Study On Several Issues For Air-Fuel Ratio Control Of A Coal-bed Gas Engine

It is very important and significant to develop electronic control system for the coal-bed gas (CBG) engine, so as to improve CBG engine's power performance, economy and emission properties. Modeling of sensors in air-fuel ratio feedback control system and control-oriented modeling of CBG engine are necessary for analysis and design of CBG engine control system, and are basic for designing optimally control software in the engine electronic control unit and developing control strategy.Several issues related air-fuel ratio control of the CBG engine was studied in this thesis. Main contents as follows.(1) Based on data from an exhaust gas oxygen (EGO) sensor static and dynamic calibration experiment, dynamic modeling of the EGO sensor Hammerstein model was studied with the two-step identification method. Considering delay of excitation signal and using correlation analysis, sensor excitation signal under the different experimental conditions was estimated. The delay time of the EGO sensor step response at different temperatures was determined by the least square method. Stemmed from different error criterion, dynamic linear link model structure and order in Hammerstein model corresponding to delay time was chosen by crossing criterion method. According to relationship between dynamic nonlinear, temperature and delay time, a unified Hammerstein model of the EGO sensors in respect to positive and negative step response was built with BJ (Box-Jenkins) model selected as a dynamic linear link. The EGO sensors time domain and frequency domain characteristics was analyzed by this model.(2) In terms of hypothesis of average stationary intake flow, using speed -density method and experimental data from engine test stand, a hierarchical model for steady-status air fuel ratio feed-forward control was established by mean value, polynomials and fuzzy neural network modeling method. Three-dimensional original control maps were produced by the model, and data generation problem of non-experiment operation condition points was solved. Through simulation and engine steady state control experiments, the model and initial control map were verified.(3) In order to eliminate error in three-dimensional original control maps, steady-status air fuel ratio correcting algorithms with two-valves adjusting was studied adopting iteration learning control techniques. The convergence rate of different algorithms and air-fuel ratio correction effect were examined by means of an excess air ratio ANFIS model. The simulation results show that fuzzy adaptive tuning PID learning law is better than PID learning law and fuzzy learning law.(4) In accordance to the dynamic experimental data, the control-oriented dynamic model for the CBG engine was established. Dynamic modeling problem of a linear neural network model, an ANFIS model and a block-oriented model were studied, and several models were verified and compared. With reference to the air-fuel ratio feedforward - feedback control structure and dynamic model of the CBG engine, feed-forward control was based on a mean value model control algorithm, and feedback control was based on PID control algorithm, a transient air-fuel ratio control simulation was carried out.