Adaptive control for ship steering based on clonal selection model identification

Using clonal selection model identification, an adaptive PD control algorithm with disturbance compensation is proposed for uncertain and nonlinear system in ship steering. Actual ship with disturbance and model uncertainty, regarded as a black box, is dynamically identified as second-order linear model with disturbance terms; then, certainty equivalence principle is applied to tune the PD control parameters while the disturbance is compensated. The combination of previous elitist reservation and stochastic initialization for the initial population in clonal selection algorithm improves the optimization efficiency for dynamic problem. Simulations on the third-order nonlinear cargo vessel verify that the proposed adaptive control algorithm possesses strong robustness and improves the capability of PD controller in inhibiting stable error.