Nanopositioning systems are very popular and playing an increasingly vital role in micro and nano-scale positioning industry due to their unique ability to achieve high-precision and high-speed operation. However, hysteresis, commonly existing in piezoelectric actuators, degrades the precision seriously. Uncertain dynamics and sensor noises also greatly affect the accuracy. To address those challenges, a variable bandwidth active disturbance rejection control (VBADRC) is proposed and realized on a nanopositioning stage. All undesired issues are estimated by a time-varying extended state observer (TESO), and cancelled out by a variable bandwidth controller. Convergence of the TESO, advantages of a TESO over a linear extended state observer (LESO), and the closed-loop stability of the VBADRC are proven theoretically. Improvements of the VBADRC versus the linear active disturbance rejection control (LADRC) are validated by simulations and experiments. Both numerical and experimental results demonstrate that the VBADRC is not only able to provide the same disturbance estimation ability as the LADRC, but also more powerful in noise attenuation and reference tracking.
Epilepsy is one of the most common neurological disorders. Neuro-modulation becomes a promising way to address it. For an effective modulation, closed-loop mode is necessary but difficult. A control algorithm, which can adjust itself to get desired suppression of epileptic activity, is in great need. In this paper, active disturbance rejection control (ADRC) is utilized for its satisfied disturbance rejection and regulation performance. However, fixed observer parameters are difficult to fit the time-varying electrophysiological signals. Therefore, based on the estimation errors, an iterative learning approach is designed to get the parameters of an extended state observer (ESO). By combining the advantages of ADRC and the iterative learning, a learning type ADRC (LTADRC) is proposed to suppress the high amplitude epileptiform waves generated by the Jansen's neural mass model (NMM). For those variable parameters of an ESO, scalable bandwidths can be obtained to adapt to time-varying disturbance signals. It is of great significance for both ADRC and the neuro-modulation of epilepsy. Simulation results show that, compared with ADRC, much better performance can be obtained. It may provide a promising closed-loop regulation way for epilepsy in clinics. INDEX TERMS ADRC, a learning type ADRC, epilepsy, NMM, closed-loop modulation.
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