Novel adaptive backstepping control of an innovative engine cooling system

This paper proposes a novel adaptive backstepping control for an innovative engine cooling system. The engine cooling system belongs to a special class of nonlinear systems with both matched and mismatched state-dependent lumped disturbances. The parameter update laws resemble a nonlinear reduced-order disturbance observer and guarantee the convergence of the estimated parameter values to the true ones. In each recursive design step, only a single parameter update law is required for each lumped disturbance in contrast to the standard adaptive backstepping control based on overparametrization and tuning functions. An experimental analysis on a dedicated test rig highlights the effectiveness of the proposed novel adaptive backstepping control in terms of asymptotic tracking, global stability and guaranteed parameter convergence.