Neural network observer for twin rotor MIMO system: An LMI based approach

This paper presents a neural network based observer for the twin rotor multi-input-multi-output (MIMO) system which belongs to a class of nonlinear system. The unknown nonlinearities are estimated by neural network whose weights are adaptively adjusted. The stability of the neural network observer is shown by Lyapunov's direct method. A coordinate trans-formation is used to reformulate this inequality as a linear matrix inequality. A systematic algorithm is presented, which checks for feasibility of a solution to the quadratic inequality and yields an observer when-ever the solution is feasible. The state estimation errors and neural network weights are guaranteed to be uniform ultimate boundness to zero asymptotically.