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Due to advantages such as high speed, high accuracy, low maintenance and high reliability, permanent magnet synchronous motor (PMSM) servo systems have been employed in many fields. In some cases, for example, speed fluctuations caused by load mutation would restrict the control stability, thereby limiting the usefulness of PMSM in high-precision applications. The speed regulation problem of PMSM servo control systems is discussed in this paper. A sliding mode disturbance control is developed in the vector control system to improve tracking performance of the PMSM system in order to suppress the speed fluctuations. The integration of sliding mode control and the proportional plus integral (PI) control can improve the performance of the closed-loop system and attenuate disturbances to a great extent. The proposed method can effectively improve the robustness and response speed of the system. Simulation and experimental analyses are conducted to demonstrate the superior properties of the proposed control method.

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Most research regarding multi-target, multi-base, and multi-unmanned aerial vehicle (UAV) coordinated combat mission planning faces the problems of ignoring heterogeneous UAVs, as well as poor task allocation and trajectory planning coupling. To solve these problems, based on air maneuver combat mission backgrounds, the present paper provided a heterogeneous multi-UAV cooperative mission planning method in the complex three-dimensional (3D) mountain environment. In the present paper, based on the Life-cycle Swarm Optimization (LSO) algorithm, varying the number of individuals in the population was utilized to improve the algorithm and further combined with the Rapidly exploring Random Tree (RRT) algorithm to obtain an optimized path. Then, an improved algorithm was utilized for task allocation and trajectory optimization, and the number and speed of drones dispatched by each base were determined regarding time coordination. Finally, a simulation experiment was conducted. Numerical simulation results showed that the following algorithm was compared with the Particle Swarm Optimization (PSO) algorithm and the Whale Optimization Algorithm (WOA) when considering radar threats and solid obstacle areas. This has good approximation and high convergence accuracy, and it was effectively utilized in the planning of UAV collaborative missions in 3D complex terrain environments.