Optimized Control of PMSM-Quadrotor UAV System

Quadrotor Unmanned Aerial Vehicles (UAVs) are flying mechatronic systems equipped with propellers, motors, electronic speed controllers, and sensors integrated into their cross-shaped structure. Quadrotor UAVs are widely recognized as the most promising configuration of UAV for their controllability and relatively low cost. Among brushless motors, Permanent Magnet Synchronous Motors (PMSMs) are remarkably dependable and efficient. Without compromising the torque generation capability, PMSMs can help reduce the size of the platform thanks to their power-to-size ratio. PMSMs enhance UAV control by generating thrust and improving maneuverability. The literature rarely considers motor dynamics even though it is crucial for the development of the drone digital twin. This paper investigates the performance of a quadrotor equipped with four PMSMs, focusing on its behavior during hovering and maneuvering. Controlling UAVs with PMSMs involves controlling motors’ speed to control the drone's direction, this is typically achieved with a series of decentralized Proportional-Integral-Derivative (PID) controllers. To further optimize the performance of the closed-loop system, a Particle Swarm Optimization (PSO) algorithm is designed to tune the parameters of each PID controller. The quadrotor model is derived adopting the Newton-Euler approach and is intended to be constituted by four three-phase PMSMs controlled with a velocity control loop-based Field Oriented Control (FOC) technique. The PSO algorithm is used to tune the parameters of the PID controllers of quadrotor height, quadrotor attitude angles, and PMSMs rotational speeds which represent the eight critical parameters of the PMSM-quadrotor UAV system. The PSO algorithm is designed to optimize eight Square Error (SE) cost functions which quantify the error dynamics of the controlled variables. For each stabilization task, the PID tuning is divided in two phases. Firstly, the PSO optimizes the error dynamics of altitude and attitude angles of the quadrotor UAV. Secondly, the desired steady-state rotational speeds of the PMSMs are derived, and the PSO is used to optimize the motors dynamics. Finally, the complete PMSM quadrotor UAV system is simulated for stabilization during the target task. The study is carried out by means of simulations in MATLAB/Simulink. The validity of the approach is corroborated by extensive simulation campaigns with different initial conditions for both hovering and maneuvering tasks. By exploiting the PSO algorithm for the optimal tuning of all the PID controllers of the system it is possible to comprehensively simulate the PMSM-quadrotor UAV system and optimize the performances.