Data-Driven Active Disturbance Rejection Control of Plant-Protection Unmanned Ground Vehicle Prototype: A Fuzzy Indirect Iterative Learning Approach
Tao Chen, Ruiyuan Zhao, Jian Chen, Zichao Zhang
Abstract
Dear Editor, This letter proposes a fuzzy indirect iterative learning (FIIL) active disturbance rejection control (ADRC) scheme to address the impact of uncertain factors of plant-protection unmanned ground vehicle (UGV), in which ADRC is a data-driven model-free control algorithm that only relies on the input and output data of the system. Based on the established nonlinear time-varying dynamic model including dynamic load (medicine box), the FIIL technology is adopted to turn the bandwidth and control channel gain online, in which the fuzzy logic system is used to update the gain parameters of iterative learning in real time. Simulation and experiment show the FIIL-ADRC scheme has better control performance.