Optimized Uncertainty and Disturbance Estimator Based Robust Controller for Micro Aerial Vehicle

The work presents optimized uncertainty and disturbance estimator (UDE) based robust controller to achieve the fixed wing micro-aerial vehicle (MAV) longitudinal and lateral stability. In the proposed control methodology, genetic algorithm (GA) is used to find the optimal value of UDE filter parameter (GAUDE) which is filter time constant. GA uses minimization of integral absolute time error (IATE) based fitness function. In this work, the proposed controller is GAUDE-based adaptive sliding mode control (SMC). The Lyapunov theory is used to establish the stability of the presented controller. The performance of proposed SMC-GAUDE controller is analyzed through comparative analysis using numerical simulations. The comparative analysis consists of the proposed controller performance evaluation with existing UDE-based and conventional controllers. The comparative study shows the faster response to attain desired states along with smooth and chattering free control efforts offered by SMC-GAUDE controller. The results present viability of the proposed controller. To show the robustness of the proposed controller, IATE performance index is evaluated. Also, the Monte Carlo simulations are done to highlight the efficacy of the proposed controller in the presence of parametric variations in MAV aerodynamic coefficients and velocity.