A learning control strategy is preferred for the control and guidance of a fixed-wing unmanned aerial vehicle to deal with lack of modeling and flight uncertainties. For learning the plant model as well as changing working conditions online, a fuzzy neural network (FNN) is used in parallel with a conventional P (proportional) controller. Among the learning algorithms in the literature, a derivative-free one, sliding mode control (SMC) theory-based learning algorithm, is preferred as it has been proved to be computationally efficient in real-time applications. Its proven robustness and finite time converging nature make the learning algorithm appropriate for controlling an unmanned aerial vehicle as the computational power is always limited in unmanned aerial vehicles (UAVs). The parameter update rules and stability conditions of the learning are derived, and the proof of the stability of the learning algorithm is shown by using a candidate Lyapunov function. Intensive simulations are performed to illustrate the applicability of the proposed controller which includes the tracking of a three-dimensional trajectory by the UAV subject to time-varying wind conditions. The simulation results show the efficiency of the proposed control algorithm, especially in real-time control systems because of its computational efficiency.

To facilitate accurate tracking in unknown/uncertain environments, this paper proposes a simple learning (SL) strategy for feedback linearization control (FLC) of aerial robots subject to uncertainties. The SL strategy minimizes a cost function defined based on the closed-loop error dynamics of the nominal system via the gradient descent technique to find the adaptation rules for feedback controller gains and disturbance estimate in the feedback control law. In addition to the derivation of the SL adaptation rules, the closed-loop stability for a second-order uncertain nonlinear system is proven in this paper. Moreover, it is shown that the SL strategy can find the global optimum point, while the controller gains and disturbance estimate converge to a finite value which implies a bounded control action in the steadystate. Furthermore, utilizing a simulation study, it is shown that the simple learning-based FLC (SL-FLC) framework can ensure desired closed-loop error dynamics in the presence of disturbances and modeling uncertainties. Finally, to validate the SL-FLC framework in real-time, the trajectory tracking problem of a tilt-rotor tricopter unmanned aerial vehicle under uncertain conditions is studied via three case scenarios, wherein the disturbances in the form of mass variation, ground effect, and wind gust, are induced. The real-time results illustrate that the SL-FLC framework facilitates a better tracking performance than that of the traditional FLC method while maintaining the nominal control performance in the absence of modeling uncertainties and external disturbances, and exhibiting robust control performance in the presence of modeling uncertainties and external disturbances.

This paper studies the sloshing suppression problem in point-to-point liquid container transfer via a Prismatic–Prismatic–Revolute robot. A multi-mass–spring model is introduced for the characterization of the most prominent liquid-sloshing modes. The control inputs are two forces and a torque applied to the prismatic joints and the revolute joint, respectively. The control objective is to control the robot end-effector movement while suppressing the sloshing modes. A nonlinear mathematical model that reflects all of these specifications is first derived. Then, a Lyapunov-based nonlinear feedback controller is designed to achieve the control objective. Finally, a simulation example is included to demonstrate the effectiveness of the controller.

Stabilization and tracking control of Unmanned Aircraft Systems (UASs) such as helicopters in a complex environment with system uncertainties, unknown disturbances, and noise is a challenging task; therefore, to compensate for system uncertainties and unknown disturbances, this paper presents a trajectory tracking control strategy for a 2-DOF (degree of freedom) helicopter system testbed by employing a gradient descent-based simple learning control law that minimizes the cost function corresponding to desired closed-loop error dynamics of the nonlinear system under control. In addition, to ensure the stability of the closed-loop nonlinear system, further analysis is provided. The learning capability of the designed controller makes it suitable to take system uncertainties and unknown disturbances into account. The results of computer simulations and real-time experiment using the Quanser AERO helicopter are included to demonstrate the effectiveness of the designed control strategy.