Yaw Control Research Articles

Multi-row extremum seeking for wind farm power maximization

This paper presents results from wind tunnel experiments to evaluate power gains from wake steering via yaw control. An experimental scaled wind farm with 12 turbines in an aligned rectangular array is used. Wake steering is performed by yawing turbines using a closed-loop algorithm termed the Log-of-Power Proportional Integral Extremum Seeking Control (LP-PIESC). Two configurations are considered. In the first configuration, the turbines in the first two upstream rows are controlled. In the second case, yaw control is applied to the turbines in the first upstream row and the third row. For both cases, uncontrolled turbines have no yaw misalignment. The results show that by independent parallel maximization of the power sum of a reduced number of turbines, it is possible to obtain a close approximation of the true maximum power. The data shows that the LP-PIESC algorithm can converge relatively fast compared to traditional ESC algorithms.

Evaluation of LES-based time-decoupled model-predictive control in different wind farm layouts

Very recently, Janssens and Meyers (Wind Energy Sci., vol. 9, 65–95, 2024) proposed a time-decoupled model-predictive control (TD-MPC) framework for wind farm power optimization using a control model based on coarse-grid large-eddy simulation (LES). In a receding-horizon fashion, by computing the controls for the next time window based on a prediction of the flow (so ahead of time), they achieved (almost) real-time computational speed. The current paper further explores the potential of this approach in the context of dynamic yaw control, under the assumption of exact state knowledge. To that end, the LES-based, TD-MPC framework is validated on a reference wind farm for three different wind directions, for which we observe gains ranging from 4 (for staggered configurations) to 42% (for aligned configurations w.r.t. the wind direction). The LES-based controller always outperforms a Betz optimal controller and a static (wake model based) yaw controller at near real-time speed. An analysis of the yaw angles reveals that the framework is very effective in steering the wakes away from downstream turbines in upstream and downstream regions, synchronized to the turbulent inflow. By comparing the performance of the LES-based controllers (based on the actuator disc turbine model) against a reference simulation that uses a more accurate actuator sector model, we illustrate the robustness of the approach in the presence of the additional model error.

Optimizing Wind Farm Efficiency through Active Yaw Control: A Neural Network-Aided Game Theory Approach

This research investigates the potential of a game-theoretic-based Active Yaw Control (AYC) strategy to enhance power generation in wind farms. The proposed AYC strategy in this study replaces traditional look-up tables with a trained Artificial Neural Network (ANN) that determines the optimal yaw misalignment for turbines under time-varying atmospheric conditions. The study examines a hypothetical 3x2 rectangular arrangement of NREL 5-MW wind turbines. The FAST.Farm simulation tool, utilizing the dynamic wake meandering (DWM) model, is employed to assess both the power performance and structural load on the wind turbines. When tested with two different inflow directions and ambient turbulence (10%), the AYC strategy demonstrated a maximum increase in total power output of 2.6%, although it affected individual turbines differently. It also exhibits an increase in some structural loads, such as tower-top torque, while some components experience a slight reduction in load. The results underscore the effectiveness of the ANN-guided game-theoretic algorithm in improving wind farm power generation by mitigating the negative impact of wake interference, offering a scalable and efficient method for optimizing large-scale wind farm. However, it is essential to evaluate the overall impact of AYC on wind farm efficiency in terms of both Annual Energy Production (AEP) and structural loading under various atmospheric conditions.

Experimental and numerical investigation on the potential of wake mixing by dynamic yaw for wind farm power optimization

This study investigates open-loop dynamic yaw control as a strategy for enhancing wind farm performance through wake mixing. The focus is on understanding the potential for enhanced wake mixing under different turbulence intensities, and the mechanisms triggering mixing. Experimental tests are conducted using scaled wind tunnel experiments with two model wind turbines. The wake flow is analysed by large eddy simulations (LES). Dynamic yawing is prescribed by sinusoidally varying the yaw angle at different excitation frequencies. The study reveals that dynamic yaw control, particularly at low inflow turbulence, leads to increased wake mixing. The resulting enhanced wind farm power capture has a rather flat maximum around an optimal Strouhal number. This is in contrast to high inflow turbulence, where the effectiveness of the control strategy is significantly reduced. The meandering motion of the wake induced by dynamic yaw excitation is identified as the key mechanism for improved wake recovery.

