% Create a 2D lookup table lut = mbc2dlookup('filename.slx', table); writeblock(lut); Or generate C-array:
writecfile(table, 'calibration_table.c'); | Object/Function | Purpose | |----------------|---------| | xydesign | Generate DOE points | | mbcdata | Manage experimental data | | mbcgp , mbcquadratic | Build models | | calset | Multi-objective optimization | | mbc2dlookup | Export to Simulink | | crossvalidate | Validate model accuracy | 4. Practical Example: Engine Calibration Goal: Calibrate spark timing for max torque while limiting knock. mcc toolbox
% 3. Build knock model (binary: 0=no knock, 1=knock) knock_model = mbcgp(data, 'Knock', 'Speed','Load','Timing', 'Distribution','binomial'); knock_model = fit(knock_model); % Create a 2D lookup table lut = mbc2dlookup('filename
% 4. Optimize timing cal = calset(torque_model, 'Goal','maximize', 'Response','Torque'); cal = addconstraint(cal, 'pred(knock_model) <= 0.1'); % knock probability <10% cal = setfactorrange(cal, 'Timing', -10, 30); optimal = optimize(cal); Build knock model (binary: 0=no knock, 1=knock) knock_model
% 1. Load data load engine_data.mat % contains Speed, Load, Timing, Torque, Knock % 2. Build torque model torque_model = mbcgp(data, 'Torque', 'Speed','Load','Timing'); torque_model = fit(torque_model);
data = mbcdata.import('engine_test.csv'); % Remove outliers data = removeoutliers(data, 'Response', 'BSFC'); % Split into training/validation [train, val] = splitdata(data, 0.8); Use mbcmodels to create response surface models.