Closed-loop seizure modulation via extreme learning machine supervisor based sliding mode disturbance rejection control

Abstract

Neuromodulation is a low-risk and high-efficient therapy to treat epilepsy. In clinic, there is an urgent need for a regulation strategy that is adaptable to unknown nonlinearities and strong robust to kinds of disturbances and uncertainties. Linear active disturbance rejection control (LADRC) can adapt to complex epileptic dynamics and improve the epilepsy modulation, even if little model information is available, various uncertainties and external disturbances exist. However, a proportional plus derivative controller in the LADRC is weak to resist external disturbances that are not addressed by an extended state observer. In addition, the phase delay of the input and output lowers the speed of modulation. An extreme learning machine (ELM) based supervisor can get an inversion of the plant timelier and more accurately, and an ELM supervisor based sliding mode disturbance rejection control (ESSMDRC) is proposed to improve both speed and robustness of the modulation. Closed-loop stability and the phase-leading property are analysed. Numerical results show that the proposed ESSMDRC guarantees a more satisfactory closed-loop neuromodulation.