Application of Continuous Stability Control to a Lightweight Solar-Electric Vehicle Using SMC and MPC

This paper investigates the application of contusion stability yaw control of a lightweight solar-electric vehicle. The vehicle’s customized design envelope makes it more sensitive to variations in load due to its low weight and relatively large size. To address this issue, control strategies were developed using differential motor torques to generate direct yaw moments using the vehicle’s rear in-wheel motors. This paper introduces the working conditions of solar vehicles and demonstrates the necessity of stability control. Vehicle parameters such as mass and center of gravity position are obtained to apply control to the real vehicle. The paper then describes two stability control strategies, using (i) sliding-mode control (SMC) and (ii) model predictive control (MPC). To account for the road bank angle of the test area and the impact of additional weight from a driver and passenger, a Kinematic-Based Observer is designed to estimate the vehicle’s side-slip based on measured values. To collect real-time data, a dSPACE MicroAutobox was installed on the solar vehicle. The results show the effect of the observer and controllers under different vehicle speeds and load conditions. Finally, closed-loop simulation results are presented to support the findings from the open-loop testing.

Simulation and Controller Design for a Fish Robot with Control Fins.

In this paper, a nonlinear simulation block for a fish robot was designed using MATLAB Simulink. The simulation block incorporated added masses, hydrodynamic damping forces, restoring forces, and forces and moments due to dorsal fins, pectoral fins, and caudal fins into six-degree-of-freedom equations of motion. To obtain a linearized model, we used three different nominal surge velocities (i.e., 0.2 m/s, 0.4 m/s, and 0.6 m/s). After obtaining output responses by applying pseudo-random binary signal inputs to a nonlinear model, an identification tool was used to obtain approximated linear models between inputs and outputs. Utilizing the obtained linearized models, two-degree-of-freedom proportional, integral, and derivative controllers were designed, and their characteristics were analyzed. For the 0.4 m/s nominal surge velocity models, the gain margins and phase margins of the surge, pitch, and yaw controllers were infinity and 69 degrees, 26.3 dB and 85 degrees, and infinity and 69 degrees, respectively. The bandwidths of surge, pitch, and yaw control loops were determined to be 2.3 rad/s, 0.17 rad/s, and 2.0 rad/s, respectively. Similar characteristics were observed when controllers designed for linear models were applied to the nonlinear model. When step inputs were applied to the nonlinear model, the maximum overshoot and steady-state errors were very small. It was also found that the nonlinear plant with three different nominal surge velocities could be controlled by a single controller designed for a linear model with a nominal surge velocity of 0.4 m/s. Therefore, controllers designed using linear approximation models are expected to work well with an actual nonlinear model.

Data-driven constrained reinforcement learning algorithm for path tracking control of hovercraft

This paper presents a novel Constrained Backstepping Reinforcement Learning (CBRL) approach designed for path tracking of hovercraft. The approach addresses the challenges posed by complex modeling, multiple constraints, and large sideslip angles during high-speed maneuvers of underactuated hovercraft. Initially, a unique state constraint function is formulated, incorporating constraint boundaries related to navigation speed and path curvature. Additionally, transformations are applied to the yaw angular velocity and virtual yaw control law, ensuring that the yaw angular velocity remains within safety limits. Subsequently, a data-driven backstepping reinforcement learning optimal approach, coupled with a line-of-sight guidance law, is employed to guide the hovercraft along the intended path by regulating its yaw angle. Simultaneously, the backstepping reinforcement learning optimal control scheme is used to regulate the surge speed of the hovercraft above the resistance peak speed, thereby preventing excessive sideslip angles during path tracking. Neural network observers are integrated into the design process of both the yaw and surge controllers to monitor system dynamics in the presence of interference. Through simulation, the effectiveness and feasibility of the proposed CBRL algorithm are demonstrated.

