改进粒子群算法在四旋翼PID参数优化中的应用

Application of Improved PSO in PID Parameter Optimization of Quadrotor Aircrafts

采用试凑方式对四旋翼飞行器PID控制参数人工进行调整工作量大、费时且难以达到较好的控制效果。为了解决控制参数优化问题,提出基于带交叉因子的粒子群算法(PSO)的PID参数优化策略。将带交叉因子的粒子群算法能快速准确找到最优参数解的特点与PID控制结合起来,在控制过程中将PID参数作为粒子群中的粒子,用遗传算法对粒子进行选择、保优、交叉,以ITAE准则作为误差性能指标,用粒子群算法调整PID参数,得出最优的粒子作为四旋翼飞行器的PID控制器参数。仿真结果显示,该方法具有更强的灵活性、适应性和鲁棒性,并能提高控制系统的精度,具有很好的工程应用价值。

Manual optimization of PID control parameter for the quadrotor aircraft is time-consuming, and it is difficult to achieve good control effect. In order to solve the problem of control parameter optimization, the strategy of PID parameter optimization of Particle Swarm Optimization （PSO） with cross factor is proposed. This strategy integrates the characteristic of PSO cross factor, which can quickly and accurately find out the optimal parameters, with PID control. During control process, the PID parameters are regarded as particles of particle swarm. The genetic algorithm is used for selecting, quality ensuring and crossing of the particles. The standard of ITAE is the performance index of error. PSO is used to adjust the PID parameters, and the optimal particles are taken as the PID parameters of the quadrotor aircraft. The simulation results show that the strategy has better flexibility, adaptability and robustness than that of the traditional PID control, and can improve the accuracy of the control system.