  Machine learning technique can help reduce computational cost of model
predictive control (MPC). In this paper, a constrained deep neural networks
design is proposed to learn and construct MPC policies for nonlinear input
affine dynamic systems. Using constrained training of neural networks helps
enforce MPC constraints effectively. We show the asymptotic stability of the
learned policies. Additionally, different data sampling strategies are compared
in terms of their generalization errors on the learned policy. Furthermore,
probabilistic feasibility and optimality guarantees are provided for the
learned control policy. The proposed algorithm is implemented on a rotary
inverted pendulum experimentally and control performance is demonstrated and
compared with the exact MPC and the normally trained learning MPC. The results
show that the proposed algorithm improves constraint satisfaction while
preserves computational efficiency of the learned policy.