Numerical investigation on the transverse jet into a supersonic crossflow with different pressure ratios

The jet can be applied to the yaw control of a projectile, however, the complex interaction of the jet with the supersonic mainstream makes the flow field complex and yaw force unpredictable. To reveal the evolution of flow structures under different pressure ratios (PRs), or momentum flux ratios, a transverse sonic jet injected into a supersonic laminar crossflow has been studied numerically. Large-eddy simulations are employed to simulate the flow fields and evolution tendency of flow structures under different PRs of 10, 50, 100, 300, and 500. Our results show clearly the shock and flow structures of the jet interaction with crossflow under different PRs. Moreover, we find that, with the increase of PR, a larger upstream recirculation zone (RZ) and jet shock core appear, which accelerates the transformation of the bow shock (BoS) and the instability of the jet shear layer due to its stronger interaction with the crossflow. In addition, a high PR also accelerates RZ instability and produces a strong compressing effect on the major counter-rotating vortex pair in the jet flow, which makes the streamwise vortex tube stronger in the wake. These findings provide important information for applications of jet control of projectiles.

Research on lateral stability control strategy for distributed drive electric vehicles considering driving style

To improve the handling stability of distributed drive electric vehicle (DDEV), a lateral stability control strategy considering driving style is proposed. Firstly, a driving style recognition model based on support vector machines (SVM) was constructed to identify driving styles in different lateral motion scenarios. Then, a layered architecture is used to design a driver/Active Front Steering (AFS)/Direct Yaw Control (DYC) stability coordination control strategy. The upper layer is based on the phase plane of the sideslip angle and extension control theory, dividing the vehicle operating range into classical, extension, and non domains. In the middle level, three control strategies are designed for different work domains. In the classical domain, a driver/AFS coordinated control strategy based on non-cooperative Nash games is constructed, and the weight coefficients in the AFS system cost function are modified based on driving characteristics; Construct a coordinated control strategy for AFS/DYC systems based on cooperative Pareto games in the extension domain; In non-domain, construct a direct yaw torque control strategy based on twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The lower layer aims to minimize tire utilization and optimizes the allocation of additional yaw moment. Finally, by establishing Hardware in the Loop (HIL) experiment platform, the effectiveness of the proposed control strategy is verified in lane changing scenarios of different styles of drivers. Compared to DYC stability control based solely on TD3, the maximum yaw rate and standard deviation of the lateral stability control strategy considering driving style reduce by 2.4 % and 10.82 %, respectively, and the maximum and standard deviation of the sideslip angle reduce by 29.29 % and 39.24 %, respectively, thereby improving the vehicle's handling stability.

Yaw control strategy optimization

In recent years, with the rapid development of large-scale wind turbines, the issue of yaw misalignment has attracted increasing attention. This problem not only reduces the power generation capacity of the turbine but also affects the fatigue load on its blades. Therefore, in this paper, the shortcomings of common yaw control strategies were introduced first, and we propose a decoupling approach for wind direction angle and yaw angle to eliminate physical deviations and improve accuracy in aligning with the wind direction.

Coordinated control of yaw and roll stability in heavy vehicles considering dynamic safety requirements

In the field of heavy vehicle stability research, traditional safety requirements are often based on static scenario settings. However, the complexity and variability of actual road environments require safety control strategies that can be adapted to different driving conditions and environmental changes in real-time. To address this challenge, the paper proposes a coordinated control strategy for yaw and roll stability that considers the dynamic safety requirements. First, a quantitative analysis method for vehicle stability is proposed based on the dissipated energy theory, taking into account the lateral-vertical dynamics coupling characteristics. Additionally, a dynamic safety requirements identification model is developed by integrating the vehicle's future driving risk prediction algorithm. To meet dynamic safety requirements, a dynamic weight model predictive control method based on randomized ensembled double Q-learning reinforcement learning is designed. This method adjusts the control weights of yaw and roll stability online to flexibly address various destabilization risks, aiming to achieve more precise and efficient stability control. Through simulation and experimental verification, the results demonstrate that the proposed coordinated control strategy can effectively enhance the stability and safety of heavy vehicles in complex and dynamic driving environments.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

The Modeling and Control of a Distributed-Vector-Propulsion UAV with Aero-Propulsion Coupling Effect

A novel distributed-vector-propulsion UAV (DVPUAV) is introduced in this paper, which has the capability of Vertical takeoff and landing (VTOL), and can realize relatively high-speed cruise. As the core of the DVPUAV, the propulsion wing designed under the guidance of the integration idea is not only a lifting body but also a propulsion device and a control mechanism. However, this kind of aircraft has a series of difficult problems with complex aero-propulsion coupling, flight modes switching, and so many inputs and control coupling. In order to describe this coupling effect to improve the accuracy of dynamics, an aero-propulsion coupling model is developed, considering both computation reliability and real-time. Afterward, a unique control framework is designed for the DVPUAV. By optimizing control logic, this control framework realizes the decoupling of longitudinal and lateral directional control and even the decoupling of roll and yaw control. Next, based on the Iterative linear quadratic regulator (ILQR), a new Model Predictive Control (MPC) controller with the ability to solve complex nonlinear problems is proposed which achieves the unification of the controller for the full flight envelope. Finally, the good performance of the control framework and controller is verified in the whole process of the flight simulation from take-off to landing.

