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Tustin neural networks: a class of recurrent nets for adaptive MPC of mechanical systems
[article]
2019
arXiv
pre-print
The use of recurrent neural networks to represent the dynamics of unstable systems is difficult due to the need to properly initialize their internal states, which in most of the cases do not have any physical meaning, consequent to the non-smoothness of the optimization problem. For this reason, in this paper focus is placed on mechanical systems characterized by a number of degrees of freedom, each one represented by two states, namely position and velocity. For these systems, a new recurrent
arXiv:1911.01310v1
fatcat:moyed5aloregzmjibzzzto5hay