In this paper, the issue of distributed adaptive finite-time fault-tolerant cooperative control (FT-FTCC) problem is investigated for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with unknown parameter uncertainties, actuator faults, input saturation and external disturbances. Starting from the dynamic models of the UAVs and UGVs, an unified control model is presented. Then, a sliding-mode estimator is presented to estimate the position of the leader for the followers which only uses the information from neighbours. Next, a distributed adaptive FT-FTCC scheme, which can also deal with the uncertainties, actuator faults, input saturation and disturbances, is proposed by utilising disturbance observers and neural networks. Based on Lyapunov function approach, the tracking errors of all followers subject to the pre-defined desired positions are uniformly ultimately bounded. Finally, simulations are given to validate the efficiency of the developed FT-FTCC scheme.
The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms, we propose a CNN-LSTM-A model to optimize the temperature settings for a decomposition furnace. The proposed model combines the features selected by Least Absolute Shrinkage and Selection Operator (Lasso) with others suggested by domain experts as inputs, and uses CNN to mine spatial features, LSTM to extract time series information, and an attention mechanism to optimize weights. We deploy sensors to collect production measurements at a real-life cement factory for experimentation and investigate the impact of hyperparameter changes on the performance of the proposed model. Experimental results show that CNN-LSTM-A achieves a superior performance in terms of prediction accuracy over existing models such as the basic LSTM model, deep-convolution-based LSTM model, and attention-mechanism-based LSTM model. The proposed model has potentials for wide deployment in cement plants to automate and optimize the operation of decomposition furnaces.
In this article, the distributed fault-tolerant time-varying formation control (FTTVFC) strategy is presented for the heterogeneous multiple UAVs and UGVs subject to actuator faults and external disturbances with directed communication topologies. The FTTVFC scheme is presented by utilizing the adaptive updating gains, boundary layer theory and radial basis function neural networks. According to Lyapunov function method, the formation errors are uniformly ultimately bounded (UUB). Finally, the simulation results verify the efficiency of the designed scheme.
This paper addresses the issue of decentralized adaptive event-triggered fault-tolerant synchronization tracking control for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with prescribed performance subject to actuator faults and external disturbances. The transformed error is presented by integrating the prescribed performance function into the synchronous tracking errors. Based on the transformed error, the decentralized adaptive event-triggered fault-tolerant synchronization tracking control scheme is developed to achieve the synchronous tracking purpose with prescribed performance subject to actuator faults and external disturbances. Based on Lyapunov theory, the stability of the closed-loop systems is analyzed and the synchronous tracking errors are bounded within the prescribed boundary. Furthermore, the Zeno behavior is also excluded. Finally, simulation studies are provided to verify the efficiency of the proposed control scheme.
This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the XY plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme.
In this article, the issue of adaptive fault-tolerant cooperative control is addressed for heterogeneous multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and sensor faults under denial-of-service (DoS) attacks. First, a unified control model with actuator faults and sensor faults is developed based on the dynamic models of the UAVs and UGVs. To handle the difficulty introduced by the nonlinear term, a neural-network-based switching-type observer is established to obtain the unmeasured state variables when DoS attacks are active. Then, the fault-tolerant cooperative control scheme is presented by utilizing an adaptive backstepping control algorithm under DoS attacks. According to Lyapunov stability theory and improved average dwell time method by integrating the duration and frequency characteristics of DoS attacks, the stability of the closed-loop system is proved. In addition, all vehicles can track their individual references, while the synchronized tracking errors among vehicles are uniformly ultimately bounded. Finally, simulation studies are given to demonstrate the effectiveness of the proposed method.
This paper is concerned with the design of a fault estimation scheme with component fault and sensor noise for the three-phase inverter of high-speed trains. To design the component fault estimation observer, the three-phase inverter system states are augmented with the disturbance treated as new states, leading to an augmented and singular system. The observer is designed to generate the needed residual signal for fault estimation by using system output measurements. The adaptive law is established by the Lyapunov theory, and the design of the proposed observer is reformulated as linear matrix inequality(LMI). Simulation result shows the effectiveness of the proposed approach for fault estimation of the three-phase inverter.
Abstract Here, the issue of distributed adaptive event‐triggered fault‐tolerant cooperative control (FTCC) is studied for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults under denial‐of‐service attacks. To save the limited communication network resources, the distributed adaptive event‐triggered FTCC scheme is investigated for the multiple UAVs and UGVs which does not require continuous information relating to its neighbours. It is proven that the tracking errors are uniformly ultimately bounded by utilizing the Lyapunov function approach. Furthermore, the Zeno behaviour is excluded with the proposed scheme. Finally, simulation studies are provided to demonstrate the efficiency of the proposed scheme.
This paper proposes a decentralized adaptive event-triggered fault-tolerant cooperative control (ET-FTCC) scheme for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) with actuator faults and external disturbances under denial-of-service (DoS) attacks. The multiple UAVs and UGVs have a larger search radius, which is important in both the civilian and military domains. The different dynamics between UAVs and UGVs result in unbalanced interactions in the communication topologies, which increases the complexity of cooperative control. DoS attacks are conducted in both sensor and control channels. The dynamic models of UAVs and UGVs are introduced firstly, and the unified heterogeneous multiagent system model with actuator faults is established. The composite observer is designed to obtain the information of state and lumped disturbance, which is used to design the controller. In order to save the limited communication network resources, the event-triggered mechanism is introduced. The transformed error is presented by using the prescribed performance function (PPF). Then, the sliding-mode manifold is presented by combining the event-triggered control scheme to achieve the tracking purpose with actuator faults, external disturbances, and DoS attacks. Based on the Lyapunov function approach, the tracking errors are bounded within the prescribed boundary. Finally, the effectiveness of the proposed method is verified by qualitative analysis and quantitative analysis of the simulation results. This study can enhance the security and reliability of heterogeneous multiagent systems, providing technical support for the safe operation of unmanned systems. This paper mainly solves the FTCC problem of second-order nonlinear heterogeneous multiagent systems, and further research is needed for the FTCC problem of higher-order nonlinear heterogeneous multi-agent systems. In addition, the system may encounter multiple cyber attacks. As one of the future research works, we can extend the results of this paper to high-order nonlinear systems under multiple cyber attacks, which contain DoS attacks and deception attacks, and achieve fault-tolerant cooperative control of heterogeneous multiagent systems.
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