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This paper proposes a novel fixed‐time active fault‐tolerant control method for second‐order affine nonlinear systems. The key innovation lies in the integration of a super‐twisting barrier function based adaptive sliding mode control (ST‐BFASMC) with a radial basis function neural network (RBFNN) observer, achieving simultaneous improvements in convergence speed and fault tolerance. Firstly, a RBFNN observer with weight and centre value update strategy is designed. Secondly, a non‐singular fast terminal sliding surface and control law with a super‐twisting term are constructed. Furthermore, the fixed‐time convergence properties of both the controller and observer are rigorously proven using Lyapunov stability theory. Experimental studies on quadrotor UAV attitude control demonstrated that the adopted RBFNN observer achieved over 60% improvement across all performance metrics compared to baseline methods, while the control algorithm exhibited more than 30% enhancement in multiple indicators relative to conventional ASMC and barrier function based ASMC (BFASMC) approaches. These results validate the algorithm's strong robustness and fault‐tolerant capability in the presence of actuator failures. This paper proposes a fixed‐time active fault‐tolerant control method combining super‐twisting sliding mode control and a radial basis function neural network (RBFNN) observer, improving convergence speed while reducing chattering in nonlinear systems. The method features a fixed‐time RBFNN observer for accurate fault estimation and an adaptive super‐twisting controller for fast, smooth convergence, both proven via Lyapunov theory. Quadrotor UAV simulations confirm its superior robustness and fault tolerance under actuator failures.

In this paper, a novel three‐dimensional fixed‐time integrated guidance and control (IGC) scheme with multi‐stage interconnected observers is proposed for cooperative attacks using multiple missiles against a maneuvering target under impact angle and input saturation constraints. External disturbances, modeling errors, and aerodynamic parameter variations are considered as system uncertainties and a three‐channel fully coupled IGC model for multiple missiles is established. The IGC system is designed optimally based on fixed‐time stability theory, sliding mode control, and the backstepping technique. Three inter‐cascaded fixed‐time disturbance observers based on an improved super‐twisting algorithm are designed to estimate and compensate for system uncertainties. Second‐order command filters are used to constrain virtual control signals, and additional filtering error subsystems are introduced to compensate for the tracking errors of filters. System stability and uniformly ultimately fixed‐time boundedness of all states are proven using the Lyapunov stability theory. Finally, the limits of the acceleration components of the maneuvering target perpendicular to the line of sight direction are derived. The effectiveness of the designed IGC scheme and the ability of multi‐stage interconnected observers to sense disturbances with each other are verified through simulations. There are three main points: the first one is to add the actuator factors to the existing multiple‐missile cooperative guidance law to consider the design of an integrated guidance and control scheme under multiple‐missile cooperative guidance; the second one is to design the disturbance observer with the ability to influence and sense each other on the basis of the existing fixed‐time disturbance observer based on the improved super‐twisting algorithm; and the third one is to derive the limits of a maneuvering target's acceleration perpendicular to the LOS direction under input saturation constraints.

The Super-Twisting Sliding Mode Controller (STSMC) is regarded as one of the most straightforward and most practical nonlinear control systems, due to its ease of application in industrial systems. Its application helps minimize the chattering problem and significantly improves the resilience of the system. This controller possesses multiple deficiencies and issues, as its use does not promote the expected improvement of systems. To overcome these shortcomings and optimize the efficiency and performance of this technique, a new method is suggested for the super-twisting algorithm (STA). This study proposes and uses a new STA approach, named the fast super-twisting algorithm (FSTA), utilized the conventional IFOC technique to mitigate fluctuations in torque, current, and active power. The results from this suggested the IFOC-FSTA method are compared with those of the traditional SMC and STA methods. The results obtained from this study demonstrate that the suggested method, which is based on FSTA, has outperformed the traditional method in terms of ripple ratio and response dynamics. This demonstrates the robustness of the proposed approach to optimize the generator performance and efficiency in the double-fed induction generator-based wind system.

The guidance laws for intercepting maneuvering targets in three‐dimensional (3D) space poses considerable challenges owing to various inescapable factors. These factors include the impact angle, input saturation constraints, and uncertainty. To address these challenges, a novel universal operator, denoted as |||·||| is introduced for the first time. This operator, when employed to represent sliding surface vectors, demonstrates a closer alignment with practical scenarios compared to the traditional Euclidean vector norm ||·||. Following this, a novel universal fixed‐time nonsingular terminal sliding surface is introduced in both scalar and vector representations, effectively resolving issues related to singular points and achieving reduced convergence times. Additionally, Furthermore, a new continuous adaptive finite‐time nonsingular terminal sliding mode guidance law (CAFnTNTSMGL) has been formulated. This guidance law incorporates a newly proposed sliding surface, a modified finite‐time super‐twisting algorithm, and a parameter‐adaptive law. The system's stability and its finite convergence time are subsequently demonstrated. Finally, the effectiveness of CAFnTNTSMGL is validated through a comparative analysis of simulation results. CAFnTNTSMGL has the capacity to effectively mitigate the negative effects resulting from the indeterminate upper limit of the overall uncertainty has less intercept time, smaller terminal line‐of‐sight (LOS) angle error, smaller maximum field of view, and smaller total cost of energy. The article introduces novel operator III.III, innovative non‐singular sliding surface, and advanced guidance law. A comparative analysis of theoretical and numerical simulations against four alternative methodologies has yielded favorable outcomes.

