This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We first propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work studying event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities, which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information, and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics that include the vector of gravitational potential forces, an adaptive algorithm is proposed. This requires more information about the agent dynamics but allows for the estimation of uncertain parameters associated with the agent self-dynamics. For each algorithm, a trigger function is proposed to govern the event update times. The controller is only updated at each event, which ensures that the control input is piecewise constant and thus saves energy resources. We analyze each controller and trigger function to exclude Zeno behavior.

Signed networks are widely applied to describe cooperative and competitive interaction relationships arising from various social, biological and physical systems. In this paper, a cooperation-competition evolutionary dynamic model is offered to explain the gradual evolution of the interaction relationship between neighboring nodes in signed networks. The core feature of our model is to portray the interaction relationship between neighboring nodes as a nonlinear function of the state difference, i.e., neighboring nodes cooperate with each other only if their state difference is within a limited range (called cooperation range ); otherwise they antagonize with each other. We investigate the leader-follower dynamics, and present the lower bound of the convergence rate by using the infinite norm of products of sub-stochastic matrices. For the leaderless dynamics, we establish algebraic conditions with respect to the state differences and the cooperation range for realizing consensus and polarization behaviors. Finally, numerical examples are provided to illustrate the evolution dynamics.

Visual servoing of Unmanned Aerial Vehicles (UAVs) has achieved satisfactory performance in fixed and planar motion targets. Due to highly coupled system dynamics and the sensitivity of the target image to aircraft attitude, the problem for chasing free-flying targets remains challenging. In this paper, a vision-based algorithm is designed for controlling an UAV while tracking an intruder flying arbitrarily in 3D space. Image-based visual servoing is used to design controllers that depend directly on errors in image plane. Specifically, a virtual camera approach is adopted to decouple the UAV dynamics by compensating the pitch and yaw, and an improved image error term is proposed to reduce the impact of the UAV rotation on error signals in the process of tracking, thus a simplified control design is achieved, and the stability of the visual servo system is guaranteed. Comparison and ablation experiments in both simulated and real environments are provided to verify the effectiveness of the proposed method.

Mapless navigation is a popular approach for guiding a robot in an unknown environment. However, current learning-based methods for mapless navigation cannot guarantee safe exploration during training. In this article, we aim to safely train a mapless navigation policy with 2-D LiDAR inputs using imitation learning (IL). We propose a novel IL training scheme based on the dataset aggregation with extra safety enhancement. In order to avoid collisions during training, we design a controlled exploration strategy for demonstration data collection. In this strategy, the output of the novice and expert policy are fused adaptively to control the robot. In order to improve the obstacle avoidance capability of the trained policy, we design a safety-enhanced loss function for network training. This proposed loss function focuses on the obstacle avoidance by prioritizing the training samples collected in different states. In addition, inspired by the symmetrical pattern of the navigation task, we propose a new data augmentation technique, which increases the training speed significantly. In both simulation and real-world experiments, our method demonstrates the remarkable performance in terms of training safety and navigation success rate compared with other IL-based approaches.

This article is concerned with the output feedback security control of a class of high-order nonlinear-interconnected systems with denial-of-service (DoS) attacks, nonlinear dynamics, and exogenous disturbances. First, extreme learning machine (ELM) and adaptive techniques are adopted to approximate the unknown nonlinearities. Then, novel adaptive ELM-based nonlinear state observers with adaptive compensation functions are developed to estimate the unmeasurable states during DoS attacks under the influence of the disturbances. Further, by combining with the backstepping control and filtering techniques, adaptive ELM-based controllers are proposed to achieve uniformly ultimately bounded results based on the observation and adaption control signals under the influence of DoS attacks, nonlinear dynamics, and exogenous disturbances. Comparative studies are carried out to validate the effectiveness of the developed ELM-based adaptive observation and control strategies for two interconnected power systems.

This paper revisits the circular formation control problem proposed in [20], for multiple nonholonomic mobile robots with non-identical constant forward speeds. Specifically, we study two types of circular formations: 1) a circular motion with synchronized/balanced phase configuration; 2) a concentric circular formation with both circular orbits control and phase synchronization/balancing. Existing works on the above problems utilize fixed-gain control input, which increases the workload of the engineers for selecting favourable algorithm parameters. To reduce the corresponding workload, we study the above problems from a new perspective by utilizing optimization methods, based on which two variable-gain control algorithms are proposed such that much greater flexibility on the selection of algorithm parameters is acquired. The global convergence properties of the proposed algorithms are analyzed under some mild assumptions on the proposed objective functions. Both simulations and field experiments are presented to validate the effectiveness of the proposed formation algorithms.