Xin Jin

In this paper, we propose a new random reset rule for attitude control systems, where a new Lyapunov-Foster function implies the robust global attitude control without adding any constraints on the random reset rule, except the support domain for the involved random variables. Compared with deterministic binary logic variables, random variables introduce an automatically adjusted hysteresis half-width for each reset process, thus bring a flexible tradeoff between the hysteresis-induced inefficiency for avoiding unwinding phenomenon and the robustness to measurement noise. A benchmark case for the attitude tracking control of a miniature quadrotor prototype verifies the designed random reset rule.

In this article, event-triggered attitude consensus is considered for multiagent systems with guaranteed fixed-time convergence. Due to the non-Euclidean property of the attitude configuration space, the attitude consensus is more challenging to achieve under the sampled-data setting. An event-triggered attitude consensus protocol and event-triggered condition are proposed based on the axis–angle attitude representation. The fixed-time attitude consensus is reached if the initial attitudes lie in local regions on the attitude configuration space. The theoretical results reveal that the settling time is related to the interevent interval and the algebraic connectivity of the topology graph. We further consider the consensus protocol under a jointly connected graph, and establish the settling time estimation that depends on the switching instants. Numerical simulations are conducted to verify the validity of the theoretical results finally.

In this paper, an attitude synchronization problem with velocity-free measurements is investigated for multiple rigid body systems under event-triggered mechanism. To avoid the angular velocities in the control design, an auxiliary system is constructed for the indirect asymptotic estimation of angular velocities, which provides the necessary damping for the closed-loop system. The event-triggered attitude protocol is proposed with a clock-based triggering strategy, and the continuous communication and monitoring requirements among rigid bodies are removed. The convergence of synchronization is analyzed based on a positively invariant set contained in \mathbb{SO}(3), which inherently avoids the singularity problem or non-uniqueness issue for the attitude representation. Furthermore, based on an invariant-like principle, we extend the topology graph to a switching topology under an average dwell-time condition. A numerical simulation is conducted to show the effectiveness of the proposed control strategy.

A distributed event-triggered protocol is proposed to deal with the time-varying formation control problem of Euler–Lagrange systems with uncertain model parameters in this research. By utilizing the small-gain theorem and matrix transformation, we show the input–output stability of the closed-loop cascade system with Euler–Lagrange dynamics. An event-triggered condition (ETC) is designed by only using the local information of each agent, which avoids the continuous communication in the event detection and excludes the Zeno behavior. Finally, the proposed methods are applied to the practical flight platform for quadrotors. The experiment using three quadrotors in the outdoor environment is demonstrated to test the effectiveness of the formation protocol.

In this paper, an event-triggered Reinforcement Learning (RL) method is proposed for the optimal attitude consensus of multiple rigid body networks with unknown dynamics. Firstly, the consensus error is constructed through the attitude dynamics. According to the Bellman optimality principle, the implicit form of the optimal controller and the corresponding Hamilton-Jacobi-Bellman (HJB) equations are obtained. Because of the augmented system, the optimal controller can be obtained directly without relying on the system dynamics. Secondly, the self-triggered mechanism is applied to reduce the computing and communication burden when updating the controller. In order to address the problem that the HJB equations are difficult to solve analytically, an RL method which only requires measurement data at the event-triggered instants is proposed. For each agent, only one neural network is designed to approximate the optimal value function. Each neural network is updated only at the event-triggered instants. M eanwhile, the Uniformly Ultimately Bounded (UUB) of the closed-loop system is obtained, and Zeno behavior is also avoided. Finally, the simulation results on a multiple rigid body network demonstrate the validity of the proposed method.

