This paper investigates the distributed adaptive event-triggered least-distance formation control problem for nonlinear multiagent systems (MASs) over switching digraphs via noncooperative game theory. First, distributed event-triggered estimators incorporating a switching-activated communication strategy are proposed to estimate the moving target and all agents’ decisions while reducing inter-agent communication. Based on the designed distributed estimators, a distributed time-varying Nash equilibrium (NE) seeking algorithm is established such that all agents’ decisions asymptotically reach the NE solution. Since high-order derivatives of the proposed distributed estimator states do not exist due to digraphs switching and event-triggering, the backstepping control design cannot be implemented. To overcome this difficulty, three-stage cascade filters are designed to provide sufficiently smooth signals. Then, based on the developed three-stage cascade filters, an adaptive event-triggered least-distance formation controller is proposed by the backstepping control technique. It is proved that the constructed formation control method can ensure that all agents achieve the least-distance formation, i.e., all agents’ outputs asymptotically reach a desired shape while minimizing the overall distance to the moving target. Finally, a simulation example on nonholonomic mobile robots is provided to illustrate the validity of the developed theoretical results.

This article studies the game-based fuzzy adaptive least-distance formation control problem for nonlinear multiagent systems (MASs) under event-triggered communication. Since agents cannot obtain the nonneighboring agents’ actions, and a subset of agents cannot access the moving target, distributed event-triggered observers are designed to estimate all agents’ actions and the moving target. Based on the designed distributed observers, a distributed time-varying Nash equilibrium (NE) seeking strategy is constructed such that the agents’ actions asymptotically converge to the NE. To overcome the nonexistence problem of high-order derivatives of the estimated signals arising from intermittent communication, three stage cascade filters are proposed to generate sufficiently smooth signals to replace the estimated signals. Then, a fuzzy least-distance formation controller is developed by utilizing the proposed cascade filters and the backstepping control theory. It is proven that the proposed formation control scheme can guarantee that all agents asymptotically achieve the least-distance formation. Finally, simulation and comparison results on marine surface vehicles (MSVs) verify the effectiveness of the proposed control scheme.

This paper investigates the distributed fuzzy adaptive Nash equilibrium (NE) seeking problem in noncooperative games for nonlinear multi-agent systems under unreliable communication networks. Since the considered unreliable communication networks are jointly strongly connected switching networks and suffer from time-delays, agents cannot receive their neighboring agents' actions or can only obtain delayed actions. To estimate the neighboring agents' actions, a distributed NE seeking observer is developed. Then, based on the proposed distributed NE seeking observer and backstepping control technology, a distributed fuzzy adaptive control scheme is constructed by utilizing fuzzy logic systems and adopting integrable functions and bounded parameter estimation algorithms. It is proven that the observation error of the distributed NE seeking observer asymptotically converges to zero, and the developed fuzzy adaptive control scheme can ensure that the agents' outputs asymptotically converge to the NE of the noncooperative game. Moreover, the non-differentiable problem of virtual controllers is avoided. Finally, we apply the distributed fuzzy adaptive control scheme to marine surface vehicles, and the simulation and comparison results confirm its effectiveness.

In this article, we study the distributed fuzzy formation control problem for a class of strict-feedback nonlinear multiagent systems (NMASs) under communication delays and jointly connected switching topology. Since the communication between agents is affected by time-varying delay and some agents cannot access the leader's information under jointly connected switching topology, a communication-delay-related distributed formation observer is designed to estimate the leader's information and simultaneously mitigate the effects of communication delays. By using fuzzy logic systems to approximate the unknown functions, the controlled uncertain NMASs are transformed into the strict-feedback parameterized NMASs. Then, based on the designed communication-delay-related distributed formation observer and the backstepping control design theory, a fuzzy adaptive formation control algorithm is proposed. By constructing the Lyapunov functions, it is proved that the designed communication-delay-related distributed formation observer errors converge to zero exponentially and the proposed distributed fuzzy formation control algorithm can ensure that the closed-loop systems are semi-globally uniformly ultimately bounded, with the formation tracking errors converging to an adjustable neighborhood around zero. Finally, we apply the distributed fuzzy formation control scheme to marine surface vehicles (MSV), the simulation results and comparisons with the previous control methods verify its effectiveness.

