Nonlinear parametric identification of a McKibben artificial pneumatic muscle using flatness property of the system

We present an approach to estimating the parameters in a nonlinear model of the studied system. This approach uses a classic nonlinear estimator algorithm (damped Gauss-Newton in our case), applied to a flat differential model of a process. The method is applied to an artificial pneumatic muscle, and compared with standard nonlinear identification method. For both cases, identification results are given and discussed