STATISTICAL MODELING AND MULTI-OBJECTIVE OPTIMIZATION OF ANTI-LOCK BRAKING CONTROL PARAMETERS UNDER VARIABLE OPERATING CONDITIONS
DOI:
https://doi.org/10.69980/k9ewjm11Keywords:
anti-lock braking system, response-surface modeling, multi-objective optimization, Pareto analysis, braking performanceAbstract
When a single control setting must be used for all operating conditions, trade-offs exist between anti-lock braking performance and characteristics of tires and the load carried by a vehicle. This research quantified the effects of anti-lock braking control parameters on multiple braking-distance responses and identified a balanced configuration across varying tire and load conditions. A quantitative computational framework was applied to two control parameters and five braking-distance responses using descriptive statistics, Pearson correlation, quadratic response-surface modeling, parameter sensitivity assessment, multi-response optimization, and Pareto analysis. Control-parameter influence varied across braking conditions. The quadratic models achieved the strongest fit for y4 (R² = 0.9356) and the weakest for y3 (R² = 0.2666). Equal-weight optimization identified x1 = −1.2 and x2 = −2.8 as the best overall compromise, producing a composite score of 0.0936. The selected configuration was Pareto-optimal, while 405 of the 10,101 evaluated configurations (4.0095%) were non-dominated. Results show that the braking responses vary between tires and loads and are used in the support condition-aware parameter calibration. The response-surface modeling together with a Pareto-based optimization approach is an interpretable tool to optimize conflicting anti-lock braking performance criteria.
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