The diffusion models including Denoising Diffusion Probabilistic Models (DDPM) and score-based generative models have demonstrated excellent performance in speech synthesis tasks. However, its effectiveness comes at the cost of numerous sampling steps, resulting in prolonged sampling time required to synthesize high-quality speech. This drawback hinders its practical applicability in real-world scenarios. In this paper, we introduce ReFlow-TTS, a novel rectified flow based method for speech synthesis with high-fidelity. Specifically, our ReFlow-TTS is simply an Ordinary Differential Equation (ODE) model that transports Gaussian distribution to the ground-truth Mel-spectrogram distribution by straight line paths as much as possible. Furthermore, our proposed approach enables high-quality speech synthesis with a single sampling step and eliminates the need for training a teacher model. Our experiments on LJSpeech Dataset show that our ReFlow-TTS method achieves the best performance compared with other diffusion based models. And the ReFlow-TTS with one step sampling achieves competitive performance compared with existing one-step TTS models.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

This article investigates the fuzzy adaptive resilient formation control issue for uncertain nonlinear multiagent systems (MASs) with immeasurable states and under denial-of-service (DoS) attacks. Fuzzy logic systems are utilized to model unknown agents, and a fuzzy state estimator is formulated to reconstruct the agents' unknown states. To obtain the unknown leader information estimation and its high-order derivatives under DoS attacks, a distributed resilient formation estimator is proposed. Based on the designed fuzzy state estimator and resilient formation estimator, a fuzzy output-feedback adaptive resilient formation control scheme is developed via backstepping control methodology. It is proved that the developed fuzzy resilient formation control scheme can guarantee the controlled nonlinear MASs are stable and formation tracking errors converge even under unknown states and DoS attacks. Finally, the proposed fuzzy adaptive resilient formation control method is applied to marine surface vehicles, the simulation results and comparisons show the effectiveness of the presented fuzzy adaptive resilient formation control methodology.

This paper investigates the observer-based fuzzy event-triggered consensus fault-tolerant control (FTC) problem for nonlinear multiagent systems (MASs) with jointly connected switching topologies and actuator faults. Since a part of agents cannot receive information from their neighbors and leader under switching topologies, a distributed observer is designed to estimate unknown leader. At the same time, to avoid continuous information transmission and enhance the efficiency of network resources utilization among agents, an event-triggered communication mechanism is constructed to schedule inter-agent communication. Meanwhile, a fuzzy state observer is formulated to estimate the unmeasured states of the agents. Based on the distributed event-triggered observer and fuzzy state observer, an output-feedback event-triggered fuzzy FTC scheme is proposed by backstepping recursive control design. It is demonstrated that all signals of the controlled MASs are semi-globally uniformly ultimately bounded (SGUUB), consensus tracking errors converge to a small neighborhood of zero, and continuous communication between agents is avoided. Finally, simulation results on marine surface vehicles (MSVs) testify the advantages and effectiveness of the theoretical results.

This article studies the fuzzy adaptive event-triggered resilient formation (RF) control problem for nonlinear multiagent systems (MASs) subject to denial-of-service (DoS) attacks and input saturation. Fuzzy logic systems (FLSs) are adopted to identify unknown subsystems, and a fuzzy state estimator is established to address the issue resulted from unmeasurable states. A distributed RF filter is proposed to estimate the unknown leader and obtain the$n$-order derivative of the estimated leader under DoS attacks. To save limited communication resource and reduce the number of controller updates, a switching event-triggered mechanism (SETM) is introduced. Then, a fuzzy event-triggered RF control algorithm is designed via backstepping control method. It is demonstrated that the controlled MASs are stable and formation errors converge even subject to DoS attacks and input saturation. Finally, we apply the event-triggered RF control algorithm to unmanned surface vehicles (USVs), the simulation results verify the effectiveness of the proposed event-triggered RF control algorithm.

In this paper, we present our work in track 2 of the Multi-modal Information based Speech Processing (MISP) 2022 Challenge. We built a cascaded system and explored different acoustic front-ends and end-to-end speech recognition back-ends based on multimodal. To promote effective fusion between the different modalities, we introduced a multi-level feature fusion network. By utilizing several additional strategies, our system achieved 31.88% in the concatenated minimum permutation character error rate (cpCER) on the evaluation set, achieving the 3th place ranking in the competition.

