To simultaneously achieve space formation flight and target tracking of multiple unmanned aerial vehicles (UAVs) and solve the rotation buffeting problem of the UAV, a robust formation control and target tracking algorithm is proposed. The artificial potential function consisting of formation control term and target tracking term is established, and its convergence is proved. The sliding mode control method with the saturation function is established, and a sufficient condition for sliding mode to occur is analysed. Finally, the numerical simulation is conducted for the proposed algorithm, and the simulation result is analysed. The results show that the proposed algorithm can quickly achieve the formation flight and target tracking of multi-UAVs and improve the tracking performance; meanwhile, it can effectively weaken the rotation buffeting and improve the robustness.

To improve the accuracy and reliability of the relative positioning for unmanned aerial vehicles (UAVs), a relative positioning method based on multi-source information fusion is proposed. An integrated positioning scheme is constructed by the Beidou Navigation Satellite System (BDS) receivers, Global Positioning System (GPS) receivers, Vision-Based Navigation System (VisNav), and Inertial Navigation System (INS). The BDS pseudorange relative difference equation, the GPS relative difference equation, the relative line-of-sight vector equation, and the INS measurement equation are established, respectively. The least squared (LS) method is used to realize information fusion, and the Gauss-Newton method is used to iteratively solve the relative position results. Finally, numerical simulation and result analysis are conducted with different sensor configurations. Simulation results show that BDS/INS/GPS/VisNav relative positioning result is obviously better than that of INS/GPS, INS/VisNav, and INS/GPS/VisNav, and the proposed method reduces the relative positioning errors and has a higher accuracy and excellent robustness. The research result is suitable for application scenarios with high navigation accuracy requirement such as AAR and intelligent swarm formation control.

Aiming at the problems of manual setting of control rules and low control accuracy of the fuzzy PID control method, an attitude control method of the unmanned aerial vehicle (UAV) based on the extended Kalman filter (EKF) and adaptive fuzzy PID (AF PID) is proposed. The dynamics equations and measurement equations of UAV are established, and the EKF is used to estimate and predict attitude changes; an adaptive fuzzy PID control algorithm is designed, and the adaptive adjustment method is adopted to revise fuzzy control rules and parameters online; a simulation platform of the attitude control system (ACS) of UAV is built to simulate and verify the control effect of the method. The simulation results show that the proposed algorithm can estimate the change of attitude angles accurately and improve the control accuracy; meanwhile, it also ensures the stability and rapidity of the attitude changes. The research results can resolve the contradiction between high precision and rapid stability of complex systems to a certain extent.

The tubular furnace is one of the main production equipment in petrochemical industry, the main functions of which is to heat the liquid oil in multiple branch tubes in the furnace to the target temperature. Since the temperature of each branch furnace tube is affected by the feed composition, the distribution position of the furnace tube and the uneven distribution of the furnace temperature, these factors may result in the deviation of the oil outlet temperature of each branch, and the serious temperature deviation may lead to the coking of the furnace tube and even cause accidents. In order to overcome the problem of unbalanced outlet temperature of each branch tube in the tubular furnace, this paper proposes a temperature control method GSA-MPIDNN, which is based on genetic simulated annealing (GSA) algorithm to optimize multi-input multi-output proportion-integration-differentiation neural network(MPIDNN). The GSA algorithm is used to find out the optimal initial weights of the MPIDNN, to overcome the deficiency of the algorithm by manually setting the initial weights, and to improve the control performance of the MPIDNN controller on the outlet temperature of the tubular furnace. The Matlab software is used to build the mathematical model of GSA-MPIDNN controller and tubular furnace, and the results are compared and analyzed with the traditional methods such as MPIDNN, PID and fuzzy PID, etc. The results show that the convergence time and error of GSA-MPIDNN are better than the traditional methods, which verifies the effectiveness of the method.