Active Control Strategy Research Articles

Numerical Investigation of Aero-Optical Distortions from High-Speed Turbulent Boundary Layers

Wall-modeled large-eddy simulations of turbulent boundary-layer flows over a flat plate at Mach 3.5, 7.87, and 13.64 were carried out, and the aero-optical distortions resulting from density fluctuations were investigated. The conditions for the Mach 3.5 case match those of a direct numerical simulation in the literature. The Mach 7.87 conditions are representative of the Hypersonic Wind Tunnel at Sandia National Laboratories, and the Mach 13.64 conditions match those of the Arnold Engineering Development Complex Hypervelocity Tunnel 9. The wall-modeled large-eddy simulations were validated using available experimental and DNS reference data. The normalized root-mean-square optical path difference for all three cases is in good agreement with respective data obtained from reference direct numerical simulations and experiments (within 1.53% for M∞=3.5, 1.25% for M∞=7.87, and 12% for M∞=13.64, compared to numerical data). Above M∞=5.0, the normalized path difference obtained from the simulations is above the value predicted by a semi-analytical relationship by Notre Dame University. With increasing Mach number, the bulk contribution to the total optical distortion shifts toward the boundary-layer edge. In addition, a proper orthogonal decomposition reveals that the nature of the dominant density fluctuations, which are located near the boundary-layer edge, changes from three-dimensional to two-dimensional. Understanding how the coherent structures contribute to the optical path difference may pave a way toward devising active flow control strategies geared toward a reduction of the optical path difference.

Research on active-passive integrated vibration isolator based on metal rubber and piezoelectric actuator

Abstract The paper introduces a novel active-passive integrated vibration isolator based on metal rubber and piezoelectric actuator, along with an adaptive active vibration control strategy. The active control strategy employs the adaptive dynamic step filtered-x normalized least mean squares algorithm, allowing the step size to adaptively adjust with the error. The secondary control path of the algorithm is modeled using the enhanced rate-dependent Prandtl–Ishlinskii model and the auto-regressive with extra inputs model. The active-passive integrated vibration isolator achieves broadband vibration isolation from 10 to 200 Hz. Compared to passive isolation, the transmissibility decreases from 0.99 to 0.056 at 10 Hz, from 3.02 to 0.068 at the resonance frequency, and from 0.057 to 0.046 at 200 Hz. This study provides a theoretical and experimental foundation for the design of a novel, broadband, and efficient active-passive integrated vibration isolator structure and active control method.

An active protection method based on superimposed phase currents for active distribution systems

To enhance the fault current amplitude of active distribution systems during the weak fault period and decrease the influence of the measurement equipment noise, this paper proposes an active protection method based on superimposed phase currents for active distribution systems. This method is shown as follows. First, the reference value of the filter inductor current of each distributed generator (DG) is set as different values when the fault intensity of the active distribution system changes. Second, the superimposed phase current in each relay is obtained by subtracting each sample of phase current from its corresponding sample of phase current in the previous cycle. Third, the fundamental phase angle of the superimposed phase current is extracted. This is because the fundamental current component of the superimposed current cannot be affected by the active control strategy, which is proved in this paper in detail. Meanwhile, the feature direction is built by fundamental phase angles at both ends of the line to detect faulty zone. Finally, the proposed protection method has been validated in a modified IEEE 13-bus test system, and the test results prove the effectiveness of the proposed active protection method that can enhance the faulty feature to detect faults.

Model experimental studies on active heave compensation control strategy for electric-driven offshore cranes

Ship-mounted heave compensation offshore cranes are indispensable for isolating connected payloads from the support vessel during lifting operations under harsh sea conditions. In this paper, an innovative adaptive robust control strategy is presented for the electric-driven active heave compensation (EDAHC) system, combining an equivalent model predictive control (EMPC) method with a bias proportional integral derivative (BPID) framework to effectively mitigate the adverse effects of wave-induced heave motions from the support vessel on the suspended payload. Building upon the inherent field-oriented control in the permanent magnet synchronous motor (PMSM), the BPID-based control structure is introduced, motivated by its prompt responsiveness and robust resistance against model discrepancies and irregular disturbances. Facilitated by a torque compensation mechanism, the EMPC-based control scheme, synthesized with an autoregressive integrated moving average (ARIMA)-based heave motion prediction algorithm, is subsequently developed to achieve nonlinear friction deadzone correction and adaptive regulation of BPID parameters, thereby ensuring optimal performance of the EDAHC system within specified state and input constraints. Comparative experimental tests conducted on a scaled EDAHC testbed validate the superior capabilities of the proposal in station-keeping, position tracking, constraint satisfaction, and robustness against parametric uncertainties.

