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.
This paper reports on an investigation of feedback control of coronal plane posture in paraplegic subjects who stand using functional neuromuscular stimulation (FNS). A feedback control system directed at regulating coronal plane hip angle in neutral position was designed, implemented, and evaluated in two paraplegic subjects. The control system included sensor mounting and signal processing techniques, a two-stage feedback controller, stimulation hardware, and a set of percutaneous intramuscular electrodes. The feedback controller consisted of two-stages in cascade: a modified discrete-time proportional-integral-derivative (PID) stage and a nonlinear single-input, multiple-output stage to determine the stimulation to be sent to several muscles. The focus of this work was on evaluating the performance of the feedback controller by comparing the response of the feedback-controlled system to that of an open-loop stimulation system. In an evaluation based on temporal response characteristics the controlled system exhibited a 41% reduction in root-mean-squared (rms) error (where error is defined as the deviation from the desired angle), a 52% reduction in steady-state error, and a 22% reduction in hip compliance. In addition, the feedback-controlled system exhibited significant reductions in variability of these measures on several days. These results demonstrate the ability of the feedback controller to improve the temporal response characteristics of the FNS control system.
Muscle input/output models incorporating activation dynamics, moment-angle, and moment-velocity factors are commonly used to predict the moment produced by muscle during nonisometric contractions; the three factors are generally assumed to be independent. We examined the ability of models with independent factors, as well as models with coupled factors, to fit input/output data measured during simultaneous modulation of the fraction of muscle stimulated (recruitment) and joint angle inputs. The models were evaluated in stimulated cat soleus muscles producing ankle extension moment, with regard to their potential applications in neuroprostheses with either fixed parameters or parameter adaptation. Both uncoupled and coupled models predicted the output moment well for random angle perturbation sizes ranging from 10 degrees to 30 degrees. For the uncoupled model, the best parameter values depended on the range of perturbations and the mean angle. Introducing coupling between activation and velocity in the model reduced this parameter sensitivity; one set of model parameter values fit the data for all perturbation sizes and also fit the data under isometric or constant stimulation conditions. Thus, the coupled model would be the most appropriate for applications requiring fixed parameter values. In contrast, with continuous parameter adaptation, errors due to changing test conditions decreased more quickly for the uncoupled model, suggesting that it would perform well in adaptive control of neuroprostheses.
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