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Title:
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Online identification and nonlinear control of the electrically stimulated quadriceps muscle
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Permalink:
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http://hdl.handle.net/10446/20879
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| Suggested citation: | PREVIDI, FABIO, AA.VV., (2005). Online identification and nonlinear control of the electrically stimulated quadriceps muscle. Control Engineering Practice (9/13), 1207- 1219. Retrieved from http://hdl.handle.net/10446/20879 |
| Author⁄s: | PREVIDI, FABIO
AA.VV. |
| Type of content: | journal article - articolo |
| Language: | eng |
| Date issued: | 2005 |
| Is part of: | Control Engineering Practice |
| Issue: | 9/13 |
| From p. : | 1207 |
| to p. : | 1219 |
| Keywords (Italian): | Electrical stimulation;Extended Kalman filter;Physiological model;Neural network;Nonlinear control |
| Abstract: | A new approach for estimating nonlinear models of the electrically stimulated quadriceps muscle group under nonisometric conditions is investigated. The model can be used for designing controlled neuro-prostheses. In order to identify the muscle dynamics (stimulation pulsewidth—active knee moment relation) from discrete-time angle measurements only, a hybrid model structure is postulated for the shank-quadriceps dynamics. The model consists of a relatively well known time-invariant passive component and an uncertain time-variant active component. Rigid body dynamics, described by the Equation of Motion (EoM), and passive joint properties form the time-invariant part. The actuator, i.e. the electrically stimulated muscle group, represents the uncertain time-varying section. A recursive algorithm is outlined for identifying online the stimulated quadriceps muscle group. The algorithm requires EoM and passive joint characteristics to be known a priori. The muscle dynamics represent the product of a continuous-time nonlinear activation dynamics and a nonlinear static contraction function described by a Normalised Radial Basis Function (NRBF) network which has knee-joint angle and angular velocity as input arguments. An Extended Kalman Filter (EKF) approach is chosen to estimate muscle dynamics parameters and to obtain full state estimates of the shank-quadriceps dynamics simultaneously. The latter is important for implementing state feedback controllers. A nonlinear state feedback controller using the backstepping method is explicitly designed whereas the model was identified a priori using the developed identification procedure. |
| In: | Journal contributions - Contributi su rivista scientifica
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