RBF based adaptive backstepping neural control of a dual-axis motion platform

In this paper, Lagrange equation of motion is used to derive the dynamic model of a serial type dual-axis motion platform. The dynamic model of the serial type dual-axis motion platform is second order MIMO nonlinear system and can be described in strict-feedback form. The adaptive backstepping neural control technique is applied to control the azimuth and elevation steering angles of the system under study. Through the backstepping controller design, radial basis function (RBF) neural networks are used to approximate the unknown nonlinearities in the control laws, and the networks adaptive update laws are derived based on Lyapunov stability theory. The proposed controller guarantees semi-global boundedness of all signals in the closed-loop system, and the system output is proven to track the desired trajectory with a small tracking error. The simulation study illustrates the effectiveness of the proposed controller to achieve the required tracking performance.