This article investigates the distributed optimal output consensus problem of heterogeneous linear multiagent systems over weight-unbalanced directed networks. A novel distributed continuous-time state feedback controller is proposed to steer the outputs of all the agents to converge to the optimal solution of the global cost function. Under the standard condition that the unbalanced digraph is strongly connected and the local cost functions are strongly convex with global Lipschitz gradients, the exponential convergence of the closed-loop multiagent system is established. Then, the proposed state feedback control law is extended to an observer-based output feedback setting. Two examples are finally provided to illustrate the effectiveness of the proposed control schemes.

This paper studies the distributed continuous-time nonconvex optimization problem of multi-agent systems over unbalanced digraphs. Each agent is endowed with a local cost function, which is privately known to the agent but not necessarily convex. We aim to drive all the agents to cooperatively converge to the optimal solution of the sum of all local cost functions. Based on the adaptive control approach, a fully distributed algorithm is developed for each agent in the case that neither prior global information concerning network connectivity nor convexity of local cost functions is available. A key feature of the algorithm is that it removes the dependence on the smallest strong convexity constant of local cost functions, and the left eigenvector corresponding to the zero eigenvalue of the Laplacian matrix of unbalanced digraphs.

Insulator visual aiming is difficult for washing drone due to the complex washing environment, strong dis-turbance, lack of debugging environment, and other factors. Conventional visual servo control methods often fail to consider these complex factors adequately and fall short in reliable insulator visual aiming. To address these problems, we propose a novel multi-feature fusion-based drone visual servo control method for accurate insulator visual aiming. A multi-feature fusion neural network (MFFNet) is proposed to map the dif-ferent input modalities into an embedding space spanned by the learned deep features. Suitable control commands are generated by the simple combination of learned deep features. These deep features represent the intrinsic structural properties of the insulator and the motion pattern of the drones. Particularly, our method is trained purely in simulation and transferred to a real drone directly. Moreover, accurate visual aiming is guaranteed even in strong disturbance environments. Simulation and experimental results verify the high accurate insulator aiming, anti-disturbance, and sim-to-real transfer capabilities of the proposed method. Video: https://youtu.be/Ptlajzvp46A.

Disturbance mainly caused by recoil force in-evitably makes washing drone seriously deviate from the desired position, thereby reducing the cleaning efficiency. It is neces-sary to develop an effective anti-disturbance control method. Although some progresses have been made, the position error thereof is still large, rendering existing methods inapplicable in washing drone. In this paper, we propose a learning-based anti-disturbance control (LADC) method to significantly reduce the position error by combining robust nonlinear control and partial differential equation network (PDENet). Taking data noise into account, we use differential spectral normalization in the training of the PDENet. A distinguishing feature of our method is to directly learn PDENet parameters from flight logs without installing extra sensors. Experimental results indicate that the proposed method outperforms classical PD method and extended state observer (ESO) based control method with 70 % and 50 % reduced position error, respectively, and can be further applied in variable scenarios. Video: https: / / youtu.be/gNfLFAXalkI

When multiple quadrotors fly in a cluttered environment, collision-free flight must be assured. In this paper, we propose a novel elastic safety clearance based model predictive control (ESC-MPC) for multiple maneuverable quadrotors to avoid collisions in the presence of disturbance. This is accomplished through leveraging tube based model predictive control to maintain the quadrotor in a tube of trajectories. Exponential control barrier function (ECBF) is integrated to realize the elastic safety clearance mechanism which offers a dynamic safety margin in maneuverable flight. We validate the superiority of our approach with laboratory experiments.

In this paper, we study the distributed Nash equilibrium (NE) seeking problem for a class of aggregative games with players described by uncertain perturbed nonlinear dynamics. To seek the NE, each player needs to construct a distributed algorithm based on information of its cost function and the exchanging information obtained from its neighbors. By combining the internal model principle and the average consensus technique, we propose a distributed gradient-based algorithm for the players. This paper not only assures the NE seeking of aggregative games but also achieves the disturbance rejection of external disturbances.

In this paper, we present a novel universal adaptive output feedback control for the cooperative output regulation problem of linear multi-agent systems. The agent plant is general in the sense that those interesting practical issues such as unknown dynamics, direct network graph, and nonidentical relative degree agents are taken into account simultaneously. The obtained controller consists of the reduced-order observer, the internal model, as well as the adaptive control law, all of which are shown to be fully distributed. It is of interest to see that, in our recursive design, each agent does not need to estimate the unknown parameters caused by the neighbors, giving rise to a merely two-dimensional adaptive law with quite a simple structure. It is also this feature that our design has the capability of handling the agents with arbitrary nonidentical high relative degrees.

In this paper, a distributed optimization problem is studied for continuous-time multiagent systems with unknown-frequency disturbances. A distributed gradient-based control is proposed for the agents to achieve the optimal consensus with estimating unknown frequencies and rejecting the bounded disturbance in the semi-global sense. Based on convex optimization analysis and adaptive internal model approach, the exact optimization solution can be obtained for the multiagent system disturbed by exogenous disturbances with uncertain parameters.

The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.