Pointwise-in-time state and control constraints represent some of the key challenges in many automotive powertrain control problems. Although for specific applications the engineers are usually successful in treating the constraints on a case-by-case basis, systematic control system design techniques that deal with constraints are of significant interest, and they hold promise to greatly reduce the development time and effort.
In particular, Model Predictive Control (MPC) provides a flexible and powerful framework for enforcing constraints while optimizing system performance. The MPC is based on an on-line dynamic optimization of the control input subject to constraints, over a receding horizon. By augmenting an MPC controller with on-line parameter estimation and accounting upfront for uncertainties and unmeasured disturbances in its design, robust constraint enforcement can be guaranteed At the same time, for memory and chronometrics limited automotive microcontrollers implementing a general MPC controller can be intricate. Suboptimal schemes that apply on-line optimization only to selected parameters in the nominal control laws can reduce the computational requirements and deal effectively with pointwise-in-time constraints. These reduced complexity embedded optimization (EO) algorithms are referred to as parameter governors.
The talk will start by reviewing some of the powertrain control applications in which dealing with constraints is an important priority. The parameter governors and their theoretical properties will be described next and illustrated with several examples. The results will be specialized to three classes of parameter governors that include reference governors, feed-forward governors and gain governors. Other applications of parameter governing-like ideas to on-line parameter estimation will be touched upon.