Adaptive critic learning techniques for automotive engine control

A new approach for automotive engine torque and air-fuel ratio control is presented in this paper. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in the present project. Adaptive critic designs are defined as designs that approximate dynamic programming in the general case, i.e., approximate optimal control over time in noisy, nonlinear environment. The present work uses a system, called "critic", to approximate the cost function in dynamic programming and thus to achieve optimal control. The goals of the present learning control design for automotive engines are emissions reduction and maintenance of optimal performance under various operating conditions. Using the data obtained from a test vehicle, we first develop a neural network model of the engine. A neural network controller is then designed based on the idea of approximate dynamic programming to achieve optimal control. In the simulation studies, the initial controller is trained using the neural network engine model developed rather than the actual engine. We have developed and tested self-learning neural network controllers for both engine torque and exhaust airfuel ratio control. For both control problems, good transient performance of the neural network controller has been observed. A distinct feature of the present technique is the controller\´s real-time adaptation capability based on real vehicle data which allows the neural network controller to be further refined and improved in real-time vehicle operation through continuous learning.