In this paper, a tracking flight control scheme is proposed based on a disturbance observer for a quadrotor with external disturbances. To facilitate the processing of external time-varying disturbances, it is assumed to consist of some harmonic disturbances. Then, a disturbance observer is proposed to estimate the unknown disturbance. By using the output of the disturbance observer, a flight controller of the quadrotor is developed to track the given signals which are generated by the reference model. Finally, the proposed control method is applied to flight control of the quadrotor Quanser Qball 2. The experimental results are presented to demonstrate the effectiveness of the developed control strategy.

In this article, a disturbance-observer-based adaptive fuzzy tracking control scheme is proposed for a medium-scale unmanned helicopter of six degrees of freedom in the presence of system uncertainties, flight boundary constraints, and external disturbances. A flight boundary protection algorithm is proposed to ensure its flight trajectory within the given safety range. A fuzzy logic system is utilized to estimate the system uncertainties and a nonlinear disturbance observer is adopted to handle the unknown compound terms of the external disturbances and the estimation errors resulting from the fuzzy logic system. An inverse optimal control approach is then used to avoid solving the Hamilton–Jacobi–Bellman equation in minimizing a cost function in the attitude loop. It is shown via the Lyapunov method that the desired safe tracking performance of the position loop and attitude loop of the controlled unmanned helicopter can be achieved. Simulations are provided to illustrate the effectiveness of the proposed control scheme.

In this article, an adaptive neural fault-tolerant control (FTC) scheme is proposed for the medium-scale unmanned autonomous helicopter subject to external disturbance, actuator fault, and input saturation. Multiple approximators are constructed to handle the unknown terms and promote the control design. The nonlinear coupled function terms are approximated by virtue of the radial basis function neural networks. The unknown disturbance is tackled by the developed disturbance observer. Meanwhile, two auxiliary systems are introduced to handle the actuator fault and input saturation, respectively. In the framework of the backstepping method, a multiapproximator-based adaptive FTC strategy is presented, which assures the boundedness of all closed-loop system signals. Simulation results are presented to validate the availability of the designed controller.

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.

In this article, an adaptive neural safe tracking control scheme is studied for a class of uncertain nonlinear systems with output constraints and unknown external disturbances. To allow the output to stay in the desired output constraints, a boundary protection approach is developed and utilized in the output constrained problem. Since the generated output constraint trajectory is piecewise differentiable, a dynamic surface method is utilized to handle it. For the purpose of approximating the system uncertainties, a radial basis function neural network (RBFNN) is adopted. Under the output of the RBFNN, the disturbance observer technology is employed to estimate the unknown compound disturbances of the system. Finally, the Lyapunov function method is utilized to analyze the convergence of the tracking error. Taking a two-link manipulator system, as an example, the simulation results are presented to illustrate the feasibility of the proposed control scheme.

This paper proposes a disturbance interval estimation approach for nonlinear systems. Distinct from the conventional estimation schemes aiming to estimate the magnitude of the disturbance, the disturbance interval estimation approach is capable of approximating the time-varying upper and lower boundaries for the disturbance. To achieve this, the dynamics of the boundary estimation errors are constructed as positive systems by introducing a time-invariant coordinate transformation. The boundary estimation errors are guaranteed to be nonnegative and bounded. Then, the generated interval is capable of covering all possible values of the current disturbance and the interval width can provide a quantized guidance on the estimation accuracy at any time. Furthermore, a general antidisturbance control scheme is summarized in light of the proposed disturbance interval estimation approach. As an illustrative case, an antidisturbance control method is developed by integrating the proposed estimation approach and the sliding mode control. Finally, the proposed disturbance interval estimation approach and the anti-disturbance control method are demonstrated through both the numerical and the experimental validation.