Quadrotor Attitude Control using Incremental Nonlinear Dynamics Inversion
Given the large flight envelope of vertical take-off and landing vehicles, and the nonlinear nature of multirotor aircraft, especially in aggressive maneuvering, nonlinear control strategies are often considered. Yet, most available solutions are model dependent and thus require a precise knowledge of the system dynamics, including the hard to model aerodynamics. This paper proposes a sensor-based approach to the problem. It considers a recent strategy based on incremental control and Nonlinear Dynamics Inversion (NDI), the Incremental Nonlinear Dynamics Inversion (INDI), to solve the attitude control problem of quadrotors. The INDI general formulation is presented, and then applied considering the attitude stabilization of a quadrotor. In order to apply the INDI solution to this case study, a linear predictor for the angular acceleration is provided. Simulation results demonstrate the robustness of this sensor-based approach to model-parameter uncertainties and wind disturbances. An analysis is also done regarding the tuning of INDI parameters and its sensitivity to the chosen sampling time.
L1 Adaptive Controller for Attitude Control of Multirotors
In this work we show an application of L1 Adaptive control theory for attitude control of UAVs. We implement the flight control system on a multirotor to show robustness and precise attitude tracking in the presence of modeling uncertainties and environmental disturbances. We choose backstepping control architecture, since the kinematics and dynamics of multirotors in most cases can be written in strict feedback form. We further exploit the fact that the kinematics of the plant, while free of uncertainties, is nonlinear, which makes it highly suitable for dynamic inversion control at each level of backstepping. On the other hand, plant dynamics is uncertain and is affected by environmental disturbances such as wind gusts, unmodeled dynamics etc. Therefore, we consider 3 variants of the control architecture. The first method uses backstepping to determine the moment demand and augments it with the L1 adaptive controller to account for uncertainties and provide robustness with guaranteed transient performance. The second architecture apply the concept of L1 adaptive backstepping to the same problem; and the third architecture uses L1 backstepping for quaternion representation of the system dynamics, which helps to avoid the singularities associated with Euler angles.
Adaptive nonlinear control system design for helicopter robust command augmentation
Abstract In this paper, the design and evaluation of a helicopter trajectory tracking controller are presented. The control algorithm is implemented using the feedback linearization technique and the two time-scale separation architecture. In addition, an on-line adaptive architecture that employs a Sigma-Pi neural network, which is simple in its structure so that it is easily applicable to on-line adaptation, compensating the model inversion error caused by the deficiency of full knowledge of helicopter dynamics is applied to augment the attitude control system. Trajectory tracking performance of the control system is evaluated using a generic helicopter model simulation program. It is shown that the on-line neural network in an adaptive control architecture is very effective in dealing with the performance degradation problem of the trajectory tracking control caused by insufficient information of dynamics.