This paper presents an output feedback prescribed performance control method for a class of high-order nonlinear uncertain multiagent systems. General prescribed performance consensus control for multiagent systems requires that the initial consensus error is constraining within a boundary value. Unlike existing works, the conservative condition of prescribed performance consensus control for multiagent systems is relaxed as the consensus error is independent of the initial state. In this condition, we employ a control scheme with improved prescribed performance to constraint the transient behavior of the system. By designing reduce order dynamic gain K-filter, the state variables of the systems are reconstructed. Based on the baskstepping method and dynamic surface control technology, we introduce an innovative prescribed performance event-triggered approach aimed at ensuring prescribed performance levels for both transient and steady-state aspects of consensus control and reducing the communication bandwidth resource. Furthermore, we rigorously demonstrated through the Lyapunov function that all agents can achieve consensus with the leader driven by the controller. The simulation results have demonstrated the effective tracking performance of the developed control approach. Finally, the effectiveness and reliability of the proposed control strategy are verified through the successful execution of multi-QUAVs formation encirclement control.Note to Practitioners—This paper considers output feedback prescribed performance consensus control problem of multiagent systems, which can be applied to some practical systems, e.g., low-altitude formation flight of autonomous aerial vehicle with specified transient performance, multi underwater robot for oil field line-cruising, etc. Furthermore, in harsh working environments, the states of agent devices are difficult to measure or cannot be measured. Hence the control objective in these applications can be transformed into output feedback prescribed performance consensus control problem of multiagent systems. Besides, in military applications, high-speed aircraft need to fly at a certain height above the ground in order to avoid radar detection, which poses strong limitations on the position of the aircraft. This challenging issue can be well addressed through prescribed performance control. Compared with the existing results, this paper proposed a novel prescribed performance control strategy for multiagent systems with unrestricted initial state. It should be noted that in practical applications, communication bandwidth resources are extremely limited, so we propose event triggered control to effectively alleviate the communication pressure of multiagent system cooperative control. The nonlinear high-order system model studied in this article can be converted into a Lagrangian system, multi-complexity manipulator system and an autonomous aerial vehicle system, which has strong engineering practical significance.

This paper focuses on the cooperative control problem of multi-agent systems subject to limited energy supply, unreliable communication and malicious attacks on devices. As the advancement of network technology, Denial-of-Services attacks targeting multi-agent systems are prevalent. To detect the valid Denial-of-Services attacks, this paper designs a novel detection mechanism, which based on the voltage levels of the transceiver and receiver. Due to the state information may be more difficult to measure, the reduced order dynamic gain k-filters are designed to reconstruct the states based on only the system outputs. Then, based on the detection mechanism and estimated states, event-triggered resilient consensus controller and switched control strategy are designed, such that the multi-agent systems achieve leader-following consensus with less consumption of energy and nice resilience to Denial-of-Services attacks. Finally, a simulation example is presented to demonstrate the effectiveness of the obtained theoretical results.

The unmanned air-ground vehicle system has been successfully applied in civil and military domains. Collaborative vision-based target geolocation with this system can provide an enduring and accurate estimate of moving target state. Traditional decentralized information filter (DIF) treated each platform in the system identically. In fact, the observation capabilities of aerial and ground platform typically differ from each other due to different configurations and changing sensor noises. Without considering these differences, the resources of each platform cannot be fully utilized. To handle the issue, we develop a weighted DIF for geolocating of moving targets via air-ground collaboration. Specifically, it can produce a weighted factor autonomously for each platform based on the similarity of tracks from the air-ground system. Then, it is able to have more accurate global estimates than the traditional filter. Finally, simulation experiments and actual tests are conducted and the results are presented to validate the efficacy of the proposed method. Additional details can be seen in our video submission.

The H ∞ control method is an effective approach for attenuating the effect of disturbances on practical systems, but it is difficult to obtain the H ∞ controller due to the nonlinear Hamilton—Jacobi—Isaacs equation, even for linear systems. This study deals with the design of an H ∞ controller for linear discrete-time systems. To solve the related game algebraic Riccati equation (GARE), a novel model-free minimax Q -learning method is developed, on the basis of an offline policy iteration algorithm, which is shown to be Newton’s method for solving the GARE. The proposed minimax Q -learning method, which employs off-policy reinforcement learning, learns the optimal control policies for the controller and the disturbance online, using only the state samples generated by the implemented behavior policies. Different from existing Q -learning methods, a novel gradient-based policy improvement scheme is proposed. We prove that the minimax Q -learning method converges to the saddle solution under initially admissible control policies and an appropriate positive learning rate, provided that certain persistence of excitation (PE) conditions are satisfied. In addition, the PE conditions can be easily met by choosing appropriate behavior policies containing certain excitation noises, without causing any excitation noise bias. In the simulation study, we apply the proposed minimax Q -learning method to design an H ∞ load-frequency controller for an electrical power system generator that suffers from load disturbance, and the simulation results indicate that the obtained H ∞ load-frequency controller has good disturbance rejection performance.