Data-Driven Formation Control for Unknown MIMO Nonlinear Discrete-Time Multi-Agent Systems With Sensor Fault

A Hierarchical Distributed Data-Driven Adaptive Learning Control for Nonaffine Nonlinear MASs

This article designs a new hierarchical distributed data-driven adaptive learning control algorithm to accomplish the leader-following tracking control objective for nonaffine nonlinear multiagent systems (MASs). The proposed hierarchical control structure is composed of a distributed observer and a decentralized data-driven adaptive learning controller. Considering that some followers cannot directly receive information from the leader, a distributed observer is designed to estimate the information of the leader. Based on this, a decentralized data-driven adaptive learning controller is further devised to enable the follower to track the estimated information of the leader, where the model parameter learning algorithm is developed to capture the dynamic characteristics of the original system. One advantage of the developed hierarchical control learning algorithm is that neither the leader's system model nor the follower's system model is needed. The other one is the elimination of the noncausal problem without the additional assumption. Simulation results exemplify the merits of the theoretical results by comparisons.

Adaptive Descriptor Sliding-Mode Observer-Based Dynamic Event-Triggered Consensus of Multiagent Systems Against Actuator and Sensor Faults

Actuator and sensor faults are among the most common factors affecting the stability of multiagent systems (MASs). This article proposes a dynamic event-triggered fault-tolerant control (FTC) algorithm based on descriptor sliding-mode observers to address actuator and sensor faults in MASs. First, the MAS dynamics are reformulated into a descriptor form, enabling an observer to simultaneously achieve state estimation and fault diagnosis. Using the estimation results, an adaptive FTC algorithm is developed to maintain the stability of MASs in the presence of concurrent faults, with control gains updated based on the observer consensus error. A dynamic event-triggered mechanism is incorporated to manage data transmission and update neighboring agents' information for the controller, thereby reducing communication overhead. Finally, a numerical simulation involving multiple quadrotors is conducted to validate the effectiveness of the proposed method.

Event-triggered fault-tolerant tracking control for multiagent systems under actuator/sensor faults

Sensor faults can contaminate system measurements such that reliable estimation cannot be achieved and further cause abnormalities in multiagent systems (MASs). Addressing this problem, this study develops an event-triggered fault-tolerant tracking control (FTTC) protocol. The descriptor approach is first used to construct extended states and eliminate sensor faults from the measurement equation. A sliding-mode observer is then tailored based on the transformed system to realize state estimation and fault diagnosis (FD) simultaneously. Subsequently, an event-triggered distributed estimator is developed to isolate the uncertainty's influence and reduce communication overhead while estimating the leader's state. By incorporating the estimator and observer outputs, an FTTC protocol is developed to ensure stability under actuator/sensor faults. Finally, the investigated FTTC method is validated with a numerical simulation of multiple quadrotors.

Model-free adaptive consensus design for a class of unknown heterogeneous nonlinear multi-agent systems with packet dropouts

This paper studies the consensus problem for a class of unknown heterogeneous nonlinear multi-agent systems via a network with random packet dropouts. Based on the dynamic linearization technique, novel model-free adaptive consensus protocols with the data compensation mechanism are designed for both leaderless and leader-following cases. The advantage of this approach is that only neighborhood input and output data of the agents are required in the protocol design. For the stability analysis, a new Squeeze Theorem based method is developed to derive the theoretic results instead of the traditional contraction mapping principle used in model-free adaptive control. It is shown that the consensus can be achieved for both leaderless and leader-following cases if the communication topology is strongly connected. Finally, numerical simulations verifying the correctness of the theoretical results are given.

Event-Triggered Cooperative Model-Free Adaptive Iterative Learning Control for Multiple Subway Trains With Actuator Faults

This article investigates the issue of speed tracking and dynamic adjustment of headway for the repeatable multiple subway trains (MSTs) system in the case of actuator faults. First, the repeatable nonlinear subway train system is transformed into an iteration-related full-form dynamic linearization (IFFDL) data model. Then, the event-triggered cooperative model-free adaptive iterative learning control (ET-CMFAILC) scheme based on the IFFDL data model for MSTs is designed. The control scheme includes the following four parts: 1) the cooperative control algorithm is derived by the cost function to realize cooperation of MSTs; 2) the radial basis function neural network (RBFNN) algorithm along the iteration axis is constructed to compensate the effects of iteration-time-varying actuator faults; 3) the projection algorithm is employed to estimate unknown complex nonlinear terms; and 4) the asynchronous event-triggered mechanism operated along the time domain and iteration domain is applied to lessen the communication and computational burden. Theoretical analysis and simulation results show that the effectiveness of the proposed ET-CMFAILC scheme, which can ensure that the speed tracking errors of MSTs are bounded and the distances of adjacent subway trains are stabilized in the safe range.

Robust Hierarchical Formation Control of Unmanned Aerial Vehicles via Neural-Based Observers

Herein, we investigate the robust formation control problem for a group of unmanned aerial vehicles (UAVs) with system uncertainty. A hierarchical formation control strategy is introduced to ensure the uniform ultimate boundedness of each UAV’s reference tracking error. First, a group of saturated high-level virtual agents are defined to act as the trajectory planners that offer feasible position references to the actual UAVs. A sliding mode neural-based observer is then constructed to estimate the nonlinear uncertainty in the UAV model. Furthermore, sliding mode controllers are designed for both the position loop and the attitude loop of the UAV. To attenuate the chattering phenomenon in the control input, a saturated and smoothed differentiator is proposed along with an observation introduction function. The effectiveness of the proposed control scheme is validated by both the Lyapunov stability theory and numerical simulations based on a multiple-UAV system.