Interval Observer-based Feedback Control for Rehabilitation in Tremor

Abstract

Tremor, one of the most common health disorders, is defined as a chronic movement disorder. To reduce tremor in patients, the design of stabilizing techniques is critical. For this purpose, we consider an uncertain continuoustime linear time-varying oscillator model of tremor in this article. We design a state feedback control for deep brain stimulation technique by considering the practical case in which only sets of admissible values are given for the nominal values of the stimulation amplitude, the time-varying tremor's angular frequency and the tremor measurement noise. First, we estimate state signal bounds that include the true unknown value of the state. Next, we design a stabilizing control input based on the estimated bounds. The stability of the controlled system is verified using linear matrix inequalities (LMIs). We demonstrate the methodology's performance in numerical simulations.

Advanced non-linear control based on artificial intelligence tuner for hand tremor suppression

“…The kinematic models generally refer to the study of properties of motion, position, velocity, and acceleration. So, it can be considered as a general dynamic model to implement and tested the intelligence closed-loop DBS system (Degue et al, 2020). In the advanced closed-loop DBS structure, hand tremor amplitude serves as the system's input signal (feedback).…”

Design of Interval Observers for Uncertain Linear Impulsive Systems

“…However, for industrial systems, the state disturbances and measurement noises can often be modeled as bounded signals, whereas their statistical properties are harder to estimate. Interval estimators [3], [4] are then advantageous, as they can handle the presence of disturbances whose values are only known to belong to given intervals or polytopes, and many types of bounded uncertainties more generally.…”