Neural-network-based car drive train control

The optimization of the drive train string for high comfort requirements involves several control problems. One is the determination of optimal torque trajectory. Classical solutions of this problem suffer from strongly nonmonotonic torque trajectory, resulting from the difficulty of formulating the monotonicity requirement in the classical quadratic objective function form. A model-based neural-network trainable controller has been applied to this problem. In contrast to previous neural-network approaches, it fully exploits the available information about the plant. By its capability of using an arbitrary nonlinear differentiable control objective function (as well as an arbitrary nonlinear differentiable plant), it allows a direct formulation of the torque monotonicity requirement. The development time for the controller has been only two days. No control engineering competence has been required-the design procedure is very general and automatic