Qing Wang

This paper addresses the three-dimensional helical guidance problem for attacking a stationary target. Different from the existing works, a three-dimensional helical guidance law is developed to cater for impact time constraints. Firstly, an analytical time-to-go estimation expression is proposed, and a vector guidance law is designed by using an inverse design method to ensure accurate time-to-go prediction. Furthermore, a helical term is incorporated to induce helical maneuvers, enhancing the system observability without compromising time-to-go estimation accuracy. To broaden applicability, a biased feedback command is augmented to accommodate diverse impact time constraints, with a rigorous convergence proof provided via Lyapunov theory. Additionally, an unmanned aerial vehicle-based pure physical system and a fixed-wing hardware-in-the-loop system are introduce to provide experimental validation. Finally, the effectiveness and reliability of the proposed guidance law are confirmed through both numerical simulations and physical experiments.

For a class of uncertain nonlinear stochastic multiagent systems, a consensus control strategy is proposed with an adjustable-time-varying-gain-based event-triggered extended state observer (ATVG-ETESO) via dual-terminal event-triggered mechanism (DTETM). The ATVG-ETESO estimates internal uncertainties and external stochastic disturbances. Its adjustable time-varying gain avoids peaking phenomenon at the initial stage and accelerates estimation error convergence. A DTETM with an adaptive threshold reduce communication burdens on both the input and output channels of the ATVG-ETESO. Theoretically, both the ATVG-ETESO estimation errors and the state consensus errors are bounded. Finally, two illustrative simulation examples are given to illustrate the effectiveness of the control strategy.

This article addresses the problem of robust time-varying output formation tracking (TVOFT) in heterogeneous multiagent systems (MASs) having multiple heterogeneous leaders under a directed topology, where both followers and leaders may exhibit distinct dynamics and dimensional properties. The objective is for the followers to track a predefined convex combination of the leaders’ outputs while simultaneously achieving an expected time-varying formation. Current related approaches often assume that each follower is either well-informed (i.e., connected to all leaders) or uninformed. This work overcomes this constraint by introducing a novel approach that employs a fully distributed observer-based scheme. First, the proposed method utilizes neighboring information and local estimations to design an adaptive observer for each follower that can estimate multiple leaders’ states with unknown inputs. Besides, to further handle the unknown external disturbances, a novel neuro-adaptive learning algorithm is proposed. Moreover, the output regulation method and the Lyapunov stability theory are used to establish robust TVOFT criteria for the closed-loop system based on the proposed observer-based control protocol. The parameters of the control protocol are derived using a structured four-step algorithm. At last, a heterogeneous experimental platform consisting of three autonomous aerial vehicles and three autonomous ground vehicles is constructed to validate the theoretical results.

Time-varying formation tracking (TVFT) control problem for multiple Euler–Lagrange (EL) systems with an uncertain leader is investigated in this article. The objective of this problem is for the followers' states to track the uncertain leader's output and achieve the desired formation. One of the major challenges in solving such a problem lies in estimating the information of the leader. In this article, an observer-based TVFT control framework is presented in this article, which does not require all or part of the follower agents to know the system dynamic knowledge of the leader agent directly in advance. First, two fully distributed adaptive observers are presented for estimating the uncertain leader's state, output, and the unknown system matrices under an IE condition, which relaxes the existing restrictive persistently exciting or cooperative finite-time excitation condition. Then, on the basis of the constructed adaptive observers, state and output variables of the leader, a fully distributed TVFT for EL systems with an uncertain leader is developed, which means the proposed formation tracking controller operates independently of the system dynamic knowledge of the leader agent; with the help of the Lyapunov stability theory, the TVFT criterion for the considered system is derived. Finally, in order to demonstrate the theoretical results derived in this article, a practical air–ground formation tracking platform, which consists of an unmanned aerial vehicle and four unmanned ground vehicles, is introduced, and physical experimental results are obtained.

This article discusses the robust predefined output containment (RPOC) control problem for heterogeneous nonlinear multiagent systems having multiple uncertain nonidentical leaders. In order to solve this problem, a new kind of distributed observer-based RPOC control framework is presented. First, for obtaining the information of nonidentical leaders’ dynamics, including uncertain parameters in leaders’ system matrices, output matrices, states, and outputs, four kinds of adaptive observers are constructed in a fully distributed form without any knowledge of the dynamics of nonidentical leaders, exactly. Second, on the basis of adaptive learning technique, a new RPOC controller is then developed by using the presented observers, where the adaptive observers can make up for the uncertain parameter in followers’ dynamics, and the solutions of output regulation equations can be obtained adaptively by the developed adaptive strategy. Furthermore, with the help of the output regulation method and Lyapunov stability theory, the RPOC criteria for the considered system under unknown nonidentical leaders’ dynamics are derived from the constructed controller. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed RPOC controller.

The distributed nonsmooth constrained optimization problems over higher-order systems are investigated in this study. The challenges lies in the fact that the output of the agent is directly controlled by the state variable rather than the control input. Compared to existing works, the local objective function is merely assumed to be nonsmooth. Firstly, an initialization-free fully distributed derivative feedback control scheme is developed for the known objective function over double-integrator systems. The local generic constraint is addressed by an adaptive nonnegative penalty factor. Secondly, an initialization-free fully distributed state feedback control scheme is proposed for the unknown objective function over double-integrator systems. Addressing the local box constraint involves incorporating an adaptive penalty factor. Thirdly, the above two algorithms are extended to the general higher-order systems using the tracking control method. In addition, the above-developed methods are proved to be asymptotically convergent under certain conditions. Eventually, the efficiency of the above-produced methods is shown via four simulation cases.