Nonlinear model reference adaptive control using tap-delay filters

1992 ◽  
Vol 22 (2) ◽  
pp. 360-368 ◽  
Author(s):  
F. Bolourchi ◽  
R.A. Hess
Keyword(s):  
Adaptive Control ◽  
Nonlinear Model ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
Model Reference Adaptive

A New Impedance Controller Based on Nonlinear Model Reference Adaptive Control for Exoskeleton Systems

2019 ◽  
Vol 16 (05) ◽  
pp. 1950020
Author(s):  
Kai Gui ◽  
U-Xuan Tan ◽  
Honghai Liu ◽  
Dingguo Zhang
Keyword(s):  
Adaptive Control ◽  
Nonlinear Model ◽  
Mechanical Impedance ◽  
Model Reference Adaptive Control ◽  
Model Reference ◽  
High Compliance ◽  
Adaptive Law ◽  
Simulation And Experiment ◽  
Novel Method ◽  
Model Reference Adaptive

Robotic exoskeletons are expected to show high compliance and low impedance for human–robot interactions (HRIs). Our study introduces a novel method based on nonlinear model reference adaptive control (MRAC) to reduce the inherent impedance and replace the traditional impedance controller in HRIs. The control law and adaptive law are designed according to a candidate Lyapunov function. A simple system identification and initialization method for the nonlinear MRAC is put forward, which provides a set of better initial values for the controller. From the results of simulation and experiment, our controller can reduce the mechanical impedance and achieve high compliance for HRI. The adaptive control and compliance control can be both achieved by the proposed nonlinear MRAC framework.

scholarly journals Nonlinear model reference adaptive control

1986 ◽  
Vol 28 (2) ◽  
pp. 147-157 ◽  
Author(s):  
J. M. Skowronski
Keyword(s):  
Adaptive Control ◽  
Nonlinear Model ◽  
Degrees Of Freedom ◽  
Control Program ◽  
Model Reference Adaptive Control ◽  
Design Technique ◽  
Model Reference ◽  
State Spaces ◽  
Adaptive Law ◽  
Model Reference Adaptive

AbstractThe known linear model reference adaptive control (MRAC) technique is extended to cover nonlinear and nonlinearizable systems (several equilibria, etc) and used to stabilize the system about a model. The method proposed applies the same Liapunov Design Technique but avoids the classical error equation. Instead it operates in the product of the state spaces of plant and model, aiming at convergence to a diagonal set. Control program, Liapunov functions and adaptive law are specified. The case is illustrated on a two-degrees of freedom robotic manipulator.

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