Formation Generation for Multiple Unmanned Vehicles Using Multi-Agent Hybrid Social Cognitive Optimization Based on the Internet of Things

Adaptive fixed-time consensus for stochastic multi-agent systems with uncertain actuator faults

In this paper, we study the adaptive fixed-time consensus control for stochastic multi-agent systems (SMASs) with uncertain actuator faults. Firstly, a fully distributed adaptive consensus protocol and an adaptive fault-tolerant consensus protocol are proposed, respectively, to ensure that the fixed-time consensus of SMASs with actuator faults can be reached. Secondly, an adaptive fault-tolerant containment consensus protocol is further proposed for the SMASs by leveraging the signum function, and this protocol can effectively solve the containment consensus in the unbalanced communication network. Finally, some simulation examples are given to verify the effectiveness of our consensus protocols.

Quasi-Consensus of Disturbed Nonlinear Multiagent Systems with Event-Triggered Impulsive Control

Considering the external disturbances, in this paper, the quasi-consensus of multiagent systems is studied via event-triggered impulsive control. By designing a novel event-triggered mechanism (ETM), sufficient conditions to realize leader-following quasi-consensus are derived with event-triggered impulsive control. Additionally, Zeno behavior is also excluded. It is shown that the event-triggered frequency is closely related to the parameters selected in the designed ETM, and less conservative results can be obtained compared with the existing results. Finally, a simulation example is given to demonstrate the effectiveness of our proposed results.

A Combined Model Based on the Social Cognitive Optimization Algorithm for Wind Speed Forecasting

The use of wind power generation can reduce the pollution in the environment and solve the problem of power shortages on offshore islands, grasslands, pastoral areas, mountain areas, and highlands. Wind speed forecasting plays a significant role in wind farms. It can improve economic and social benefits and make an operation schedule for wind turbines on large wind farms. This paper proposes a combined model based on the existing artificial neural network algorithms for wind speed forecasting at different heights. We first use the wavelet threshold method with the original wind speed dataset for noise reduction. After that, the three artificial neural networks, extreme learning machine (ELM), Elman neural network, and Long Short-term Memory (LSTM) neural network, are applied for wind speed forecasting. In addition, the variance reciprocal method and social cognitive optimization (SCO) algorithm are used to optimize the weight coefficients of the combined model. In order to evaluate the forecasting performance of the combined model, we select wind speed data at three heights (20 m, 50 m, and 80 m) at the National Wind Technology Center M2 Tower. The experimental results show that the forecasting performance of the combined model is better than the single model, and it has a good forecasting performance for the wind speed at different heights.