Neural Dynamic Programming for Automotive Engine Control

1. Neural Dynamic Programming for Automotive Engine Control Investigator: Derong Liu, Department of Electrical and Computer Engineering Prime Grant Support: National Science Foundation and General Motors Computational Intelligence Laboratory • Automobile emissions are a major source of pollution • Exhaust air-to-fuel ratio control to reduce emission • Engine torque control to improve driveability • On-board learning to deal with vehicle aging effects • Reduced emissions - Environmental benefit • Better fuel efficiency - Economic benefit • Dynamic programming minimizes a cost function • Neural network approximation of the cost function • Neural network controller to minimize the cost function • Approximate optimal control/dynamic programming • Initial controller will be trained off-line using data • Controller is further refined through on-line learning • Controller performance is improved with experience • Self-learning controller for better transient torque • Self-learning controller for tighter air-to-fuel ratio • Neural network modeling of automotive engines • Neural network modeling of several engine components • Other potential application: Engine diagnostics • Short term goal: Collaborate with industry • Long term goal: Implement our algorithms in GM cars