Closed-Loop Identification to Unravel the Way the Human Nervous System Controls Bodily Functions
The central nervous system controls body functions and initiates actions through an integrated system of multiple feedback loops. System identification can be a valuable tool to assess the system dynamics. The challenge is to assess the functioning of an intact feedback system in vivo, where cause and effect are intermingled. In this paper we show the errors which can occur when estimating system dynamics using standard (open-loop) system identification algorithms in the presence of feedback loops. Furthermore, we show how these issues can be addressed by using closed-loop algorithms. A closed-loop system identification approach is essential to assess the separate feedback loops of the central nervous system in an intact functional system.
Summary form only. The author introduces the concept of a dynamic parameter, a concept based on the fact that a component (for this study, an electric motor) possesses many attributes of a dynamic role. A system's function can be expressed as a function of the motor-related sensors, interlocks, instrumentation, and other actuating devices. This process can be used to build intelligent structures. These structures would be expert-defined and become powerful communication highways which include traditional or classical engineering relationships as well as specific new relationships defined in the present work. >