Wind power prediction through acoustic data-driven online modeling and active wake control

Accurate online prediction of wind power is essential for the reliable operation of power systems. However, the precision of wind turbine power forecasts is often hindered by wake effects and inadequate online modeling capabilities. To tackle this issue, this paper proposes a data-driven online prediction method for wind turbine power, leveraging acoustic data for training. The approach involves a neural network that learns acoustic feature changes related to wind output power, by enhancing the kernel extreme learning machine (KELM) technique and applying data augmentation. Additionally, to address the wake effects in real wind fields, active wake control is integrated, considering that most wind turbines operate in the wake of upstream turbines. Experiments using actual wind tunnel measurement data were conducted to assess the performance of the proposed method. The findings demonstrate that this method surpasses other techniques without increasing computational costs. The mean absolute error (MAE) of the proposed method was only 0.4111 and 0.4302, and the root mean square error (RMSE) was just 0.4868 and 0.5195, respectively, in both experiments. Moreover, the proposed acoustic data-driven online power prediction method proves stable in high background noise environments and achieves accurate wind power prediction with just a 10% data sample following the implementation of the active wake control strategy. This work significantly contributes to the efficient utilization of wind energy resources.

Fusing binocular vision and deep learning to detect dynamic wheel-rail displacement of high-speed trains

Large displacements between the wheel and rail lead to track damage or even train derailment. Detecting dynamic wheel-rail displacement (DWRD) can provide a technical means for discriminating the operational health status of rail vehicles and formulating active control strategies. In this paper, a new method, which fuses binocular vision (BV) and deep learning (DL), is proposed to detect the DWRD online, the method is abbreviated as BV-DL. First, a binocular camera is used to capture the dynamic wheel-rail contact video online, while two lasers are equipped to focus on the wheel-rail contact region to locate some keypoints in the wheel-rail contact images. Then, an ROI (region of interest) detection network is proposed to recognize the wheel-rail contact region automatically, and a keypoint detection network is proposed to detect the wheel-rail pixel displacement (WRPD). Finally, a 3D coordinate transformation (3DCT) model is built to calculate the wheel-rail world displacement in real-time, i.e., the DWRD between the keypoints. To test the performance of the method, a laser displacement sensor-based method is used to verify the reliability of the 3DCT model. Meanwhile, a traditional image processing method, a MobileNet V3-based method, and a YOLOX-s-based method are introduced to predict the WRPD. Experimental results show that compared with the four methods, the proposed BV-DL method can achieve optimal results in pixel coordinate calculation and 3D coordinate computation.

Investigation of multifunctional grid-tied PV inverter with even and odd order harmonic mitigation using active and harmonic power weight control strategy

Investigation of multifunctional grid-tied PV inverter with even and odd order harmonic mitigation using active and harmonic power weight control strategy

Study of Resonance between Bogie Hunting and Carbody Mode via Field Measurements and Dynamic Simulation.

By addressing the phenomenon of carbody abnormal vibrations in the field, the acceleration of the carbody and bogie was measured using accelerometers, and the diamond mode of the carbody was identified. The equivalent conicity of the wheelset and the acceleration at the frame end indicated that the shaking of the carbody was caused by bogie hunting. In the SIMPACK simulation, the acceleration frequency and amplitude at the frame end and midsection of the side beam were calculated. The lateral deformation amplitude of the side beam in the finite element model was extracted, and a modal shape function for the diamond-shaped mode was established. By utilizing the modal vibration equation, the modal generalized forces of the carbody were computed, revealing that, during carbody shaking, the yaw damper force contributed significantly among the forces of the secondary suspension, with the phase difference between the front and rear bogies approaching 180°. This insight offers a novel perspective for subsequent active control strategies. Subsequently, these modal generalized forces were applied as external excitation to a coupled vibration model encompassing both the carbody and transformer. Aiming to reduce the acceleration amplitude at the side beam, the transformer was treated as a dynamic vibration absorber, allowing for the optimization of its lateral suspension parameters. As a result, the lateral and vertical acceleration amplitudes at the side beam were concurrently reduced, with the maximum decrease reaching 58.5%, significantly enhancing the ride comfort.

