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Quadcopter unmanned aerial vehicles continue to play important roles in several applications and the improvement of their control performance has been explored in a great number of studies. In this paper, we present an altitude control algorithm for quadcopters that consists of a combination of nonlinear and linear controllers. The smooth transition between the nonlinear and linear modes are guaranteed through controller gains that are obtained based on mathematical analysis. The proposed controller takes advantage and addresses some known shortcomings of the conventional proportional–integral–derivative control method. The algorithm is simple to implement, and we prove its stability through the Lyapunov theory. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our control method to that of a conventional proportional–derivative–integral approach. Furthermore, we use a DJI-F450 drone equipped with a laser ranging sensor as the experimental quadcopter platform to evaluate the performance of our new controller in real flight conditions. Numerical simulation and experimental results demonstrate the effectiveness of the proposed algorithm.

Nowadays, quadcopter unmanned aerial vehicles play important roles in several real-world applications and the improvement of their control performance has become an increasingly attractive topic of a great number of studies. In this paper, we present a new approach for the design and stability analysis of a quadcopter adaptive trajectory tracking control. Based on the quadcopter nonlinear dynamics model which is obtained by using the Euler–Lagrange approach, the tracking controller is devised via the backstepping control technique. Besides, an adaptive law is proposed to deal with the system parameterized uncertainties and to guarantee that the control input is finite. In addition, the vehicle’s vertical descending acceleration is ensured to not exceed the gravitational acceleration by making use of a barrier Lyapunov function. It is shown that the suitable parameter estimator is stable and the tracking errors are guaranteed to be asymptotically stable simultaneously. By prescribing certain flight conditions, we use numerical simulations to compare the control performance of our method to that of existing approaches. The simulation results demonstrate the effectiveness of the proposed algorithm.

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This study presents a self-organizing interval type-2 fuzzy asymmetric cerebellar model articulation controller (MSIT2FAC) design for synchronizing chaotic satellite systems that use a modified grey wolf optimizer. The proposed control system uses MSIT2FAC as the main controller (which mimics an ideal controller) and a robust compensation controller (which addresses the approximation error between the ideal controller and the main controller). The self-organizing algorithm is used to generate the first network layer. In subsequent iterations, it autonomously increases or decreases the number of network layers using the tracking error. The adaptive laws for adjusting the parameters for the fuzzy rule for the proposed system are derived using the gradient descent method. The optimal learning rates for the adaptive laws are achieved using a modified grey wolf optimizer. The Lyapunov stability analysis guarantees the stability of the proposed algorithm. Finally, the numerical simulation results illustrate the effectiveness of the proposed method. INDEX TERMS Interval type-2 fuzzy neural network, self-organizing algorithm, cerebellar model articulation controller, asymmetric membership function, chaotic satellite.