An algorithm for parameter estimation of twin-rotor multi-input multi-output system using trust region optimization methods

Abstract

The twin-rotor, multi-input, multi-output system is a typical lab-scale helicopter system, since it resembles behavior of helicopter systems. This experimental complex aerodynamic test rig needs precise modeling to assure satisfactory control performance. Therefore, this article is intended to provide a gray-box approach for the modeling of the twin-rotor, multi-input, multi-output system. An algorithm is proposed by dividing the system into independent subsystems that can be estimated for an excitation input or a characteristic response. The proposed algorithm uses trust region optimization methods for successive estimations. The iterative optimization methods of Levenberg–Marquardt, gradient descent active set, and interior point are compared with respect to trust region methods. The model is validated with various sets of scenarios: different inputs, different types of motion, and different operating points. Effect of operating point selection on the parameter estimation process is investigated. Time domain response comparison of the single-degree-of-freedom vertical, single-degree-of-freedom horizontal, and two-degrees-of-freedom models are acceptable.

Bond graph modeling and multi-body dynamics of a twin rotor system

“…The main and tail rotor BG models are shown in Figures 23 and 24, respectively. The drag and thrust forces due to the rotor are modeled therein as per equations (5)- (9). According to the relative motions between the rotors and the support beam, constraints are imposed at the joints.…”

“…They found that both models give almost similar results; the Lagrangian model being marginally better than the Newtonian model. A gray box approach was proposed by Moness and Mostafa 9 for the modeling and identification of the TRMS using trust region optimization methods. They partitioned the TRMS into two decoupled subsystems and estimated the parameters of those subsystems independently for various input excitations.…”

Tuning a digital multivariable controller for a lab-scale helicopter system via simulated annealing and evolutionary algorithms

“…The helicopter parameters were estimated by the authors in Moness and Mostafa (2013) and listed in Table 1. The non-linear model of the lab-scale helicopter system is linearized around the hovering point to obtain the transfer functions matrix described in Equation ( 8).…”

Surrogate Optimal Fractional Control for Constrained Operational Service of UAV Systems

“…In this setup, a substantially interconnected propeller, controlled by the main rotor, assumes the responsibility for stabilizing the platform's vertical orientation, effectively counteracting angular deviations along the vertical axis. Simultaneously, a smaller propeller, maneuvered by the tail rotor, enables precise adjustments along the horizontal axis [6,7]. The dynamic interplay of these configurations, whether quadrotor or twin-rotor, is regulated by intricate adjustments in pitch and yaw angles.…”