A compression algorithm for real-time distributed nonlinear MPC

Model Predictive Control (MPC) requires the online solution of an Optimal Control Problem (OCP) at each sampling time. Efficient online algorithms such as the Real-Time Iteration (RTI) scheme have been developed for real-time MPC implementations even for fast nonlinear dynamic systems. The RTI framework is based on direct Multiple Shooting (MS) for centralized systems. Distributed Multiple Shooting (DMS) is an MS-based OCP discretization strategy for distributed systems. Many fast dynamic systems can be described as connected subsystems and in order to exploit this structure, a DMS based RTI scheme has been developed and implemented in ACADO code generation. A novel technique called compression is proposed to reduce the dimensions of the convex subproblem, while exploiting the coupling structure. The performance of the presented scheme is illustrated on a nontrivial example from the literature, where a speedup of factor 11 in simulation time and factor 6 in the total computation time can be shown over the classical RTI scheme.