We present a robust adaptive second order sliding mode controller that rejects external disturbances and uncertainties to improve the tracking performance of attitude and altitude in a quadcopter based on a Proportional Integral Derivative (PID) sliding surface. The algorithm provides a rapid adaptation and strict robustness of the flight control for the vehicle under the effect of perturbations. The proposed controller design is based on the theory of second order sliding mode technique which eliminates the chattering phenomenon present in first order sliding mode controllers. In addition, we derive an adaptive law from the Lyapunov stability to ensure the robust control for the quadcopter even without knowing the upper bound for disturbances. Applying the same external disturbances, we use a numerical simulation to compare our algorithm to recent alternatives such as: normal adaptive Sliding Mode Control, Super-Twisting Sliding Mode Control, Modified Super-Twisting Sliding Mode Control, and Nonsingular Terminal Sliding Mode Control. The results demonstrate the effectiveness of our proposed algorithm.

Underwater vehicles (UVs) are subjected to various environmental disturbances due to ocean currents, propulsion systems, and un-modeled disturbances. In practice, it is very challenging to design a control system to maintain UVs stayed at the desired static position permanently under these conditions. Therefore, in this study, a nonlinear dynamics and robust positioning control of the over-actuated autonomous underwater vehicle (AUV) under the effects of ocean current and model uncertainties are presented. First, a motion equation of the over-actuated AUV under the effects of ocean current disturbances is established, and a trajectory generation of the over-actuated AUV heading angle is constructed based on the line of sight (LOS) algorithm. Second, a dynamic positioning (DP) control system based on motion control and an allocation control is proposed. For this, motion control of the over-actuated AUV based on the dynamic sliding mode control (DSMC) theory is adopted to improve the system robustness under the effects of the ocean current and model uncertainties. In addition, the stability of the system is proved based on Lyapunov criteria. Then, using the generalized forces generated from the motion control module, two different methods for optimal allocation control module: the least square (LS) method and quadratic programming (QP) method are developed to distribute a proper thrust to each thruster of the over-actuated AUV. Simulation studies are conducted to examine the effectiveness and robustness of the proposed DP controller. The results show that the proposed DP controller using the QP algorithm provides higher stability with smaller steady-state error and stronger robustness.

Nowadays, underwater vehicles (UVs) are applied to various tasks such as carrying objects or maintenance of underwater structures. To carry out well these tasks, UVs should keep the position and orientation in the water to perform the specified tasks. However, the systems used in underwater operations are always under the influence of disturbance such as ocean currents and model uncertainties. In this paper, the robust station-keeping (SK) control algorithm based on a sliding mode control (SMC) theory is designed to guarantee stability and better performance of a hovering over-actuated autonomous underwater vehicle (HAUV) despite the existence of model uncertainties and ocean current disturbance in the horizontal plane (HP). Using the Lyapunov theorem, the stability of the proposed controller is demonstrated. Besides, an optimal allocation control (AC) algorithm is also designed to keep the linear position and Euler angles of the HAUV in the presence of model uncertainties as well as ocean currents and to minimize the energy consumption of the system. Finally, a series of simulations and experiments for the HAUV system is conducted to demonstrate the superior performance of the proposed method.

This paper presents a lumped perturbation observer-based robust control method using an extended multiple sliding surface for a system with matched and unmatched uncertainties. The fundamental methodology is to apply the multiple surfaces to approximate the unknown lumped perturbations simultaneously influencing on a nonlinear single input–single output (SISO) system. Subsequently, a robust controller, based on the proposed multi-surface and the approximated values, is designed to highly improve the control performance of the system. A general stability of the lumped perturbation observer and closed-loop control system is obtained through the Lyapunov theory. Results of a numerical simulation of an illustrative example demonstrate the soundness of the proposed algorithm.

Nowadays, with the increasing popularity of quadcopter unmanned aerial vehicles in several real-world applications, achieving a fully autonomous quadcopter flight has become an imperative topic investigated in many studies. One of the most pressing issues in such a topic is the precision landing task, which always is devastatingly influenced by the ground effect and external disturbances. In this paper, we present an autonomous quadcopter landing algorithm allowing the vehicle to land robustly and precisely onto a heaving platform. Firstly, a robust control algorithm addressing the altitude flight under the ground effect and external disturbances is derived. We strictly prove the closed-loop system stability by using the Lyapunov theory. Secondly, a landing target state estimator is proposed to provide state estimations of the moving landing target. In addition, we propose a landing procedure to ensure the landing task is achieved safely and reliably. Finally, we use a DJI-F450 drone equipped with an infrared sensor and a laser ranging sensor as the experimental quadcopter platform and conduct experiments to evaluate the performance of our new algorithm in real flight conditions. The experimental results demonstrate the effectiveness of the proposed method.

Fractional-order four-wing (FO 4-wing) systems are of significant importance due to their complex dynamics and wide-ranging applications in secure communications, encryption, and nonlinear circuit design, making their control and stabilization a critical area of study. In this research, a novel model-free finite-time flexible sliding mode control (FTF-SMC) strategy is developed for the stabilization of a particular category of hyperchaotic FO 4-wing systems, which are subject to unknown uncertainties and input saturation constraints. The proposed approach leverages fractional-order Lyapunov stability theory to design a flexible sliding mode controller capable of effectively addressing the chaotic dynamics of FO 4-wing systems and ensuring finite-time convergence. Initially, a dynamic sliding surface is formulated to accommodate system variations. Following this, a robust model-free control law is designed to counteract uncertainties and input saturation effects. The finite-time stability of both the sliding surface and the control scheme is rigorously proven. The control strategy eliminates the need for explicit system models by exploiting the norm-bounded characteristics of chaotic system states. To optimize the parameters of the model-free FTF-SMC, a deep reinforcement learning framework based on the adaptive dynamic programming (ADP) algorithm is employed. The ADP agent utilizes two neural networks (NNs)—action NN and critic NN—aiming to obtain the optimal policy by maximizing a predefined reward function. This ensures that the sliding motion satisfies the reachability condition within a finite time frame. The effectiveness of the proposed methodology is validated through comprehensive simulations, numerical case studies, and comparative analyses.