Design and Implementation of a MIMO Integral Resonant Control for Active Vibration Control of Pedestrian Structures

In contemporary construction, the prevalence of vibration serviceability issues in lightweight and slender structures has become increasingly common, owing to advancements in building materials and construction methods. While these structures often meet the criteria for ultimate limit states, they can still elicit complaints due to excessive vibrations induced by human activity. To address this challenge, the integral resonant control (IRC) technique has emerged as a favored approach for actively damping vibrations in various systems. This study introduces a fresh perspective by proposing the implementation of a multi-input multi-output (MIMO) IRC scheme for active vibration control (AVC) specifically tailored for pedestrian structures utilizing inertial mass actuators. This application of MIMO IRC for AVC represents a novel advancement in the field, offering a new solution to address vibration issues in lightweight and slender structures. Building upon a common framework and design methodology outlined in previous research, this work presents a novel application of MIMO IRC for AVC. The designed controller undergoes rigorous testing and is implemented on a laboratory floor structure to validate its efficacy. The outcomes of this study demonstrate the effectiveness of the proposed MIMO IRC scheme in actively damping vibrations, thereby enhancing the serviceability and comfort levels of lightweight and slender structures subjected to human-induced excitations.

A parallelized environmental-sensing and multi-tasks model for intelligent marine structure control in ocean waves coupling deep reinforcement learning and computational fluid dynamics

In addressing the active control challenges of marine structures in ocean waves, a coupling model is proposed combining computational fluid dynamics (CFD) and deep reinforcement learning (DRL). Following the Markov decision process (MDP), the proposed DRL-CFD model treats the wave fields and simplified marine structures as the environment and the agent, respectively. The CFD component utilizes the PIMPLE algorithm to solve the Navier–Stokes equations, in which the free surface is reconstructed using the volume of fluid method. The DRL component utilizes the Soft Actor-Critic algorithm to realize the MDP between marine structures and the wave fields. Three simulation cases with different control purposes are conducted to show the effectiveness of the DRL–CFD coupling model, including the active controls for wave energy absorption, attenuation, and structure heave compensation. Comparative analyses with passive (resistive) control are performed, demonstrating the advantages of the DRL–CFD coupling model. The results confirm that the proposed coupling model enables the marine structure to observe the wave environment and generate effective active control strategies for different purposes. This suggests that the model has the potential to address various active control challenges of marine structures in ocean waves, while being capable of environmental sensing and handling multiple tasks simultaneously.

Prescribed-time active fault-tolerant control of dynamic positioning vessels based on improved iterative learning observer

Considering the dynamic positioning (DP) vessel control system with actuator faults and unknown environment disturbances, a fault-tolerant control scheme based on improved iterative learning observer (ILO) is proposed in this article. Firstly, the kinematics and dynamics models of DP vessels are established containing the actuator faults and environmental disturbances. Secondly, an improved ILO is designed to handle the unknown faults and disturbances concurrently with higher estimation precision by introducing the state information of the previous moment. Further, a prescribed-time active fault-tolerant control scheme is presented on the basis of the improved ILO and prescribed-time stability. And the stability of proposed ILO and DP fault-tolerant control system are analyzed based on Lyapunov theory. Finally, the effectiveness and advantages of the presented ILO and active fault-tolerant control scheme are illustrated by numerical simulations and comparisons.

Deep reinforcement learning for tuning active vibration control on a smart piezoelectric beam

Piezoelectric transducers are used within smart structures to create functions such as energy harvesting, wave propagation or vibration control to prevent human discomfort, material fatigue, and instability. The design of the structure becomes more complex with shape optimization and the integration of multiple transducers. Most active vibration control strategies require the tuning of multiple parameters. In addition, the optimization of control methods has to consider experimental uncertainties and the global effect of local actuation. This paper presents the use of a Deep Reinforcement Learning (DRL) algorithm to tune a pseudo lead-lag controller on an experimental smart cantilever beam. The algorithm is trained to maximize a reward function that represents the objective of vibration mitigation. An experimental model is estimated from measurements to accelerate the DRL’s interaction with the environment. The paper compares DRL tuning strategies with [Formula: see text] and [Formula: see text] norm minimization approaches. It demonstrates the efficiency of DRL tuning by comparing the control performance of the different tuning methods on the model and experimental setup.

