01570nas a2200157 4500008004100000245008300041210006900124300001600193520099700209653002101206653001301227100001101240700001601251700001501267856013001282 2008 eng d00aNeural network based robust adaptive control for a variable stiffness actuator0 aNeural network based robust adaptive control for a variable stif a1028 - 10343 a
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.
10aEmbedded Control10aRobotics1 aHuh, S1 aTonietti, G1 aBicchi, A. uhttps://www.centropiaggio.unipi.it/publications/neural-network-based-robust-adaptive-control-variable-stiffness-actuator.html