This paper studies the distributed time-varying convex optimization problem for a class of nonlinear multiagent systems. An auxiliary system is first designed to estimate the global optimal state and its high-order derivatives by means of a distributed algorithm. Relying on this auxiliary system, the solution of the distributed time-varying convex optimization problem is converted into that of a tracking problem for a class of nonlinear systems. A tracking control scheme is then proposed based on the back-stepping strategy. Simulations are provided to validate the theoretical results.

In this article, a class of distributed quadratic games is considered over an undirected graph. The issue on the communication topology restriction is introduced and the players’ dynamics are with nonlinear dynamics and unknown time-varying perturbation. A distributed Nash equilibrium seeking algorithm is proposed based on a high-gain observer method, and the convergence is analyzed by the Lyapunov stability theory. It is shown that each player estimates the rival players’ states, and the errors between the players’ states and the Nash equilibrium are ultimately bounded by a small bound. Moreover, the presented algorithm is free of chattering phenomena because it is designed using the hyperbolic tangent function instead of the signum function to dominant the perturbation. The effectiveness of the proposed algorithm is validated via a simulation of the oligopoly game in which five firms produce the same products in a duopoly market structure.

This article studies a distributed constrained optimization problem of multiple agents characterized by Euler-Lagrange systems with unknown inertial parameters. The optimization objective is to compute a feasible solution within the intersection of a series of constrained sets such that a global payoff function summed by a group of local ones is minimized. Meanwhile, each local payoff function and constrained set are just privately available to their respective agent. To accomplish the concerned constrained optimization objective, a fully distributed continuous-time algorithm resorting to a projection-based auxiliary dynamics is synthesized without using global topology information. The proposed distributed optimization algorithm is privacy-preserving in the sense that no actual state information is exchanged between distinct agents during the seeking progress. Besides, the associated stability analysis is carried out in terms of the Lyapunov theorem. Finally, a source localization example is performed to verify the effectiveness of the proposed distributed optimization algorithm.

The time-varying economic dispatch over a network is considered where both the cost function and the equality constraint are time-varying. A distributed algorithm is first designed combing the prediction-correction framework and the consensus + innovations approach. In the prediction steps, the sensitivity analysis method is introduced to calculate the shift between the estimated and the exact optimization solution of the correction steps. Afterwards, the overall optimal errors in two steps are calculated by the sub-optimal analysis method. The convergence analysis shows that the dispatch errors are bounded and the convergence process is Q-linear. A dynamic prediction-correction algorithm is then presented to improve the accuracy at every time instant. Finally, numerical examples using the IEEE 118-bus system are presented to illustrate the validity of the algorithms.

In this article, a class of distributed nonlinear placement problems is considered for a multicluster system. The task is to determine the positions of the agents in each cluster subject to the constraints on agent positions and the network topology. In particular, the agents in each cluster are placed to form the desired shape and minimize the sum of squares of the Euclidean lengths of the links amongst the center of each cluster and its corresponding cluster members. The problem is converted into a time-varying noncooperative game and then a distributed Nash equilibrium-seeking algorithm is designed based on a distributed observer method. A new iterative approach is employed to prove the convergence with the aid of the Lyapunov stability theorem. The effectiveness of the distributed algorithm is validated by numerical examples.