@conference {MED08-HTB,
	title = {Neural network based robust adaptive control for a variable stiffness actuator},
	booktitle = {Control and Automation, 16 th Mediterranean Conference on Control and Automation},
	year = {2008},
	pages = {1028 - 1034},
	abstract = {<p>In this paper we present a robust adaptive controller based on a neural network (NN) for a variable stiffness actuator (VSA). The controller is able to independently set the mechanical stiffness and position at the joint shaft to guarantee robustness with respect to slowly time-varying and unmodeled friction coefficients affecting the dynamics of the actuator. The lumped uncertainties of the VSA including unmodeled dynamics are considered and approximated by a simple NN so that the controlled system is asymptotically stable, and remains effective while process conditions vary. To cope with the reconstruction error of the NN, a sliding mode like additional robust control term is introduced. The proofs for the uniformly ultimately bounded (UUB) and uniform asymptotic (UAS) stabilities for the closed-loop system are provided in detail via Lyapunov theory. Simulation and experimental results are reported in support of both validity and performance of the proposed approach.</p>
},
	keywords = {Embedded Control, Robotics},
	author = {S. Huh and G. Tonietti and A. Bicchi}
}
@article {SHH,
	title = {Robust sliding mode control of a robot manipulator based on variable structure-model reference adaptive control approach},
	journal = {IET Control Theory Appl.},
	volume = {1},
	number = {5},
	year = {2007},
	month = {September 2007},
	pages = {-1363},
	abstract = {<p>A model reference adaptive-sliding mode control is presented and applied for a variable stiffness actuated (VSA) system. The VSA is a flexible stiffness machine with two coupled actuators in each link, and its safety during the movement can be guaranteed by varying both stiffness and the angular variables. Realisation of precise control of two coupled actuators poses a considerable challenge, however, because of uncertain time-varying parameters and unknown variation bounds. In this paper a neuro-sliding mode approach based on model reference adaptive control (MRAC) is proposed. The proposed MRAC control structure induces the VSA to follow its nominal dynamics with help of sliding mode control efforts. The sliding gain, implemented by a simple neural network (NN), is adaptively updated based on the Lyapunov criterion. A control law and adaptive laws for the sliding mode control as well as the weights in the NN are established so that the closed-loop system is stable in the sense of Lyapunov. The tracking errors of both the angular variables and stiffness are managed to guarantee the system to be asymptotically stable rather than uniformly ultimately bounded. And, the feasibility of the proposed control approach is demonstrated by means of experimental results as well as computer simulations.</p>
},
	keywords = {Physical Human-Robot Interaction (pHRI), Robotics},
	author = {S. Huh and Z. Bien}
}
