Parameter Tuning of Linear Active Disturbance Rejection Controller for Altitude Based on Particle Swarm Optimization

Abstract

The flight dynamics of quadrotor unmanned aerial vehicle (UAV) is well known for its highly nonlinearity, strong coupling, and model uncertainties. Linear active disturbance rejection control (LADRC) framework with particle swarm optimization (PSO) tuning algorithm is proposed in this paper. The unique way for LADRC to handle total disturbances, including internal and external disturbances, is by designing the linear extended state observer (LESO) to estimate the total disturbances, which will be combined with the states nonlinearly and compensated by the nonlinear state error feedback (NLSEF) later. By using the observer bandwidth method, the number of LESO's parameters can be decreased dramatically, along with the stability analyzation of LADRC. Furthermore, PSO tuning algorithm is used to optimize the parameters of LESO and NLSEF quickly and precisely, which leads to a better control performance of the UA Vsystem. Instead of introducing other method to determine the suitable parameters of PSO algorithm, the main contribution of the research is by analyzing the effects of weight inertia and iteration, which are two main parameters of PSO algorithm, the PSO-based parameter tuning of LADRC process can be more reasonable and time-saving.