Sensitivity-Orthogonal Residual Learning for Adaptive Sideslip and Cornering Stiffness Estimation
Abstract: Online adaptation of the cornering stiffness keeps a model-based sideslip estimator valid as tires change, but competes with learned model correction for the same filter innovation. A corrector with enough parameters can absorb the innovation pattern that a stiffness change produces, and adaptation is suppressed while the fit improves. We propagate the stiffness sensitivities of the predicted measurements through the vehicle states and construct a corrector orthogonal to them. A null-space parameterization enforces the constraint without a penalty coefficient and preserves the static least-squares stiffness estimate on each identification segment. In full-vehicle simulation, the stiffness response of the unconstrained corrector declines as its parameter count grows, whereas the orthogonal corrector responds at least as fully as the uncorrected filter at all sizes and lowers sideslip error by 12.7% to 22.0%. In a simulated drive with a stiffness step, the orthogonal corrector adapts as quickly as the uncorrected filter and lowers sideslip error during and after the transient. On public real-vehicle data, the 430-parameter unconstrained corrector loses most of its response to deliberate offsets of the filter's nominal stiffness, whereas the orthogonal corrector of the same size retains most of it, with a sideslip error slightly above the uncorrected filter's.