Explore the vast range of titles available.

In this paper, we present a comprehensive and detailed review of dynamic soaring process, and in particular, its application to unmanned aerial vehicles (UAVs). We start by explaining the biological inspiration that comes from soaring birds and how researchers have tried to utilize the dynamic soaring phenomenon/maneuver and apply it to UAVs. We present and discuss the fundamentals of wind shear models in both the linear and nonlinear cases. Moreover, a comprehensive parametric characterization of the key performance parameters for the dynamic soaring maneuver is given. Numerical methods for nonlinear trajectory optimization are summarized and methodologies capable of generating rapid solutions suitable for real-time implementation, are presented. Additionally, the paper introduces mathematical modeling and procedure to generate the optimized dynamic soaring trajectory. Through this paper, a consolidated platform is built, which not only covers technical aspects of advancements made over the passage of time, but also identifies and discusses the existing challenges. These challenges which are encountered by UAVs curtail the potential utility of dynamic soaring. Integrating dynamic soaring with morphology and inclusion of nonlinear control theory in the flight control system are introduced as a possible future research directions that may overcome the existing limitations.

This article proposes an online universal self‐learning control (USLC) algorithm based on a physical performance policy‐optimization neural network, which aims to solve the problem of universal self‐learning optimal control laws for nonlinear systems with various uncertain dynamics. As a key system characterization, this algorithm predicts the discrepancy between the optimal and current control laws by evaluating overall performance in each iterative learning cycle, leveraging an offline‐trained universal policy network. This approach is universal, as it does not rely on an exact system model and can adaptively control performance preferences across various tasks by customizing the physical performance cost weights. Using the established control law‐performance surface and contraction Lyapunov function, the necessary assumptions and proofs for the stable convergence of the system within a three‐dimensional manifold space are provided. To demonstrate the universality of USLC, simulation experiments are conducted on two different systems: a low‐order circuit system and a high‐order variable‐span aircraft attitude control system. The stable control achieved under varying initial values and boundary conditions in each system illustrates the effectiveness of the proposed method. Finally, the limitations of this study are discussed. This study addresses the challenge of achieving real‐time Universal Self‐Learning Control (USLC) in nonlinear dynamic systems with uncertain models. The proposed control method incorporates a Universal Self‐Learning module, which introduces a model‐free online executor‐evaluator framework to enable controller adaptation in the presence of unknown disturbances. By leveraging a neural network model trained on historical system performance data, the controller can autonomously learn to approximate optimal performance during each learning cycle.

This paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for a co‐axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbines. The generalized canonical form (GCF) is used to normalize the nonlinear augmented system to achieve the dynamic control signal with a chain of integrator of system output. In addition, a tracking problem is defined to track high speed rotor (HSR) with respect to GCF. On the other hand, the HGO is used to estimate the error tracking at the expense of nonlinear terms. Furthermore, the nonlinear system observability of the augmented system is evaluated. A dynamic control law is used to solve the tracking problem based on HGO. This controller can eliminate the effect of load torque as a constant and unknown disturbance in the closed‐loop system. Moreover, the closed‐loop stability of the system under dynamic signal control is guaranteed. The system states estimation is calculated by recursive equations from tracking error estimations. Finally, numerical simulations are given to illustrate the theoretical results of proposed system. This paper proposes a nonlinear control law with a dynamic controller based on high gain observer (HGO) for co‐axial double rotor magnetic gear (CADRMG) based on Halbach array used in wind turbine.

