Adaptive neural network model based predictive control of an internal combustion engine with a new optimization algorithm

Abstract

This paper presents an application of adaptive neural network modelling and model-based predictive control (MPC) for an engine simulation. A radial basis function (RBF) neural network trained by a recursive least-squares (RLS) algorithm is compared with the network with fixed parameters and demonstrated to be more suitable for modelling the crankshaft speed, the intake manifold pressure, and the manifold temperature. Based on the obtained adaptive neural network model, an MPC strategy for controlling the crankshaft speed is realized successfully. A reduced Hessian method, a newly developed sequential quadratic programming (SQP) method for solving non-linear programming (NLP) problems, is implemented to solve the non-linear optimization in MPC. Some important modifications are proposed for the algorithm settings in this research to make the reduced Hessian method more appropriate for the adaptive neural network model based predictive control strategy of internal combustion (IC) engines.