In this paper we explore a variable gain super-twisting algorithm and super-twisting-like algorithm for the load frequency control of two-area interconnected power system with nonlinearities. The two-area system is based on two different kinds of turbine. The nonlinearities are contained in this model. The control objective is to adjust the area control error (ACE), frequency deviation and tie-line power deviation to zero. We also prove the convergence of the proposed two control methods. Additionally, the traditional super-twisting algorithm is designed and simulated. Finally, simulation results are discussed and in comparison with the traditional super-twisting algorithm, which shows the superiority of the proposed two methods.

This paper proposes a super-twisting algorithm (STA) with time delay estimation (TDE) for the problem of high-accuracy tracking trajectory of robotic manipulators in the presence of uncertainties and unexpected disturbances. The TDE method is known for it capability to estimate uncertainties simply without an exact knowledge of the system dynamics. Using the estimated uncertainties, the control law is then designed based on STA to ensure robustness, finite-time convergence and chattering reduction. The stability analysis of the closed-loop system and the finite-time convergence are proved using Lyapunov theory. In order to show the effectiveness of the proposed method, simulations and experimental results were carried out on a 2-DOF rigid robot manipulator and on the 7-DOF ANAT robot arm, respectively.

This paper develops a novel global nonsingular terminal sliding mode control (GNTSMC) strategy based on an adaptive super-twisting algorithm (STA) for tracking control of robotic manipulators with uncertain perturbations. A novel global nonsingular terminal sliding manifold is designed to steer the system trajectory to reach the switching surface at the beginning, thereby removing the reaching stage and achieving strong robustness throughout the entire response. Moreover, the proposed sliding manifold can ensure the finite time convergence of the trajectory error to the origin. Then, an adaptive STA, which does not require the boundary information of the perturbations, is devised not only to attenuate the chattering effect without degrading the tracking precision, but also to guarantee the finite time stability of the system. Finally, the superiority of the adopted GNTSMC is validated by comparative studies.

This study proposes a simplified super-twisting algorithm (SSTA) control strategy for improving the power quality of grid-connected photovoltaic (PV) power systems. Some quality issues are considered in this study including the power factor, reducing the total harmonic distortion (THD) of current, compensating the reactive power, and injecting at the same time the energy supplied by the PV system into the grid considering non-linear load. This improvement is achieved by two topologies; controlling both the boost DC–DC converter and the DC–AC inverter that links the PV system to the grid. The DC–DC converter is controlled using proportional-integral (PI) and SSTA to maximize the power generated from the PV panel regardless of its normal and abnormal conditions, while the DC–AC inverter is employed to direct power control strategy with modified space vector modulation using the phase-locked loop (PLL) technique of a three-level neutral-point-clamped (NPC) inverter based on the proposed strategies (PI and SSTA). In addition, a shunt active power filter (SAPF) is used to connect the PV system to the AC grid and feed a non-linear load. To validate the simulation results presented in this paper using Matlab software, a comparative study between the PI controller and the SSTA is presented. The results show the effectiveness and moderation of the suggested SSTA technique in terms of feasibility, tracking performance, less power ripple, dynamic response, THD value, overshoot, steady-state error, and robustness under varying irradiation, temperature, and non-linear conditions.

This paper is concerned with event-triggered sliding mode control (SMC) for uncertain mechanical systems subject to limited communication capacity. We consider the scenario where samplings of the system output and of the control input are generated by two different event-triggered strategies. An event-triggered mechanism is incorporated in the sensor to decide when the position information is transmitted from the sensor to observer. The super-twisting algorithm (STA)-based observer is developed to recover the unmeasured velocities by using event-triggered sampling of the position information. Besides, an SMC law is proposed and an input event-triggered mechanism is introduced to decide when the control signal is transmitted over the network to the actuator side. The stability for the overall closed-loop system is analyzed. It is proved that under the proposed output and input event-triggered strategies, there is no Zeno behavior exhibited. Examples are finally presented to illustrate the effectiveness of the proposed event-triggered STA-based observer and the event-triggered SMC scheme.

This paper proposes a novel missile guidance law optimization method based on deep reinforcement learning, specifically targeting terminal guidance for missiles engaging highly maneuverable targets in near-space environments. In scenarios where both the missile and target have comparable overload capabilities, effective interception becomes a significant challenge. Existing methods, such as the Saturated Super-Twisting Algorithms, demonstrate strong performance in maneuvering target interception but face difficulties in parameter tuning and control input saturation. To overcome these limitations, this study introduces the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to optimize the parameters of missile guidance laws, offering an innovative solution to these complex challenges. The TD3 algorithm, known for its ability to handle noisy environments and mitigate Q-value overestimation, enhances the guidance system's capability to intercept highly maneuverable targets with greater precision. Simulation results validate the proposed approach, demonstrating a substantial performance improvement over traditional methods, thus providing both theoretical and practical contributions to missile guidance system optimization for next-generation missile defense applications.