Influence of platform dynamic yaw motions on wake, and subsequent impacts on downwind turbine power and loads.

The floating foundations of FOWTs cause turbine motions that introduce new unknowns to the field of wind turbine wake estimation, that again leads to uncertainties in the load and power estimation of wake affected turbines. Recent studies have shown significant effects of floater motions on power production and emphasizes the need for the effect to be integrated into existing wind farm energy yield tools. Also, field measurements in installed FOWT wind farms have shown that there are measurable effects of the wake on floater yaw motions and mooring line loads.This paper describes a study that aims to quantify the effect of time-varying yaw misalignment, from floater motion and/or turbine yaw control, on the power production and turbine loads. 2 IEA 15 MW wind turbines supported by the UMaine VolturnUS-S semisubmersible, serves as the case study. The wind field and power production was simulated including the effect of steady and time-varying yawed inflow. The response of the downwind turbine in the waked wind field was simulated.Power production and loads for the downwind turbine in yaw cases are benchmarked against results for the downwind turbine without time varying turbine yaw and show that including the upwind turbine yaw motion resulted in higher mean power (up to 4%), but also larger power fluctuations. It also lead to larger standard deviations in the mooring line tension. These effects seem to decrease with increasing wind speeds and turbine spacing, and increase with the amplitude of yaw motion.

Dynamic characteristics and application of dual throat fluidic thrust vectoring nozzle

Thrust vectoring technology plays an important role in improving the maneuverability of aircraft. In order to overcome the disadvantages of mechanical thrust vectoring nozzles, such as complications of structure and significant increases in weight and cost, fluidic thrust vectoring nozzles are proposed. Dual Throat fluidic thrust vectoring Nozzle (DTN) has received wide attention due to its excellent thrust vectoring efficiency and minimal thrust loss. In this study, three-dimensional unsteady numerical simulations of a single axisymmetric DTN are conducted first to analyze its dynamic response. Then the pitch and yaw control characteristics of DTN equipped on a flying-wing aircraft are investigated. It is found that the dynamic response will experience three stages: rapid-deflecting stage, oscillating stage, and steady stage. A complete recirculation zone forms at the end of the rapid-deflecting stage, which pushes the primary flow to attach to the wall opposite the secondary injection. Meanwhile, the exhaust flow is deflected. In terms of DTN’s application, the DTN equipped on the flying-wing aircraft is capable of providing effective pitch and yaw moments at all angles of attack and Mach numbers. In addition, continuous pitch and yaw moments can be obtained by adjusting the secondary mass flow ratios. The control moment is generated due to the asymmetrical pressure distribution of nozzle surface, which is mainly contributed by the pressure decrease on the secondary injection surface. Moreover, the DTN equipped on the flying-wing aircraft has a relatively high thrust vectoring efficiency of around 5°/% and a thrust coefficient of around 0.95 when nozzle pressure ratio equals 4. These results provide an important theoretical basis for the practical application of DTN.

Coupled funnel control for supersonic tailless aerial vehicle on penetrating counter air

Supersonic Tailless Aerial Vehicles (STAVs) will become an essential force in Penetrating Counter Air (PCA), but STAVs do not have the traditional horizontal and vertical tails, making pitch and yaw control difficult. The attack angle and the sideslip angle need to be limited to ensure that the engine inlet and the aerodynamic rudder at the rear of the vehicle can work properly, which is the so-called security constraints. In addition, the tracking error of the aerodynamic angle needs to be limited to achieve effective attitude control or high-accuracy tracking of trajectories, which is the so-called performance constraints. To this end, an attitude control method that meets the needs of PCA has been devised, based on constraint definition, coupled constraints handling, and control law design. Firstly, mathematical descriptions of the security constraints, performance constraints, and control constraints are given. Secondly, two treatment methods, coupled command filter and coupled funnel control are proposed for the aerodynamic angle coupled constraint problem. Finally, based on Nonlinear Dynamic Inverse (NDI) design, the coupled funnel controller is designed and validated by simulation for two typical mission scenarios, high-altitude penetration and low-altitude surprise defence. The proposed control method not only satisfies the security and performance constraints of STAV attitude control but also is highly robust.

