Fractional order controllers increase the robustness of closed-loop deep brain stimulation systems. (November 2020)

• Record Type: Journal Article
• Authors: Coronel-Escamilla, Antonio; Gomez-Aguilar, Jose Francisco; Stamova, Ivanka; Santamaria, Fidel
• Is Part Of: Chaos, solitons and fractals. Volume 140(2020)
• Publication Date: 2020-11
• Subjects: Control theory -- Fractional order calculus -- Basal ganglia -- Motor disorders -- Lyapunov-stability; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2020.110149
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: Fractional-order controllers allow to implement robust closed-loop DBS systems. Fractional-order PID controllers are more robust than classical PID controllers. Fractional-order dynamics have an intrinsic memory trace of past activity. DBS with fractional-order controllers can treat Parkinson's disease condition. Abstract: We studied the effects of using fractional order proportional, integral, and derivative (PID) controllers in a closed-loop mathematical model of deep brain stimulation. The objective of the controller was to dampen oscillations from a neural network model of Parkinson's disease. We varied intrinsic parameters, such as the gain of the controller, and extrinsic variables, such as the excitability of the network. We found that in most cases, fractional order components increased the robustness of the model multi-fold to changes in the gains of the controller. Similarly, the controller could be set to a fixed set of gains and remain stable to a much larger range, than for the classical PID case, of changes in synaptic weights that otherwise would cause oscillatory activity. The increase in robustness is a consequence of the properties of fractional order derivatives that provide an intrinsic memory trace of past activity, which works as a negative feedback system. Fractional order PID controllers could provide a platform to develop stand-alone closed-loop deep brain stimulation systems.