Engine control unit PID controller calibration by means of local model networks

• A fully automated calibration of nonlinear PID controllers in ECUs is proposed.
• The actual physical process is represented by a dynamic local model network.
• Multi-objective optimization of controller parameters considering stability and performance.
• Static model inversion determines a feedforward map.
• The effectiveness of the proposed method is demonstrated in an example.

In this work a new approach for a fully automated calibration of nonlinear PID controllers and feedforward maps is introduced. Controller design poses a particularly challenging task in the application to internal combustion engines due to the nonlinear controller structure, which is usually prescribed by the manufacturer of the engine control unit (ECU). A dynamic local model network is used to represent the actual physical process as its architecture can beneficially be adopted for scheduling of the nonlinear controller parameters. The presented calibration technique uses a genetic algorithm to calibrate the nonlinear PID controller and a static model inversion to determine the feedforward map. Closed-loop stability is taken into account by incorporating a Lyapunov function. Finally, an example demonstrates the effectiveness of the proposed method.