The design of stable high‐performance precision controllers is often confronted with a set of actuation input nonlinearities and dynamical systems where the control input's time derivative appears as an inherent higher order dynamics term in the governing physical equations. This paper presents a permanently feasible asymptotically stable control architecture that addresses mathematical intricacies of this class of systems, designed to ensure robust stability and precise trajectory tracking despite parametric uncertainty and measurement noise, as well as concurrent imposition of multiple, cascaded saturation input nonlinearities within the main system and actuator. Built upon a gradient descent (GD)‐optimised dual‐mode discrete sliding‐mode control, the proposed framework systematically addresses these complexities through an advanced trajectory modification scheme, which defines the feasible operating region by computing the intersection of multiple constraint‐derived sets. The result is a control methodology that provides solid performance for systems where actuator dynamics, constraints, and input derivative terms emerge as intrinsic non‐negligible features. The algorithm also deeply mitigates the dependence on parameter pre‐tuning while offering low computational burden, as substantiated by dedicative analyses. It further utilises optimal position–velocity mode synergy for smooth tracking of even high‐frequency or discontinuous reference trajectories. Simulation results and subsequent comparisons verify the effectiveness of the control algorithm in terms of tracking accuracy, lower energy consumption, and enhanced robustness against parameter uncertainty. To validate real‐time performance compatibility, the controller was implemented on an isolated 2.8 GHz Intel Core i7 core under the EVL real‐time Linux kernel. During a 30‐s run at a 100 Hz sample rate, it exhibited a 2.66‐ms average execution time and a 5.54‐ms maximum (well under the 10‐ms allotted period) with no missed deadlines despite the presence of execution time jitter and statistical outliers. The sustained throughput of 393.16 Hz demonstrates that the proposed controller achieves both an affordable computational burden and deterministic timing suitable for embedded applications.

In this work, a novel model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL). Firstly, a generalized nonlinear nonaffine system with external disturbance is transformed into an affine system via the FFDL method, which contains a linearly parametric term affine to the control input and preceding output data, and an unknown nonlinear time‐varying term. Then, an adaptive estimation method and a discrete‐time extended state observer (DESO) are used to estimate the pseudo gradient (PG) vector and lumped uncertainties, respectively. Furthermore, an integral sliding mode control scheme containing a modified prescribed performance function and an anti‐windup compensator is designed to keep the output tracking error in the prescribed bound without causing any asymmetric offset error in the steady‐state and to suppress the influence of input saturation. Simulation results demonstrate the superiority of the proposed control scheme. In this paper, a novel discrete‐time extended state observer‐based model‐free adaptive integral sliding model constrained control strategy with modified prescribed performance is proposed for nonlinear nonaffine systems via full‐form dynamic linearization (FFDL).

This paper investigates the design of a model‐free adaptive iterative learning controller(MFAILC) based on sampled‐data under the presence of denial‐of‐service(DoS) attacks in nonlinear networked control systems. First, the MFAILC is presented only using I/O data, where a compensation mechanism for DoS attacks is proposed. With dynamic linearization techniques, the nonlinear system is transformed into a linear system in the iteration domain. Then an improved MFAILC is designed to actively compensate the lost data caused by DoS attacks, where the estimation of pseudo‐partial derivative (PPD) is improved by establishing the AR model. The proposed algorithm can weaken the adverse effects of the DoS attacks and ensure the excellent tracking performance of the system. Finally, the stability of the method is proved, and the effectiveness of the proposed algorithm is demonstrated by a numerical example. By combining the characteristics of MFAC and ILC, we proposed the MFAILC algorithm for the system subject to DoS attacks. An innovative approach to PPD estimation by introducing AR model in the DoS attacks environment and a new compensation algorithm based on PID idea in the iterative domain is proposed.

This research proposes a robust nonlinear hybrid control approach to the speed control of a multi-input-and-multi-output separately excited DC motor (SEDCM). The motor that was under consideration experienced parametric uncertainties and load disturbances in the weak field region. The proposed technique aims to merge the benefits of adaptive backstepping (AB) and integral sliding mode control (ISMC) to enhance the overall system’s robustness. The unknown parameters with load disturbances are estimated using an adaptation law. These estimated parameters are incorporated into the controller design, to achieve a highly robust controller. The theoretical stability of the system is proved using the Lyapunov stability criteria. The effectiveness of the proposed AB–ISMC was demonstrated by simulation, to track the reference speed under parametric uncertainties and load disturbances. The control performance of the proposed technique was compared to that of feedback linearization (FBL), conventional sliding mode control (SMC), and AB control laws without and with the adaptation law. Regression parameters, such as integral square error, integral absolute error, and integral time absolute error, were calculated to quantitatively analyze the tracking performance and robustness of the implemented nonlinear control techniques. The simulation results demonstrated that the proposed controller could accurately track the reference speed and exhibited robustness, with steady-state error accuracy. Moreover, AB–ISMC overperformed, compared to the FBL, SMC, AB controller without adaptation law and AB controller with adaptation law, in reducing the settling time by factors of 27%, 67%, 23%, and 21%, respectively, thus highlighting the superior performance of the proposed controller.