The parafoil is a flexible aircraft with good load characteristics and endurance. Compared with common rotor Unmanned Aerial Vehicles (UAV), its excellent gliding characteristics make it widely used in fixed-point airdrop missions in the aviation field. However, while the flexible flight structure brings excellent flight characteristics, the homing of the parafoil will be seriously disturbed by wind. This paper studies the wind disturbance problem of parafoils, and proposes a dynamic homing planning strategy derived from the forward model based on the parafoil dynamic model. Focusing on the homing process of the parafoil, the wind repulsion potential field is established for the disturbance of the low-altitude wind field by Artificial Potential Field (APF). Recursive navigation planning is carried out based on Dynamic Path Rapid-exploration Random Tree (DP-RRT). We validate the dynamic programming algorithm through simulation and experimental environments. The results show that the dynamic programming algorithm can effectively improve the parafoil homing process’s flight control stability and homing accuracy.

The distillation process plays an essential role in the petrochemical industry. However, the high-purity distillation column has complicated dynamic characteristics such as strong coupling and large time delay. To control the distillation column accurately, we proposed an extended generalized predictive control (EGPC) method inspired by the principles of extended state observer and proportional-integral-type generalized predictive control method; the proposed EGPC can adaptively compensate the system for the effects of coupling and model mismatch online and performs well in controlling time-delay systems. The strong coupling of the distillation column needs fast control, and the large time delay requires soft control. To balance the requirement for fast and soft control at the same time, a grey wolf optimizer with reverse learning and adaptive leaders number strategies (RAGWO) was proposed to tune the parameters of EGPC, and these strategies enable RAGWO to have a better initial population and improve its exploitation and exploration ability. The benchmark test results indicate that the RAGWO outperforms the existing optimizers for most of the selected benchmark functions. Extensive simulations show that the proposed method in terms of fluctuation and response time is superior to other methods for controlling the distillation process.

This special issue collects 9 peer-reviewed papers. The main topics deal with smart wearable device development (4 papers) and intelligent signal processing algorithms integrated into wearable devices (5 papers). These papers are based on specific applications on online monitoring or tracking of human physical activity (5 papers) and medical indices (EEG, ECG, PPG and blood glucose) (4 papers) but can also be extended to processing of more generalized scenarios.

We present a model of changing the intensity of interaction based on the individual behavior to study the iterated prisoner's dilemma game in social networks. In this model, each individual has an assessed score of reputation which is obtained by considering the evaluation level of interactive partners for its present behavior. We focus on the effect of evaluation level on the changing intensity of interaction between individuals. For an individual with good behavior, the higher the evaluation level of its partners for its good behavior, the better its reputation, and the higher the probability of surrounding partners interaction with it. On the contrary, for an individual with bad behavior, the lower the evaluation level of its partners for its bad behavior, the worse its reputation, and the less the probability of surrounding neighbors interaction with it. Simulation results show that this effective mechanism can drastically facilitate the emergence and maintenance of cooperation in the population under a treacherous chip. Interestingly, for a small or moderate treacherous chip, the cooperation level monotonously ascends as the evaluation level increases; however, for a higher treacherous chip, existing an optimal evaluation level, which can result in the best promotion of cooperation. Furthermore, we find better agreement between simulation results and theoretical predictions obtained from an extended pair-approximation method, although there are some tiny deviations. We also show some typical snapshots of the system and investigate the reason for appearance and persistence of cooperation. The results further show the importance of evaluation level of individual behavior in coevolutionary relationships.