Parameter Identification and Control for a 2-DOF Helicopter: A Finite and Fixed-Time Approach

This paper presents a finite/fixed-time distributed parameter identification method for a two-degree-of-freedom (2-DOF) helicopter system with uncertainties. The proposed approach utilizes a distributed discrete-time dynamic regression extension and mixing (DDREM) law to identify parameters in the uncertain interconnected subsystems of the 2-DOF helicopter, eliminating the need for persistence of excitation conditions (PE). An error transformation technique is applied to minimize identification errors in both subsystems in a monotonic fashion. Furthermore, an adaptive control algorithm is developed to ensure that system trajectories converge within a finite or fixed-time frame. Smoothing functions and bounded estimation methods are used to facilitate distributed online identification, ensure tracking convergence, and minimize estimation errors. The stability of the Quanser 2-DOF helicopter system is proven to be bounded in finite/fixed-time using Lyapunov-based methods. Simulation and experimental results validate the proposed control strategy, demonstrating successful parameter identification and demonstrating convergence of the pitch angle θ and the yaw angle ψ within a specified time frame, outperforming traditional gradient-based methods.