Dynamic wind farm flow control using free-vortex wake models

Abstract. A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation and an extension with wind direction preview. Under realistic variations in wind direction and wind speed, the preview-enabled look-up table controller yielded the largest gains in power production. The novel controller based on the free-vortex wake produced smaller gains in these conditions while yielding more power under large changes in wind direction. Additionally, the novel controller demonstrated potential for a substantial reduction in yaw actuator usage.

Cooperative control strategy of trajectory tracking and driving stability for distributed-drive vehicles under extreme conditions

Active chassis dynamics control has become increasingly important in recent years for achieving high-level automated driving of intelligent electric vehicles. To improve tracking performance of the commonly-used model predictive control (MPC) in extreme scenarios, such as emergency lane changing, an improved MPC motion stability controller (MPC_ISMC) based on sliding mode variable structure active steering control and differential torque yaw control is proposed. Firstly, an MPC-based trajectory tracking controller is designed to track the desired trajectory. Subsequently, active front-wheel steering (AFS) and direct yaw moment (DYC) controllers are designed by using the backstepping sliding mode variable structure and the adaptive super-twisting sliding mode variable structure, respectively. Moreover, a cooperative control law for the AFS and the DYC is formulated by the smooth switching function and intervention intensity factor with respect to vehicle stable state and trajectory tracking error. After that, a torque distribution scheme of four wheels is optimized to achieve the additional yaw moment target. Finally, the effectiveness of the proposed cooperative control strategy is validated using a hardware-in-the-loop platform. The results indicate that the proposed controller has better performance in trajectory tracking and vehicle stability under extreme conditions compared with traditional MPC and other methods.

Increased power capture efficiency of large-scale wind turbines using model-free coordinated pitch, yaw, and torque control with wind direction estimation in diverse environmental conditions

Offshore large-scale wind turbine systems (LSWTS) face challenges in pitch and yaw management mainly due to unreliable wind direction measurements, delayed responses, and redundant yaw movements, reducing their optimal efficiency under various offshore renewable environmental conditions. To address this problem and increase power capture, this study introduces a model-free coordinated super-twisting pitch, yaw, and generator torque control for LSWTS under various wind speed and direction conditions. To achieve this, a dynamic model of the weather vane is first designed to accurately measure the wind directions under fluctuating wind speed and environmental conditions. At the same time, the onshore/offshore LSWTS weather vane model is formulated to operate under optimal conditions considering air density, temperature, humidity, and external disturbances. Then, super-twisting control based on an enhanced adaptive barrier function is proposed for the coordinated control operation of the pitch, yaw, and generator torque actuators for stable maximum power extraction of the offshore LSWTS. Finally, simulation and comparative studies are presented to signify the increased power capture ability of the proposed scheme for both a 4.8 MW benchmark and a 20 MW PMVG-based offshore LSWTS to verify robustness against wind gusts, direction changes, and model uncertainties.

Wind farm optimization by active yaw control strategy using machine learning approach

ABSTRACT Recent advancements in wind farm (WF) construction have prompted designers to focus on optimizing WF performance and minimizing the negative effect of wake interference on power output. One proven technique to reduce wake influence and improve power generation is wake steering, which involves adjusting the yaw angles of wind turbines (WTs). The objective of this study is to evaluate the optimization of WF using machine learning (ML), specifically an artificial neural network (ANN) approach. In the first phase of the study, an ANN power model is selected to standardize wake loss and optimize power. In the next phase, a genetic algorithm (GA) will be employed to determine the best yaw angles for the wind direction based on data collected from the ANN power model. The ANN power model might not account for maintenance tasks around the WF. The results of the optimization demonstrate that energy is maximized between wind directions of 166° to 178°. This research achieved a .97 optimized overall power ratio, compared to non-optimized scenarios, using an optimal yaw angle technique in all directions. The findings of the study demonstrate that ANN-based optimization, combined with standardized wake degradation and suitable yaw angle direction, is an efficient method for WF optimization.