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In this article, we propose an approximate optimal control strategy for a class of nonlinear multiagent system pursuit–evasion games. Herein, multiple pursuers aim to capture multiple evaders trying to evade capture. Under the competitive framework, agents not only pursue their individual goals but also consider coordination with their teammates to achieve collective objectives. However, maintaining cohesion with teammates in existing distributed control methods has always been a challenge. To enhance team coordination, we employ a graph-theoretic approach to represent the relationships between agents. Based on this, we design a dynamic target graph algorithm to enhance the coordination among pursuers. The approximate optimal strategies for each agent are solved by utilizing the Hamilton–Jacobi–Isaacs equations of the system. As solving these equations becomes computationally intensive in multiagent scenarios, we propose a value-based single network adaptive critic network architecture. In addition, we consider scenarios where the numbers of agents on both sides are inconsistent and address the phenomenon of input saturation. Moreover, we provide sufficient conditions to prove the system's stability. Finally, simulations conducted in two representative scenarios, multiple-pursuer-one-evader and multiple-pursuer-multiple-evader, demonstrate the effectiveness of our proposed algorithm.

This article focuses on the singularity-free predefined time control design problem of the quantized nonstrict feedback (NSF) nonlinear systems. Radial basis function neural networks (NNs) are introduced to model the unknown nonlinear dynamics. With the property of the NN basis function, the algebraic loop problem posed by the NSF control structures is addressed. The input quantization is addressed by using the nonlinear decomposition technique. A nonlinear filter with predefined time stability is constructed to decrease the computational complexity. Further, by introducing the designed predefined time filter into the backstepping recursive framework, a predefined time dynamic surface control algorithm is developed, in which the improved adding power integration technique is introduced to prevent control singularities. The Lyapunov theory demonstrates the predefined time stability of the closed-loop quantized nonlinear systems. By the developed control algorithm, the controlled system can effectively track the specified command signal. Both the tracking and filtering errors can reach a small neighborhood around zero within the preset time. The validity and superiority of the proposed control algorithm are illustrated by an actual system simulation.

In this article, we propose bionic swarm control based on second-order communication topology (SOCT) inspired by the migration of birds, which solves the difficulty in constructing communication topologies and high-computational complexity in controlling large-scale swarm systems. To realize bionic swarm control, there are three problems supposed to be solved. First, the adjacency matrix and the Laplacian matrix in traditional methods cannot be applied to SOCT directly, which should be redesigned. Second, sub-swarm systems formed based on 2-order communication topology (2-OCT) and independently distributed with each other also need to be put forward to reduce computational complexity. At last, the followers in 1-order communication topology (1-OCT) are set as the leaders of sub-swarm systems in 2-OCT. As a result, coupling in large-scale swarm systems would be reduced. The bionic swarm controller is designed through the backstepping method. In this case, the stability of bionic swarm controller is proven by the designed Lyapunov function. The simulations show the efficiency of the designed bionic swarm controller. And the tracking-containment control based on SOCT with 42 swarm members is realized.