Online learning in adaptive neurocontrol schemes with a sliding mode algorithm

The novel features of an adaptive PID-like neurocontrol scheme for nonlinea r plants are presented. The controller tuning Is based on an estimate of th e command-error on its output by using a neural predictive model. A robust online learning algorithm, based on the direct use of sliding mode control (SMC) theory is applied. The proposed approach allows handling of the plant -model mismatches, uncertainties and parameters changes, The results show t hat both the plant model and the controller inherit some of the advantages of SMC, such as high speed of learning and robustness.