Title :
Adaptive Controller and Observer for a Magnetic Microrobot
Author :
Arcese, Laurent ; Fruchard, Matthieu ; Ferreira, Andre
Author_Institution :
CReSTIC EA 3804, Univ. of Reims Champagne-Ardenne, Reims, France
Abstract :
This paper discusses the control design of a magnetically guided microrobotic system in blood vessels to perform minimally invasive medical procedures. Such microrobots consist of a polymer-bonded aggregate of nanosized ferromagnetic particles and a possible payload that can be propelled by the gradient coils of a magnetic device. A fine modeling is developed and used to define an optimal trajectory which minimizes the control efforts. We then synthesize an adaptive backstepping law that ensures a Lyapunov stable and fine tracking, despite modeling errors, and estimates some key uncertain parameters. As the controller synthesis uses the microrobot unmeasured velocity, the design of a high-gain observer is also addressed. Simulations and experiment illustrate the robustness to both noise measurement and some uncertain physiological parameters for a 250-μm radius microrobot that navigates in a fluidic environment.
Keywords :
Lyapunov methods; adaptive control; aggregates (materials); blood vessels; coils; control nonlinearities; control system synthesis; ferromagnetic materials; magnetic devices; medical robotics; microrobots; nanoparticles; noise measurement; observers; optimal control; parameter estimation; polymers; robust control; surgery; Lyapunov stability; adaptive backstepping law synthesis; adaptive controller; blood vessels; controller synthesis; fluidic environment; gradient coils; high-gain observer design; magnetic device; magnetically guided microrobotic system control design; microrobot unmeasured velocity; minimally invasive medical procedures; nanosized ferromagnetic particles; noise measurement; optimal trajectory; polymer-bonded aggregate; size 250 mum; tracking; uncertain parameters; uncertain physiological parameters; Adaptive backstepping; high-gain observer; magnetic microrobot; noise and parametric uncertainties; nonlinear modeling;
Journal_Title :
Robotics, IEEE Transactions on
DOI :
10.1109/TRO.2013.2257581