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access icon free Designing a robust backstepping controller for rehabilitation in Parkinson's disease: a simulation study

© The Institution of Engineering and Technology
Received 06/05/2015, Accepted 15/12/2015, Revised 19/11/2015, Published 01/08/2016

In this study, a model of basal ganglia (BG) is applied to develop a deep brain stimulation controller to reduce Parkinson's tremor. Conventionally, one area in BG is stimulated, with no feedback, to control Parkinson's tremor. In this study, a new architecture is proposed to develop feedback controller as well as to stimulate two areas of BG simultaneously. To this end, two controllers are designed and implemented in globus pallidus internal (GPi) and subthalamic nucleus (STN) in the brain. A proportional controller and a backstepping controller are designed and implemented in GPi and STN, respectively. The proposed controllers deliver suitable stimulatory control signals to GPi and STN based on hand tremor amplitude (as the feedback). When tremor reduces, these controllers decrease the stimulatory energy intensity proportionally. Therefore, additional stimulatory signal is not delivered to the brain. Subsequently, the side effects from the excessive stimulation intensity become much less. Comparing with one area stimulation, the results show that stimulating two areas of BG results in reduction of the level of the stimulation intensity. It is observed that these two controllers are both robust in terms of changing the system parameters.

Inspec keywords: control nonlinearities; diseases; proportional control; patient treatment; brain; medical control systems; neurophysiology; patient rehabilitation

Other keywords: hand tremor amplitude; robust backstepping controller; stimulatory energy intensity; basal ganglia; Parkinson’s disease; deep brain stimulation; proportional controller; subthalamic nucleus

Subjects: Patient care and treatment; Patient care and treatment; Nonlinear control systems; Systems theory applications in biology and medicine; Electrical activity in neurophysiological processes; Biological and medical control systems

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