In this paper, an adaptive neural discrete-time (ANDT) fractional-order tracking control scheme is proposed for an unmanned aerial vehicle system with prescribed performance in the presence of system uncertainties and unknown bounded disturbances based on a discrete-time disturbance observer (DTDO). The system uncertainties are handled using neural network (NN) approximation. To compensate for the adverse effects of unknown disturbances, an NN-based DTDO is designed. On the basis of the NN, the designed DTDO and the backstepping technology, an ANDT fractional-order control scheme with prescribed performance is developed. Then, the tracking errors are convergent under the proposed control scheme. Finally, the effectiveness of the proposed discrete-time control scheme is demonstrated by numerical simulation results.

An event-triggered-based (ETB) discrete-time neural control is studied for a quadrotor unmanned aerial vehicle (UAV) with external disturbances and input saturation by using the discrete-time disturbance observer (DTDO). First, the ETB mechanism of the neural network is given and the Sigmoid-type function is employed to tackle the problem of input saturation. Then, the DTDO is designed and the saturation function is utilized to ensure the boundedness of virtual control signal. Combining the discrete-time tracking differentiator and the backstepping technology, the stability of the closed-loop system is analyzed. Finally, the experiment results of the quadrotor UAV system are given to illustrate the feasibility of the proposed control scheme.

This paper proposes a high order disturbance observer (HODO) and dynamic surface control (DSC) technique based adaptive fuzzy control scheme for nonlinear systems subjected to input saturation and external time-varying disturbances. Firstly, based on a Sigmoid function, the saturation input is tackled by utilizing a well-defined nonlinear smooth function. Furthermore, HODO and fuzzy logic systems (FLSs) are used to estimate the external disturbances and to handle the lumped unknown functions, respectively. Then, by using backstepping method and DSC technique, a HODO-based adaptive fuzzy tracking control scheme is proposed for nonlinear systems with saturation nonlinearity, uncertainties and external disturbances. Lyapunov analysis method is used to prove that all signals in the entire system are semi-globally uniformly ultimately bounded (SGUUB). In addition, the tracking error converges to a compact set with a tunable error bound determined by some design parameters. Finally, a numerical simulation of two-stage chemical reactor shows the effectiveness of the developed tracking control strategy.

This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results.

In this paper, the flight control of an unmanned aerial vehicle (UAV), quadrotor, in the presence of high-order disturbances is presented. Since during flight, UAV faces enormous, and various kinds of disturbance, the effect of such disturbances becomes vital for consideration during the development of disturbance observer (DO) based tracking control scheme. To obtain the desired tracking performance, standard sliding mode control (SMC) method is utilized while for disturbance estimation, DO based on Simpson’s approximation is developed and incorporated with SMC. Furthermore, in this paper, both matched and mismatched disturbances are considered. Hence, a matrix associated with disturbances is invoked in the system model. To show the effective and desired control performance of developed disturbance observer-based control (DOBC) scheme, extensive simulations are conducted, followed by presenting the results in the paper.

In this article, an adaptive neural network (NN) tracking control scheme is proposed for uncertain multi-input-multi-output (MIMO) nonlinear system in strict-feedback form subject to system uncertainties, time-varying state constraints, and bounded disturbances. The radial basis function NNs (RBFNNs) are adopted to approximate the system uncertainties. By constructing the intermediate variables, the external disturbances that cannot be directly measured are approximated by the disturbance observers. The time-varying barrier Lyapunov function (TVBLF) is constructed to guarantee the boundedness of the errors lie in the sets. To overcome the potential singularity problem that the denominator of the barrier function term approaches zero in controller design, the adaptive NN tracking control scheme with time-varying state constraints is proposed. Based on the TVBLF, the controller will be designed to guarantee tracking performance without violating the appropriate error constraints. The analysis of TVBLF shows that all closed-loop signals remain semiglobally uniformly ultimately bounded (SGUUB). The simulation results are performed to validate the validity of the proposed scheme.