Clonal Selection Identification and Adaptive PD Control for Uncertain Dynamical System

Using clonal selection model identification, an adaptive PD control algorithm is proposed for uncertain dynamical systems. The practical industrial processes are dynamically identified as second-order linear model; then,a certainty equivalence principle is applied to tune the PD controller. 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.