This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local errors, and introduces potential fields as an aid to the expansion process of random trees. It reasonably triggers a greedy strategy based on the principle of field strength descending gradient optimization, accelerating the process of random tree expansion to a better region and reducing path search time. Compared with other optimization algorithms that improve the sampling method to reduce the search time of the random tree, PF-RRT takes full advantage of the potential field without limiting the arbitrariness of random tree expansion. Secondly, the path construction process is based on the principle of triangle inequality for the root node of the new node to improve the quality of the path in one iteration. Simulation experiments of the algorithm comparison show that the algorithm has the advantages of fast acquisition of high-quality initial path solutions and fast optimal convergence in the path search process. Compared with the original algorithm, obtaining the initial solution using PF-RRT can reduce the time loss by 20% to 70% and improve the path quality by about 25%. In addition, the feasibility of PF-RRT for UAV path planning is demonstrated by actual flight test experiments at the end of the experiment.

The permanent magnetic suspension (PMS) train has distinct advantages in reducing energy consumption and increasing the suspension air gap. Aiming at the problems of uncontrollable magnetic field and underdamping of the PMS system, the electromagnetic damping structure is introduced, and a fuzzy linear active disturbance rejection control (Fuzzy-LADRC) method with an air gap deviation integrator is proposed. Firstly, the permanent magnet electromagnetic hybrid suspension (PEMS) system is constructed, and the model analysis is carried out to prove that the system is controllable and observable, and the hardware experimental platform is built. Secondly, a LADRC method is designed based on the model of the platform suspension control unit, and fuzzy control is introduced into the LADRC state error feedback control theory for parameter self-tuning to improve the adaptability and robustness of the platform. At the same time, an air gap deviation integrator is introduced to further improve the control accuracy. Finally, the comparison experiments with LADRC and PID control methods are carried out in terms of stability, anti-interference, robustness and adaptability. The experimental results show that the proposed Fuzzy-LADRC method has the best control effect. It provides practical reference for improving the stability of the PMS train.