Neural network control of functional neuromuscular stimulation systems: computer simulation studies.
A neural network control system has been designed for the control of cyclic movements in Functional Neuromuscular Stimulation (FNS) systems. The design directly addresses three major problems in FNS control systems: customization of control system parameters for a particular individual, adaptation during operation to account for changes in the musculoskeletal system, and attaining resistance to mechanical disturbances. The control system was implemented by a two-stage neural network that utilizes a combination of adaptive feedforward and feedback control techniques. A new learning algorithm was developed to provide rapid customization and adaptation. The control system was evaluated in a series of studies on a computer simulated musculoskeletal model. The model of electrically stimulated muscle used in the study included nonlinear recruitment, linear dynamics, and multiplicative nonlinear torque-angle and torque-velocity scaling factors. The skeletal model consisted of a one-segment planar system with passive constraints on joint movement. Results of the evaluation have demonstrated that the control system can provide automated customization of the feedforward controller parameters for a given musculoskeletal system. It can account for changes in the musculoskeletal system by adapting the feedforward controller parameters on-line and it can resist the effects of mechanical disturbances. These results suggest that this design may be suitable for the control of FNS systems and other dynamic systems.
D000222:Adaptation, Physiological; D000465:Algorithms; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004599:Electric Stimulation Therapy / Q000379:methods*; D005246:Feedback; D006801:Humans; D009422:Nervous System Diseases / Q000503:physiopathology / Q000628:therapy*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013813:Therapy, Computer-Assisted / Q000379:methods*