Computers and Electronics in Agriculture vol:98 pages:25-33
Controllers working in uncertain environments are often required to adapt themselves continuously to changing conditions to avoid steady-state errors, oscillations at the output or even instability of the closed loop system. The moving horizon estimation (MHE)–nonlinear model predictive control (NMPC)framework being proposed combines these two optimization-based methods to control field vehicles utilizing an adaptive nonlinear kinematic model. The full system state, including two unknown slip parameters
and the unmeasurable vehicle orientation, is estimated by the MHE after each new measurement and fed afterwards to the NMPC routine which provides a wheel velocity and a steering rate to follow arbitrary time-based reference trajectories in difficult environmental conditions. This control problem
occurs in modern agriculture e.g. in planting or mechanical weeding while slippery conditions make these operation difficult and off-track navigation results in plant damage. The experimental results show accurate reference tracking performance of the MHE–NMPC framework on a wet and bumpy grass field.
The feedback times lie in the range of 0.6–1.6 ms when the ACADO Code Generation tool is used, which is part of the open-source software toolkit ACADO.