This paper addresses the distributed formation-containment (DFC) problem for multiple Euler-Lagrange systems with model uncertainties via output feedback in both constant and time-varying formation cases. First, a novel definition of the DFC problem is proposed using a two-layer framework. Since only parts of the followers can acquire the states of the dynamic leader, we design a distributed finite-time sliding-mode estimator to obtain accurate estimations of the desired position and velocity for each agent. Next, to deal with the absence of velocity sensors, we propose two DFC control laws combined with the high-gain observer for the leaders and the followers, respectively, while the time-varying formation in the first layer and the leader-based containment in the second layer can be achieved. Further, the adaptive neural networks are applied to deal with the model uncertainties due to their superior approximation capability. The uniform ultimate boundedness of all the state errors can be guaranteed by Lyapunov stability theory. In addition, a unified framework is given which can be transformed to four other basic distributed problems. Finally, simulation examples are presented to illustrate the feasibility of the theoretical results.

To steer a group of agents to maneuver with the desired collective forms, this paper studies the affine formation maneuver control of high-order multi-agent systems using a two-layered leader-follower strategy, in which agents are divided into three types: first leader, second leader group, and followers. The first leader will decide the whole formation's maneuver parameters, and the second leader group will keep the desired relative positions with respect to the first leader. The followers aim at moving to the desired positions affinely localized by the leaders. The main feature of this two-layered leader-follower strategy is that the information interaction from the first leader to the second leader group requires wireless communication, and only the measurements of local relative position and its finite-order time-derivatives are needed for the followers. To achieve the control objective under the given constraints, the distributed control algorithms are designed for the second leader group and followers, in which the backstepping, distributed estimation, $PD^m$ , and adaptive gain design techniques are employed. Finally, a simulation example with obstacle avoidance is provided to validate the effectiveness of the proposed control algorithms.

Motivated by the challenging formation stabilization problem for mobile robotic teams wherein no distance or relative position measurements are available but each robot can only measure some of relative angles with respect to its neighbors in its local coordinate frame, we develop the notion of "angle rigidity" for a multi-point framework, named "angularity", consisting of a set of nodes embedded in a Euclidean space and a set of angle constraints among them. Different from bearings or angles defined in a global frame, the angles we use do not rely on the knowledge of a global frame and are signed according to the counter-clockwise direction. Here angle rigidity refers to the property specifying that under proper angle constraints, the angularity can only translate, rotate or scale as a whole when one or more of its nodes are perturbed locally. We first demonstrate that this angle rigidity property, in sharp comparison to bearing rigidity or other reported rigidity related to angles of frameworks in the literature, is not a global property since an angle rigid angularity may allow flex ambiguity. We then construct necessary and sufficient conditi

This paper addresses the event-triggered asynchronous fault detection (FD) problem of fuzzy-model-based nonlinear Markov jump systems (MJSs) with partially unknown transition probabilities. For this objective, the nonlinear plant is modeled as an interval type-2 (IT2) fuzzy MJSs with the aid of the IT2 fuzzy sets capturing the uncertainties of the membership functions. An adaptive event-triggered scheme (ETS) is introduced to bring down the costs of the communication network from the system to the fuzzy fault detection filter (FDF), in which the triggering parameter can be adaptive tuned with the system dynamics. A hidden Markov model (HMM) is employed to characterize the asynchronous phenomenon between the system and FDF. Unlike the existing results, the transition probabilities of the plant and the FDF are allowed to be partially known. By using the Lyapunov and the membership-function-dependent (MFD) methods, the existence conditions of the FDF are derived. Finally, the proposed FD methods are verified by a numerical simulation.

In this article, the problem of adaptive event-triggered dissipative control is investigated for interval type-2 (IT2) fuzzy nonlinear Markov jump systems subject to asynchronous switching and cyberattacks. To reduce unnecessary transmission, an adaptive event-triggered mechanism (AETM) is introduced to fully employ the limited network resources and alleviate the pressure of network communication. The considered cyberattacks are described by a stochastic function with stochastic distribution. Then, a novel IT2 fuzzy closed-loop system is efficiently built under the AETM and the cyberattacks model, in which the asynchronous phenomenon of the plant and controller are described by the hidden Markov model. By utilizing the Lyapunov stability approach, sufficient conditions are established for the IT2 fuzzy closed-loop systems with strictly dissipative performance. Two examples are given to verify the effectiveness of the proposed algorithm.