This paper presents a novel adaptive fuzzy backstepping dynamic surface control (DSC) scheme for a class of single-input-single-output (SISO) strict-feedback fractional order uncertain nonlinear systems. The controlled systems contain unknown nonlinear functions and unknown external disturbances. Fuzzy logic systems (FLSs) are employed for approximating the unknown nonlinear functions. Further, an auxiliary function is introduced into the control function with the aim to simultaneously compensate the unknown external disturbance and the approximation error caused by fuzzy approximation, which erases the possible chattering phenomenon in the existing results. Meanwhile, a new DSC method based on the fractional order filter is proposed to avoid the issue of explosion of complexity inherent in the backstepping procedure, which releases the limitation that the fractional order derivative of the intermediate control function need be complete known in the existing references. Under certain assumptions, the stability of the closed-loop system is proved by using the fractional order Lyapunov function stability criterion. Finally, contrastive simulation results are provided to validate the effectiveness of our proposed control strategy.

This paper studies the robust trajectory tracking control problem of a quadrotor unmanned aerial vehicle (UAV). In order to guarantee the desired trajectory tracking perfor- mance in the presence of external disturbances and model uncertainties, the design process of the quadrotor UAV controller is divided into two steps. First, by decomposing the attitude dynamic system into two serial-connected subsystems, a cascade active disturbance rejection control (ADRC) scheme is applied to the attitude subsystem. Second, by introducing an additional high-gain design parameter, a novel backstepping sliding-mode control (BSMC) scheme for position subsystem is constructed. Moreover, the Lyapunov stability analysis is provided to show that the trajectory tracking error can converge to an arbitrarily small residual set. Numerical results illustrate the effectiveness of the designed control method and its robustness to the external disturbances and model uncertainties. Finally, the proposed method is implemented on a quadrotor UAV to demonstrate its feasibility in practical application.

This paper studies the diagnosis and compensation for sensor and actuator faults in a quadrotor unmanned aerial vehicle. Without adding sensors or actuators for increased hardware redundancy, an observer-based adaptive controller is proposed to estimate and compensate for the faults. First, using a feedback linearization technique, an inner controller is designed to transform the form of the considered quadrotor unmanned aerial vehicle with faults into a nonlinear system with Lipschitz-like nonlinearities and parametric faulty models. Second, the estimations for unmeasurable state and actuator faults are performed in an output-feedback outer controller to compensate for the actuator faults. Third, a nonlinear high-gain observer is designed to provide the information of the state and faults to the outer controller, with the compensations for sensor faults. A Lyapunov-based analysis shows that appropriate choices of the controller parameters can guarantee the exponential convergence of errors in estimation and trajectory tracking under uncertainties and faults. The robustness to the external disturbances is also discussed. Simulations are given to verify the effectiveness of the proposed scheme. The proposed approach is also implemented on a quadrotor unmanned aerial vehicle to show its feasibility in real-time applications.

Adaptive fuzzy fault-tolerant control (FTC) problem for a class of uncertain switched stochastic nonlinear systems with time-varying asymmetric output constraints is addressed in this study. Under the action of a well-designed asymmetric nonlinear mapping (NM), fuzzy control technology and backstepping recursive design scheme, the actuator faults of both loss of effectiveness and lock-in-place are considered to develop the adaptive fuzzy controller. The boundedness of all signals as well as the convergence of the output tracking error of the closedloop plant to an arbitrary small neighborhood about zero are guaranteed by the developed fuzzy adaptive control strategy, and the time-varying output constraints are not violated. A simulation example is worked out to demonstrate the validity of the proposed control scheme.

This paper addresses the decentralized tracking control problem for a class of strong interconnected nonlinear systems with actuator faults. The considered interconnections are allowed to be dominated by some bounding functions, which are linear growth in the status of all subsystems. First, an adaptive high-gain technique is introduced to deal with the unknown strong interconnections. Then, a group of fault-tolerant controllers are designed to adaptively compensate for the effects of the actuator failures, in which the controller gain parameters are adjusted online only according to local available information. Furthermore, with the aid of an algebraic graph theory result, it is proved that all signals of the closed-loop system are globally uniformly bounded, and the tracking errors of all subsystems converge to zero asymptotically. The effectiveness of the proposed control algorithm is demonstrated by a numerical simulation.

The adaptive fuzzy prescribed performance tracking control design problem is investigator switched stochastic nonlinear systems with unknown control directions in this paper. The Nussbaum-type functions are introduced to handle the issue of unknown control directions. The constructed controller guarantees that all the signals in the closed-loop are bounded in probability, whereas the convergence of the tracking error and the prescribed performance bound are also ensured. Finally, simulations on a mass-spring-damper system with controller switching are performed to illustrate the developed control scheme.