Tracking Iterative Learning Control of TRMS using Feedback Linearization Model with Input Disturbance - Danh, Hoang Dang; Van, Chi Nguyen; Van, Quy Vu (Journal of Robotics and Control (JRC), 2025)
This paper presents a method for angular trajectory tracking control of the Twin Rotor Multi-Input Multi-Output System (TRMS) experimental model using linearized feedback control with nonlinear compensation and iterative learning-based angular trajectory tracking control. First, the dynamic model of the Twin Rotor MIMO System (TRMS) is developed in the form of Euler-Lagrange (ELF), including descriptions of uncertain parameters and input disturbances such as energy dependence related to the mass of components, friction forces, the effect of the TRMS flat cable, and the impact of the main rotor and tail rotor speeds on horizontal and vertical movements. Based on the nonlinear acceleration equations for the pitch and yaw angles of the TRMS, a compensator is designed to address the nonlinearity of the EL model. Notably, this compensator self-adjusts the compensation signal so that the closed-loop system, consisting of the TRMS and the compensator, becomes a predetermined linear model. Therefore, the structure of the compensator does not need to be designed based on the nonlinear model of the TRMS. After incorporating the compensator, the ELF becomes nearly linear with sufficient accuracy as designed. This system is then controlled using a predefined trajectory tracking controller based on iterative learning with proportional-type learning parameters. By adjusting a sufficiently small optional time parameter, the trajectory tracking error of the pitch and yaw angles of the closed-loop system can be reduced to a desired small-radius neighborhood. Simulation and experimental results demonstrate the trajectory-tracking capability of the closed-loop system. Although the convergence rate depends on the complexity of the TRMS dynamics, the robustness of this method with varying initial conditions is always ensured. The computational complexity is slightly higher compared to other methods, Still, this study contributes a straightforward yet effective trajectory control method under conditions of noise depending on the position, velocity, pitch and yaw angles and unmeasured kinematic model parameters for the TRMS system.