In the automotive industry the economics of the engineering design process and tighter environmental standards have led to engines and other components to be operated close to their physical limits. Continuous innovations to reduce fuel consumption and pollutant emissions while improving driveability have vastly increased the complexity of powertrain control strategies. Addressing the design of these control strategies using conventional feedback design methodologies has many drawbacks. When dealing with multi-input multi-output (MIMO) systems with nonlinear dynamics and constrained actuation, feedback design using conventional methodologies relies on manual calibration of multiple feedback loops in a process that resembles art more than science. In addition, the entire control strategy needs to be re-designed for every small change in the components being controlled. As the system complexity increases, this design methodology will not be a viable option for both economic and performance reasons.
Solvers generated by FORCES Pro enables the usage of real-time model predictive control in demanding automotive applications.
Model-based control design, and in particular model predictive control (MPC), provides a systematic control synthesis approach that results in near optimal performance in the presence of actuator limits, while significantly easing the calibration effort and reducing development time. With MPC, the effort in feedback re-design when system components change is minimal, and the same methodology can be applied to different components and engines.
In the automotive engineering design process post-design calibration of the control strategy is essential to reduce functional design iterations, which are extremely expensive and incur excessive delays. MPC is particularly well-suited for this task since it provides intuitive tuning parameters that are directly related to the different performance objectives of the system and the trade-offs between them.