Design environment for nonlinear model predictive control


Model Predictive Control (MPC) design methods are becoming popular among automotive control researchers because they explicitly address an important challenge faced by today’s control designers: How does one realize the full performance potential of complex multi-input, multi-output automotive systems while satisfying critical output, state and actuator constraints? Nonlinear MPC (NMPC) offers the potential to further improve performance and streamline the development for those systems in which the dynamics are strongly nonlinear. These benefits are achieved in the MPC framework by using an on-line model of the controlled system to generate the control sequence that is the solution of a constrained optimization problem over a receding horizon. Motivated by the application of NMPC to the Diesel engine air path control problem, we present a control design environment that leverages Maple’s symbolic computation engine to facilitate NMPC problem formulation, solution, and C code-generation. Given the limited on-line computational resources available for automotive control implementation and the dependence of effective NMPC problem formulation and solution on the application at hand, the designer needs to be able to fully explore the NMPC formulation / solution / computation cost design space. Thus our Symbolic Computing Design Environment (SCDE) for NMPC is constructed so that the designer can rapidly evaluate the performance and computation cost of several implementation options. In particular, we show by example how SCDE can be used to choose between numeric and symbolic solutions approaches and reduce NMPC computation cost by generating functions from the controller’s equations that re-use the sub-expressions common to different aspects of the solution.

SAE Technical Paper