Optimized PI-PDF active structural acoustic control of smart FG GPL-reinforced closed-cell metallic foam sandwich plate

This study investigates Active Structural Acoustic Control (ASAC) applied to sandwich plates featuring Functionally Grade (FG) porous Graphene-Platelet-Reinforced Piezoelectric (GPLRP) materials. These plates incorporate a core layer with internal pores and GPLs dispersed in a metal matrix, along with piezoelectric sensors and actuators. Using a spatial state-space formulation based on linear 3D piezoelasticity theory, a semi-analytical solution is derived for the vibroacoustic response of these sandwich plates. The mechanical properties of the porous core are modeled using a closed-cell metal foam. The effects of various parameters, including porosity distributions, porosity coefficient, weight fractions of nanofiller, and geometric parameters on the radiation efficiency and radiated sound power have been investigated. Then, the validation of the proposed model is examined by comparing the natural frequencies calculated in the present study to those from the literature. This comparison allows us to assess the accuracy and reliability of our model against established findings. Radiated sound power reduction from mechanically excited structures is achieved by employing a dual-stage Proportional Integral–Proportional Derivative with Filter (PI-PDF) controller, optimized using Grey Wolf Optimization (GWO). Finally, several numerical simulations are conducted to validate the effectiveness and accuracy of the proposed active control strategy.

Robust fractional-order PID controller assisted by active disturbance rejection control for the first-order plus time-delay systems

Time-delay characteristics of various industrial processes may degrade the stability and dynamic performance of the control systems. Aiming at the problems of the existing methods in dealing with the time delay plant, a modified fractional-order proportional–integral–derivative (FOPID) controller for the first-order plus time-delay (FOPTD) system is developed. Assisted by a modified active disturbance rejection control (ADRC) scheme with increased observer bandwidth, the proposed FOPID controller inherently obtains good robustness to time-delay uncertainties and external disturbances. In addition, taking advantage of the fractional-order operator, the proposed controller can provide larger stability margin over the proportional–integral–derivative (PID) controller. By suitably establishing the relation between ADRC and FOPID controller parameters, the proposed controller can be analytically tuned based on the common design indices. A practical tuning guideline is developed according to frequency-domain characteristic analysis, making the proposed controller more acceptable to industrial application. The performance of the ADRC-based FOPID controller is tested by the control simulation of some typical FOPTD systems and a diesel engine speed regulation system. The efficiency of the ADRC-based FOPID controller is demonstrated by the comparisons with some existing controllers.

Variable Area Jet Impingement for Enhanced Junction Temperature Control of High-Power Electronics

Abstract Convective heat transfer by jet impingement cooling offers a suitable solution for high heat flux applications. Compared to techniques that rely on bulk conduction in series with convection, direct liquid impingement reduces the thermal resistance between power device hot spots and the coolant. Although capable of highly efficient cooling, static impingement devices must be designed for the worst-case cooling requirements for a transient power profile. This can result in wasted hydraulic performance. Aircraft, highway vehicles, and heavy machinery fall into this category where a substantial factor of safety is required. This work proposes a method for improving power electronics reliability by limiting temperature fluctuations at reduced coolant pressure requirements during transient power cycling using a variable area jet. Single phase jet impingement cooling is implemented in an active control scheme using a variable diameter iris mechanism as the primary nozzle architecture. In addition to pressure drop and temperature control, the active nozzle structure introduces the ability to create pulsating jet flows to further enhance the heat transfer compared to fixed-geometry nozzles. The key underlying fluid mechanics characteristic of pulsating flows is the effect of disrupting the thermal boundary layer on the electrical device surface. By introducing a variable diameter jet, eddy formation can be fine-tuned for optimal boundary layer disruption. Using the definition of the Strouhal number, vortex shedding created by the non-steady jet flows is directly correlated with the resulting Nusselt number as a function of the iris kinematics. An experimental apparatus for jet impingement thermal-fluid testing is used to evaluate the Nusselt number versus Strouhal number for a parametric study of variable diameter iris configurations. The apparatus utilizes a voice coil actuator to achieve sine and square waveforms, to vary the amplitude of actuation, and to vary the mean of actuation. Finally, power cycling with a single emulated hot spot is performed to estimate the reliability increase as a result of maintaining constant junction temperatures with the active jet impingement scheme.

