This paper addresses the co-design problem of adaptive control for networked wind turbine systems that track the desired rotor speed while efficiently scheduling network communication. Unlike existing approaches, the proposed method integrates the control design and the communication considerations, ensuring asymptotic tracking of rotor speed under the generator torque saturation with significantly reduced communication load. Firstly, the communication scheme is developed using the dynamic event-triggering mechanism, which introduces the feedback signal in the sampling loop. Secondly, an auxiliary signal is designed to mitigate the negative effect of inevitable generator torque saturation, ensuring that the bounded controller asymptotically exits from saturation. Then, the adaptive torque controller is constructed with the compensation signals to guarantee asymptotic stability, and the measurement function for dynamic event-triggering is co-designed alongside the controller to regulate the sampling-induced error. Furthermore, the requirement for exact knowledge of the wind turbine systems is eliminated by utilizing an online approximator to learn the uncertain aerodynamics and parameters. Finally, it is theoretically proven that all the signals in the closed-loop system are bounded, and the rotor speed asymptotically tracks the reference rotor speed. The feasibility and advantages of the proposed method are demonstrated on the NREL 5-MW wind turbine using the high-fidelity OpenFAST simulation platform.

Floating offshore wind turbines (FOWTs) hold significant potential for developing wind energy in deep-sea areas. However, they are prone to additional motions, heavy workloads, and undesirable fluctuations under combined wind-wave loads. To tackle these issues, this paper presents a novel adaptive blade pitch control strategy with guaranteed transient performance for FOWTs operating in above-rated wind speed regions. This strategy effectively suppresses platform pitch motion to enhance system stability while maintaining the power output at its rated value. Specifically, this method initiates by defining a filtered regulation error to solve the non-affine nature of its model. An adaptive blade pitch controller is subsequently proposed, integrating an online learning approximator to cope with the unknown dynamics caused by unmeasurable external environmental inputs and model uncertainties. To further mitigate approximation errors, a nonlinear robust control law with adaptive gains is employed, enhancing the robustness and adaptive capabilities of the controller. Superior to the traditional adaptive controllers, a significant advantage of this strategy is its ability to quantify and ensure regulatory performance within predefined constraints during both transient and steady-state stages, thereby achieving high-performance control in power regulation. Finally, simulation studies are conducted using the OpenFAST software to show the capability of the presented blade pitch controller, which guarantees stable power generation with guaranteed transient performance.

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Note to Practitioners—This paper addresses the conflicting goals between power regulation and load mitigation for floating wind turbines (FWTs) to ensure the reliable operation of wind turbine systems. This remains an ongoing challenge due to the inherent complexity of existing FWT models, frequently resulting in controllers crafted using linearized representations that fail to accommodate real-world uncertainties effectively. Through the utilization of a simplified physical-based nonlinear FWT model, a novel adaptive nonlinear pitch controller emerges as a promising solution. Notably, the developed nonlinear FWT model elucidates the coupling between rotor and platform degrees of freedom clearly and succinctly, facilitating the design of intelligent controllers. Our approach demonstrates the capability to concurrently regulate power production and stabilize the platform. Additionally, an online approximator is integrated into the controller to capture system dynamics, thus augmenting adaptability and diminishing reliance on high-gain feedback compensation. Importantly, this control strategy holds promise for extension and implementation in various other renewable energy systems.

Blade pitch actuators for traditional onshore wind turbines primarily serve the purposes of regulating generated power to the rated value in high wind speed regions. Yet, when considering the floating offshore wind turbines (FOWTs) subject to wind and wave disturbances, it can be observed that improving power regulation often comes at the expense of exacerbating motion in the floating platform, leading to elevated platform loads. To address this issue, this paper proposes a novel robust nonlinear pitch controller specifically designed to achieve power production regulation while simultaneously mitigating platform pitch motion for FOWTs. Moreover, to tackle the challenge posed by unknown dynamics under wind-wave joint loads, the presented controller integrates a two-layer neural network (NN) for real-time learning of these unknown system dynamics. Meanwhile, a robust continuous term is introduced to alleviate the effects of residual reconstruction errors from the NN and external disturbances. Finally, the viability and efficacy of the proposed scheme are clearly demonstrated through comprehensive comparative studies with traditional pitch controllers conducted on the National Renewable Energy Laboratory (NREL) FAST platform.Note to Practitioners—This paper is motivated by the challenge of achieving the competing objectives between power regulation and load mitigation for floating offshore wind turbines (FOWTs) in high speed region. FOWTs have garnered significant interest in the field of renewable energy due to their advantages, including the ability to install high-powered wind turbines and reduced costs in deeper waters. However, there exist some limitations that existing FOWT controllers face including inefficiency in directly applying onshore WT control schemes, typically depend on precise turbine models and lack adaptability to potential uncertainties and errors encountered in practical situations. To tackle these limitations, this paper proposes a novel robust nonlinear controller for FOWTs considering the platform motion, which has the ability to simultaneously achieve power production regulation while platform stabilization. A two-layer neural network for real-time learning is developed to capture potential changes in system dynamics, while a robust continuous term is introduced to alleviate potential errors in the control scheme. This naturally provides adaptability and robustness against uncertainties while enhancing overall control performance. The effectiveness of the proposed control scheme is validated through simulation results.

Iterative Learning Control (ILC) is renowned for its capability to achieve precise tracking control for systems with repetitive actions at a fixed time interval. However, pursuing the dual objective of high-precision tracking and rapid convergence is a persistent challenge in the field of learning control. To address this problem, a novel ILC method is designed for a class of discrete-time linear systems subject to non-repetitive disturbances in this paper. Particularly, the updating term in ILC is constructed inspired by the principle of sliding mode control (SMC), which results in the learning process being divided into two distinct stages: a rapid reaching stage and a slow sliding stage. As a result, a balance between convergence speed and tracking performance can be ensured via the proposed ILC method. In addition, to attenuate the effects of non-repetitive disturbances, the disturbance compensation mechanism is integrated into the proposed ILC method. Moreover, the optimal value of the learning gain can be determined using the predicted root mean square (RMS) errors of subsequent iterations, eliminating the need for additional tuning actions. Finally, simulation examples are provided to validate the effectiveness and superiority of the proposed new ILC method. Note to Practitioners—For many mechanical components in mechatronic systems and robotics, the motions are repeatable. Iterative learning control (ILC) is a well-established technique ideally suited for enhancing the performance of such repetitive tasks without excessive requirements on sensor-feedback quality or control-loop bandwidth. However, most existing ILC approaches in the literature primarily focus on improving convergence accuracy, while little attention is paid to convergence speed in the iteration domain, especially in the presence of disturbances. This paper addresses the limitations of classical ILC schemes, and draws inspiration from the sliding mode control (SMC) technique. To be specific, a novel SMC-based ILC algorithm is proposed that allows to achieve a good balance between the fast convergence and precise tracking performance, especially in case of iteration variant disturbances. Also, it will be shown how the optimal learning gains can be determined. Base on the examples of multi-axis gantry robot and injection molding process, simulations support the theoretical results, and meanwhile show the effectiveness and advantage of the proposed ILC strategy.