A reinforcement learning (RL) based fault-tolerant control strategy is developed in this paper for wind turbine torque & pitch control under actuator & sensor faults subject to unknown system models. An incremental model-based heuristic dynamic programming (IHDP) approach, along with a critic-actor structure, is designed to enable fault-tolerance capability and achieve optimal control. Particularly, an incremental model is embedded in the critic-actor structure to quickly learn the potential system changes, such as faults, in real-time. Different from the current IHDP methods that need the intensive evaluation of the state and input matrices, only the input matrix of the incremental model is dynamically evaluated and updated by an online recursive least square estimation procedure in our proposed method. Such a design significantly enhances the online model evaluation efficiency and control performance, especially under faulty conditions. In addition, a value function and a target critic network are incorporated into the main critic-actor structure to improve our method’s learning effectiveness. Case studies for wind turbines under various working conditions are conducted based on the fatigue, aerodynamics, structures, and turbulence (FAST) simulator to demonstrate the proposed method’s solid fault-tolerance capability and adaptability.Note to Practitioners—This work achieves high-performance wind turbine control under unknown actuator & sensor faults. Such a task is still an open problem due to the complexity of turbine dynamics and potential uncertainties in practical situations. A novel data-driven and model-free control strategy based on reinforcement learning is proposed to handle these issues. The designed method can quickly capture the potential changes in the system and adjust its control policy in real-time, rendering strong adaptability and fault-tolerant abilities. It provides data-driven innovations for complex operational tasks of wind turbines and demonstrates the feasibility of applying reinforcement learning to handle fault-tolerant control problems. The proposed method has a generic structure and has the potential to be implemented in other renewable energy systems.

Transportation safety critically relies on the reliable and precise trajectory tracking control of fixed-wing autonomous aerial vehicles (AAVs). Consequently, actuator imperfections coupled with state constraints challenge AAV operations, necessitating an enhanced controller that integrates anti-windup capabilities and robust fault tolerance, particularly in dynamic transportation scenarios. This paper proposes a refined fault-tolerant control (FTC) scheme for fixed-wing AAVs to maintain high-performance and safe operation under actuator faults, external disturbances, and multiple constraints including state limits, input magnitude and rate saturations (MRS). The scheme integrates a fixed-time improved extended state observer (IESO) that rapidly estimates lumped uncertainties (including faults/disturbances) and actively compensates for input magnitude and rate constraints, which prevents performance degradation. Based on this estimation, a finite-time fractional-order FTC (FTFOFTC) is developed using fractional-order (FO) backstepping and barrier Lyapunov functions to enforce state constraints. This leverages the unique properties of FO calculus for flexible performance tuning and refined tracking dynamics. Lyapunov stability analysis proves the boundedness of all estimation and tracking errors. Extensive simulations validate that the proposed FTFOFTC guarantees desired trajectory tracking performance and system stability despite actuator imperfections, thereby significantly contributing to more robust and reliable AAV deployment in intelligent transportation systems.

Floating offshore wind turbines (FOWTs) are often subjected to heavy structural loads due to challenging operating conditions, which can negatively impact power generation and lead to structural fatigue. This paper proposes a novel reinforcement learning (RL)-based control scheme to address this issue. It combines individual pitch control (IPC) and collective pitch control (CPC) to balance two key objectives: load reduction and power regulation. Specifically, a novel incremental model-based dual heuristic programming (IDHP) strategy is developed as the IPC solution to reduce structural loads. It integrates the online-learned FOWT dynamics into the dual heuristic programming process, making the entire control scheme data-driven and free from dependence on analytical models. Furthermore, the proposed method differs from existing IDHP methods in that only partial system dynamics need to be learned, resulting in a simplified design structure and improved training efficiency. Tests using a high-fidelity FOWT simulator demonstrate the effectiveness of the proposed method.Note to Practitioners—This work achieves power regulation and load reduction simultaneously for FOWTs to guarantee the reliability of wind turbine operations. Such a task is still an open problem because existing FOWT controllers commonly rely on accurate turbine models and lack adaptability to potential uncertainties and errors in practical situations. A new data-driven, model-free control strategy based on the RL technique is developed to address these issues. Our method has the ability to capture potential changes in system dynamics by updating a so-called incremental model via online measurements. Unlike current advances in this direction that need to approximate the whole system dynamics, the proposed control algorithm only needs to update partial system information for the incremental model. This naturally simplifies the design structure and enhances learning effectiveness while providing adaptability and robustness against uncertainties and errors. The proposed control strategy can also be extended and implemented in other systems, such as autonomous systems and other renewable energy systems.

A model-free deep reinforcement learning (DRL) method is proposed in this article to maximize the total power generation of wind farms through the combination of induction control and yaw control. Specifically, a novel double-network (DN)-based DRL approach is designed to generate control policies for thrust coefficients and yaw angles simultaneously and separately. Two sets of critic-actor networks are constructed to this end. They are linked by a central power-related reward, providing a coordinated control structure while inheriting the critic-actor mechanism's advantages. Compared with conventional DRL methods, the proposed DN-based DRL strategy can adapt to the distinctive and incompatible features of different control inputs, guaranteeing a reliable training process and ensuring superior performance. Also, the prioritized experience replay strategy is utilized to improve the training efficiency of deep neural networks. Simulation tests based on a dynamic wind farm simulator show that the proposed method can significantly increase the power generation for wind farms with different layouts.