Identification and adaptation of linear look-up table parameters using an efficient recursive least-squares technique

Area of Science:

• Engineering
• Control Systems
• Automotive Engineering

Background:

• Look-up tables are crucial for modeling complex nonlinear system dynamics.
• Traditional calibration methods are often slow, error-prone, and require extensive parameters.
• Experimental data noise and uncertainties complicate parameter identification.

Purpose of the Study:

• To develop a computationally efficient method for identifying and adapting look-up table parameters.
• To enable real-time adaptation of look-up tables within embedded systems.
• To apply the novel method to gasoline engine volumetric efficiency modeling.

Main Methods:

• A novel recursive least-squares approach is presented.
• The method exploits the inherent structure of look-up table equations for efficiency.
• The technique is designed for implementation in resource-constrained embedded applications.

Main Results:

• The new method offers significant computational and memory efficiency compared to standard algorithms.
• The technique was successfully applied to identify the volumetric efficiency look-up table for a gasoline engine.
• Online adaptation of table parameters was demonstrated using real-time sensor feedback.

Conclusions:

• The proposed method provides an efficient and effective solution for look-up table parameter identification and adaptation.
• This approach is suitable for real-time applications in automotive engine control.
• The technique enhances the accuracy and adaptability of engine fueling strategies.