With the rapid growth in the amount of computations that need to be performed by modern electronic control units and in the complexity of the algorithms, there is a pressing need to develop approaches to reduce chronometric loading in automotive vehicles. This paper presents a cyberphysical systems framework for the development of stochastic optimal decision policies that trigger the computations online. For a specific case study, we consider online triggering of the computations involved in obtaining a linearized model in a setting when such a linearized model is required by control or estimation algorithms. The objective is to define a policy for triggering the linearization that balances the average model accuracy (or expected closed loop performance) with the average computational cost. The problem is formulated as a stochastic optimal control problem and solved using stochastic dynamic programming (SDP). The approach is described, then illustrated with three examples, a pendulum, a turbocharged diesel engine, and a turbocharged spark ignition engine, that illustrate the trade-off between the computational cost and expected linearized model accuracy.
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