This paper designs an event-triggering based communication strategy for the global attitude synchronization of a network of rigid bodies. To overcome the topological constraint on the manifold SO(3) , the quaternion-based hybrid control strategy is designed using a binary logic variable, relying on the relative measurements of adjacent rigid bodies, to determine the torque orientation. The Zeno-free distributed event-triggering strategies (ETSs) are designed combining with the reset of the binary logic variable to generate discrete communication instants, where only the corresponding parts of the control inputs are updated at those discrete instants. By assuming perfect knowledge of the rigid bodies’ dynamics and considering uncertainties and/or exogenous disturbances simultaneously, nominal and robust cases are analyzed to ensure the global attitude synchronization, respectively. The effectiveness of the main results is demonstrated by considering the attitude synchronization of six miniature quadrotor prototypes.

This paper develops a model-free approach to solve the event-triggered optimal consensus of multiple Euler-Lagrange systems (MELSs) via reinforcement learning (RL). Firstly, an augmented system is constructed by defining a pre-compensator to circumvent the dependence on system dynamics. Secondly, the Hamilton-Jacobi-Bellman (HJB) equations are applied to the deduction of the model-free event-triggered optimal controller. Thirdly, we present a policy iteration (PI) algorithm derived from RL, which converges to the optimal policy. Then, the value function of each agent is represented through a neural network to realize the PI algorithm. Moreover, the gradient descent method is used to update the neural network only at a series of discrete event-triggered instants. The specific form of the event-triggered condition is then proposed, and it is guaranteed that the closed-loop augmented system under the event-triggered mechanism is uniformly ultimately bounded (UUB). Meanwhile, the Zeno behavior is also eliminated. Finally, the validity of this approach is verified by a simulation example.

In this paper, we consider the event-triggered attitude consensus of multiple rigid-body systems. Two event-triggered attitude consensus protocols are designed under the absolute attitude and relative attitude measurement, respectively. For the first case, the gnomonic projection is utilized to project the attitude to the Euclidean plane almost globally. Then, a distributed attitude consensus protocol based on the projections is proposed under the event-triggered mechanism. By using the proposed protocol and event-triggered condition (ETC), the almost global attitude consensus is achieved on a positively invariant set. Next, in order to remove the requirement of the absolute attitude information, we propose an event-triggered attitude protocol with relative attitude measurements. The Riemannian gradient descent approach is utilized to design the attitude consensus protocol on a geodesically convex set of attitude configuration space. Moreover, to overcome the continuous monitoring in the event-detection, a self-triggered strategy is presented based on the event-triggered protocol only with the relative attitude measurement. Finally, simulation studies are conducted to verify the effectiveness of the proposed protocols.

In this paper, a twisting-based consensus algorithm is put forward to deal with the event-triggered finite-time consensus for networked Lagrangian systems with directed graphs. First, a fully distributed event-triggered finite-time protocol is considered, for which we can show that each agent can achieve the consensus after a settling time. In order to remove the requirement of continuous monitoring, a pull-based triggering mechanism is employed. Simultaneously, the Zeno-behavior can be excluded under a finite-time dynamic condition. Then, due to the advantages of non-chattering behaviors and finite-time convergence, a twisting-based consensus algorithm based on homogeneous techniques is developed to drive the Euler-Lagrange systems to the consensus value in a settling time. By means of Pólya's theorem and Sum of Squares tools, a polynomial Lyapunov function is constructed to verify our criteria. At last, we give a numerical example for 2-DOF prototype manipulators to verify the validity of the theoretical results.

In this paper, we design an event-triggering based hybrid control strategy for the time-varying attitude tracking control of the aircraft system, where the networked communication is confined with denial-of-service (DoS) attacks. The designed control strategy introduces a binary-logic-variable-based hybrid control law to overcome the topological constraint on SO(3), together with an event-triggering sampling mechanism to determine when to update the designed controller. Combined with the two kinds of flows/jumps and a general explicit characterization of frequency/duration properties for DoS attacks, we establish a general hybrid formalism for the attitude tracking control. The designed event-based hybrid control scheme renders the globally asymptotic tracking control resilient to DoS attacks. Simulation results are presented to show the effectiveness of the established hybrid control strategy.