改进的四旋翼单神经元PID控制器

Improved Four-Rotor Single Neuron PID Controller

姿态控制是四旋翼飞行器的重要组成部分,是能够在空中平稳飞行的前提。但传统控制器为PID控制,其参数不能在线调整,影响调整品质。单神经元PID虽具有自学习和自适应的能力,但其调节参数会造成超调量过大、响应速度变差问题。因此,本文引入了量子粒子群算法进行参数优化,可以通过自身粒子的不断更新最终筛选出符合调节的粒子,能够有效解决单神经元PID存在的调节问题。通过搭建MATLAB/SIMULINK模型,并与单神经元PID控制器比较,仿真结果验证了本文设计控制器的有效器具有较好的响应速度、抗干扰性、鲁棒性。

Attitude control is an important part of the four rotor aircraft,and it is a prerequisite for smooth flight in the air.However,the traditional controller is controlled by PID,and its parameters can not be adjusted online,affecting the quality of adjustment.Although single neuron PID has the ability of self-learning and self-adaptation,its adjusting parameters will cause overlarge overshoot and poor response speed.Therefore,the introduction of quantum particle swarm optimization algorithm to optimize the parameters,through their own particle updates can eventually screen out the particles that meet the adjustment and effectively solve the adjustment problems of single neuron PID.By building the MATLAB/SIMULINK model and comparing with the single neuron PID controller,the simulation results verify that the controller has better response speed,anti-interference and robustness.