleware is used to exchange data between the followers and their leaders. Robust adaptation of the L 1 controller makes the system robust with a high level of performance. Matlab simulation verified the robustness of the L 1 controller. We provide stability proofs using Lyapunov analysis for the UAVs framework.