This paper deals with the finite-time consensus control problem for a class of nonlinear strict-feedback multi-agent systems with heterogeneous dynamics. Due to the existence of unknown nonlinear dynamics of the system, this paper adopts the excellent approximation ability of fuzzy logic systems to design the consensus control protocol. Moreover, fuzzy logic systems and adaptive method are used to approximate the unknown system dynamics in the design of virtual controllers for different orders of the subsystems. The proposed direct adaptive fuzzy tracking controller for every agent is constructed via a backstepping design process. The proposed adaptive fuzzy controller can ensure that the outputs of all agents can track a common desired trajectory in finite time. Finally, the performance of the proposed fuzzy adaptive consensus control algorithm is demonstrated by two numerical studies of MASs including both homogeneous and heterogeneous dynamics of the interacting agents.

Scenarios data of renewable energy resources plays an essential role in the study of mitigating the risk in the power system due to their intermittent nature. Existing researches have mainly focused on how to generate high-quality time series based scenarios with corrupted or missing observed data. However, the multimodality of renewable energy resources is largely ignored from the perspective of the spatial-temporal correlation which can significantly strengthen the renewable energy prediction and optimization. To make full use of these multi-modal data and further improve the data quality of scenarios, a cross-modal Generative Adversarial Network (cGAN) model is proposed with the setting of two spatial-temporal transformer models. In cGAN, the task of scenarios generation is formulated as a probability approximation problem. Then, a cross-modal scenarios generation framework is constructed to process data from multi-modal observations. Theoretically, it can generate infinite number of uncertain scenarios data via the repeated random noise sampling technique. Extensive experiments are carried out at a NREL photovoltaic power dataset and a real-world wind power dataset, respectively. The results validate the state-of-the-art performance of cGAN, even though the validation dataset is missing at random. Moreover, the results also show that the setting of spatial-temporal transformer clearly explains the contributions of different modal data, and stabilize the training process of cGAN. Finally, a stochastic day-head economic dispatch is also studied to show the practical value of cGAN.

This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination of neural network and adaptive method to approximate the unknown system dynamics. Thus, the extended approximation property of neural network for event-based sampling can be beneficially introduced. Moreover, the designed controller only updates at discrete time, which enables that the system can be modeled as a hybrid system with impulsive dynamics. Thus, the stability theory of impulsive systems can be used to analyze the convergence of the system. It should be noted that this is the first time to propose an effective event-triggered consensus control algorithm based on neural network. Furthermore, this paper also considers a frequently encountered phenomenon of sensor saturation. The convex hull method is adopted to deal with sensor saturation problem, instead of the widely used sector condition method. Finally, the performance of the proposed neural-network based self-triggered consensus control algorithm is demonstrated by the numerical examples.

Multimodal data fusion analysis is essential to model the uncertainty of environment awareness in digital industry. However, due to communication failure and cyberattack, the sampled time-series data often have the issue of data missing. In some extreme cases, part of units are unobservable for a long time, which results in complete data missing (CDM). To impute missing data, many models have been proposed. However, they cannot address the CDM issue, because no observation data of the unobservable units can be obtained in this case. Thus, to address the CDM issue, a novel cross-modal generative adversarial network (CM-GAN) is proposed in this article. It combines the cross-modal data fusion technique and the deep adversarial generation technique to construct a cross-modal data generator. This generator can generate long-term time-series data from widely existing spatio-temporal modal data in modern industrial system, and then impute missing value by replacing them with generated data. To test the performance of CM-GAN, extensive experiments are conducted on photovoltaic (PV) power output dataset. Compared with other baseline models, the performance of CM-GAN is generally better and reaches the state-of-the-art level. Moreover, sufficient ablation studies are conducted to present the contribution of the cross-modal data fusion technique and show the reasonability of parameter settings of CM-GAN. Apart from this, some prediction experiments are also conducted. The results show that the PV data recovered by CM-GAN can provide more predictability information for improving the prediction accuracy of deep learning model.

Formation control finds broad applications in numerous fields, such as cooperative detection, surveillance, transportation, and disaster rescue. In this article, aiming at hunting a moving target, a two-stage surrounding control algorithm is proposed for linear multiagent systems subject to input saturations. An adaptive distributed observer is developed for each agent to reconstruct the target’s position. With the assistance of the algebraic graph theory and low gain feedback technique, distributed controllers are designed to drive the agents to encircle the moving target with a fixed radius and evenly distributed phase angles. Finally, both numerical simulations and experiments are conducted to verify the effectiveness of the proposed control algorithm.

This paper aims to improve the sensorless control performance of surface-mounted permanent magnet synchronous machine (SPMSM) drives in the whole speed range. Based on the thought of speed partition optimization and smooth transition, a hybrid sensorless control strategy integrating with an enhanced flux linkage observer and the current-frequency (I-f) starting method is proposed. At zero-low speed, the I-f control method is utilized for the stable startup and strong anti-jamming capability. At medium-high speed, an enhanced flux linkage observer based on the sliding-mode compensator is designed for the closed-loop sensorless operation. Taking advantage of the sliding-mode compensator, not only the position estimation errors caused by the adopted filter can be compensated effectively without any speed iteration to improve the system stability, but also the suitable cutoff frequency of the filter can be selected for the fast system dynamic response. Meanwhile, the influence of machine parameters variations on the rotor position estimation accuracy is discussed. Furthermore, in order to achieve the smooth operation between two different control schemes, an adaptive transition algorithm is described in detail. Experimental results indicate that the proposed hybrid sensorless control strategy can show an excellent performance for both steady-state and dynamic operations during full speed range.