The clustering algorithm plays a crucial role in speaker diarization systems. However, traditional clustering algorithms suffer from the complex distribution of speaker embeddings and lack of digging potential relationships between speakers in a session. We propose a novel graph-based clustering approach called Community Detection Graph Convolutional Network (CDGCN) to improve the performance of the speaker diarization system. The CDGCN-based clustering method consists of graph generation, sub-graph detection, and Graph-based Overlapped Speech Detection (Graph-OSD). Firstly, the graph generation refines the local linkages among speech segments. Secondly the sub-graph detection finds the optimal global partition of the speaker graph. Finally, we view speaker clustering for overlap-aware speaker diarization as an overlapped community detection task and design a Graph-OSD component to output overlap-aware labels. By capturing local and global information, the speaker diarization system with CDGCN clustering outperforms the traditional Clustering-based Speaker Diarization (CSD) systems on the DIHARD III corpus.

This article investigates the adaptive neural network (NN) event-triggered containment control problem for a class of nonlinear multiagent systems (MASs). Since the considered nonlinear MASs contain unknown nonlinear dynamics, immeasurable states, and quantized input signals, the NNs are adopted to model unknown agents, and an NN state observer is established by using the intermittent output signal. Subsequently, a novel event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels are established. By decomposing quantized input signals into the sum of two bounded nonlinear functions and based on the adaptive backstepping control and first-order filter design theories, an adaptive NN event-triggered output-feedback containment control scheme is formulated. It is proved that the controlled system is semi-globally uniformly ultimately bounded (SGUUB) and the followers are within a convex hull formed by the leaders. Finally, a simulation example is given to validate the effectiveness of the presented NN containment control scheme.

This article considers the finite-time containment output-feedback control problem for a class of nonlinear multiagent systems with unmeasurable states and input saturation. Fuzzy logic systems and a smooth function are first employed to model unknown agent's subsystems and input saturation, respectively. Then, a novel fuzzy state observer is established via the intermittent output signal. By introducing the first-order filter and using the sampled estimating states and triggered output signals, an event-triggered mechanism consisting of the sensor-to-controller and controller-to-actuator is formulated. Consequently, under the framework of the finite-time stability criterion and adaptive backstepping control design technique, a finite-time adaptive fuzzy event-triggered output-feedback containment control design method is proposed and the semiglobal finite-time stability of the controlled system is rigorously proved. Finally, the simulation results are given to confirm the effectiveness of the proposed control scheme.

This article investigates the finite-time adaptive fuzzy prescribed performance formation control problem for high-order nonlinear multiagent systems. Fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics. By combining the dynamic surface control technique and backstepping recursive design, an adaptive fuzzy prescribed performance formation control algorithm is developed. To reduce unnecessary transmission of network resources, a novel event-triggered mechanism is proposed in the control method. Subsequently, based on the adding power integral method and finite-time stability theory, it is proved that all signals of the controlled system are bounded and the tracking errors do not exceed the prescribed performance bounds in a finite time. Simulation results are given to verify that the presented formation control scheme achieves desired results.

This article deals with an adaptive neural network (NN) formation fault-tolerant control (FTC) issue for nonlinear multiagent systems (MASs) with intermittent actuator faults. Since the controlled MASs contain unknown nonlinear dynamics and unmeasurable states, NNs are applied to model unknown subsystems, and an NN state observer is designed by utilizing intermittent output signals. By the designed state observer and introduced first-order filter technique, a new event-triggered mechanism consisting of both the sensor-to-controller and controller-to-actuator channels is constructed. To avoid the virtual controller nondifferentiability problem by using backstepping control theory directly, this article redesign the virtual controller and controller obtained by the backstepping control technique without considering the event-triggered signals. The developed output-feedback formation FTC scheme can guarantee the controlled MASs are semi-globally uniformly ultimately bounded in presence of the unknown states and actuator faults. Finally, a simulation example confirms the effectiveness of the presented theory and approach.

This article studies the fuzzy adaptive finite-time consensus control problem for high-order nonlinear multiagent systems with unknown nonlinear dynamics. In control design,fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and under the frameworks of adaptive backstepping recursive design and finite-time stability theory, an adaptive fuzzy finite-time consensus control method is developed. To save communication resources and reduce the numbers of controller execution times, a dynamic event-triggered mechanism with a relative threshold is established. Subsequently, an event-triggered-based finite-time fuzzy adaptive control scheme is formulated. Furthermore, by constructing novel integral-type Lyapunov functions and adding a power integrator technique, the finite-time stability of the closed-loop system and the convergence of consensus tracking errors are proved. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed adaptive event-triggered consensus control method.