  Heavy computational load for solving nonconvex problems for large-scale
systems or systems with real-time demands at each sample step has been
recognized as one of the reasons for preventing a wider application of
nonlinear model predictive control (NMPC). To improve the real-time feasibility
of NMPC with input nonlinearity, we devise an innovative scheme called
cloud-based computational model predictive control (MPC) by using an
elaborately designed parallel multi-block alternating direction method of
multipliers (ADMM) algorithm. This novel parallel multi-block ADMM algorithm is
tailored to tackle the computational issue of solving a nonconvex problem with
nonlinear constraints.
