A Study On Dynamic Air-fuel Ratio Modeling Method And Feedforward MAP Correcting Method For A Coal-bed Gas Engine

The electronic control technique is an important way to heighten combustion efficiency, reduce exhaust gas and improve power performance for a coal-bed gas engine. During analysising and designing the control system, the study on dynamic air fuel ratio modeling method and feedforward MAP correcting method for a coal-bed gas engine are the key content, and it is also a basis for optimal design of control softwares and development of the control strategy in an electronic control unit.The main work as follows:(1) In order to describe the dynamic characteristic of air fuel ratio for the coal-bed gas engine, multivariable block-link model of the air fuel ratio was constructed. The polynomical fitted with the steady-state operation experiment data was used to compensate for static nonlinear gain of the model. By means of three kinds of criterione functions, the order of the dynamic model was chosen. Based on the dynamic operation experiment data, the parameters of the dynamic model were identified by the Steiglitz-McBride (SM) method and the output error method (OE). Model validation results show that a block-link model based on SM algorithm can capture transient excursion of air-fuel ratio more accurately.(2) In order to describe the strong non-linear characteristics and to compensate for the delay of the coal-bed gas engine during dynamic operation conditions, based on subtractive clustering algorithm (SCA) and online clustering algorithm (OCA), the ANFIS model and adaptive fuzzy model for air fuel ratio feedback control were constructed, respectively. Using dynamic operation experiment data with the same direction excitation and reverse direction excitation, the predicting capability of air fuel ratio was verified and cross-validated. Results show that although two models can compensate for the different engine delays and accurately describe transient excursion of exhaust air-fuel ratio, due to using recursive algorithms for parameters and appropriate choice for rule radius, and by means of online correction and adjustment, the adaptive model based on on-line clustering algorithm can better predict the real-time changes of air fuel ratio, and has higher accuracy.(3) In order to optimize fuzzy partition space and further improve the modeling precision, considering the relevance of cluster centers and introducing collaborative coefficients to G-K clustering algorithm, based on G-K collaborative clustering algorithm, the air fuel ratio fuzzy model was built with the combination of system performance index and three kinds of clustering evaluation criteria (SC,S,XB). The model validation results show that the fuzzy model based on G-K collaborative clustering algorithm has better robustness than that based on G-K clustering algorithm, and is more suitable as an air fuel ratio dynamic model.(4) In order to eliminate steady control diviation and optimize the engine control MAP as much as possible, by means of an identified steady-state model, the self-learning correction algorighms for steady-state air fuel ratio control were examined based on PI,PID and adaptive PID iterative lerning control law. To compensate for transient state excursion of air fuel ratio resulting from dynamic non-linearityand delays, with the help of an air fuel ratio dynamic model with G-K collaborative clustering algorithm, the dynamic correcting method for air fuel ratio were studied based on PID and adaptive PID iterative learning control law. Simulation results show that adaptive PID learning control algorithm has the fastest convergence rate and minimum learning error, and is much more suitable to online learning and adjusting position control parameters for the coal-bed gas engine. Through error feedback learning and real-time tracking, the method based on adaptive PID lerning congrol algorithm has better convergency and ability to control transient air fuel ratio.