Fractional modeling of unsteady flow of Jeffrey fluid using active and passive control strategies

The current work is focused on the analysis of heat and mass transfer in a magnetohydrodynamics Jeffrey nanofluid flows across a vertical plate because of these possible uses. Free convection, magnetic effect, and thermo-diffusion properties are all applied to the flow considered here. The modeling also takes into account the passive and active control of the Jeffrey nanofluid. The constant proportional Caputo fractional operator which was recently introduced is used in this work together with generalized Fick’s and Fourier’s law. Using suitable nondimensional variables, ordinary differential equations are obtained from the modeling equations, and the Laplace transform method is used to solve these modeled equations. The concentration and temperature profiles are rising functions of the fractional and thermophoresis parameters, and they drop more quickly as the values of the Schmidt, Prandtl, and Nb (Brownian motion parameters) rise. The constant proportional Caputo (CPC) fractional derivative is the best choice for achieving more stable concentration, temperature, and velocity than conventional fields. Raising the values of α will increase the rate of heat transfer and skin friction coefficient. Additionally, as compared to the classical model, the constant proportional Caputo derivative model shows more decay nature. Consequently, compared to the classical model, the constant proportional Caputo differential model has a superior memory function.

Improving Terrain Adaptability and Compliance in Closed-Chain Leg: Design, Control, and Testing

Abstract This study investigates a novel design of a reconfigurable closed-chain leg for hexapod robot with enhanced terrain adaptability. A length adjustable hydraulic cylinder is incorporated into the Theo Jansen linkage in the proposed reconfigurable closed-chain leg, allowing for flexible trajectory by adjusting the length of the hydraulic cylinder. Kinematic model and system dynamics are analyzed considering the multi-body dynamics of the proposed system. To actively adapt to different terrains with flexible footprints, a variable-domain sliding mode control strategy to adjust the length of hydraulic cylinder is investigated and compared with other control strategies. Meanwhile, an active compliant control strategy of the driving motor is analyzed and deployed to improve the stability and compliance during walking. A prototype was fabricated and tested under various configurations. Results demonstrate that the variable-domain sliding mode control algorithm exhibits fast convergence, robustness, and smooth signals for hydraulic cylinder. In addition, the proposed active compliant control strategy of the driving motor can reduce the impact force and ensure stable equilibrium during walking. Therefore, the proposed reconfigurable closed-chain leg can enhance the terrain adaptability and enrich the applications of closed-chain legged robots.

Output Feedback Active Fault Tolerant Control for a 3-DOF Laboratory Helicopter With Sensor Fault

In this paper, an output feedback-based active fault tolerant control (FTC) scheme is proposed for an unstable three degree-of-freedom (3-DOF) helicopter equipped only with angular position sensors, of which a single sensor can be faulty. Limited by the available outputs, existing fault estimation (FE) and FTC schemes cannot be applied to this helicopter because when the sensor measuring the elevation or travel angle is faulty, it does not satisfy the minimum-phase and matching conditions required by standard observers. To circumvent this problem, an adaptive interval observer is firstly designed as a fault detection and isolation (FDI) unit to indicate the occurrence and location of a fault, in contrast to the existing FDI methods that require a bank of observers and incur a high computational cost. Then a high-gain observer is combined with a sliding mode observer to form an FE unit that does not require the matching and minimum-phase conditions. Based on the estimate of the fault, an FTC scheme is constructed to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system. Finally, physical experiments on the 3-DOF helicopter verify the effectiveness of the proposed scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The 3-DOF lab helicopter serves as an ideal experimental platform for control strategies. In this paper, an active fault tolerant control (FTC) scheme is developed for a 3-DOF lab helicopter when any single sensor can be faulty. The helicopter is nonlinear and subject to external disturbances. There are only encoders measuring the attitude angles and no sensors for angular velocities. In this paper, we firstly design a fault detection and isolation (FDI) unit based on an adaptive interval observer to detect the fault and identify its location; it incurs a lower computational cost compared to existing methods that use a bank of observers. Then a high-gain observer is combined with a sliding mode observer to estimate the fault. Based on the estimate of the fault, an FTC scheme is constructed and designed using Linear Matrix Inequalities to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system.

Efficient deep reinforcement learning strategies for active flow control based on physics-informed neural networks

Reducing the reliance on intrusive flow probes is a critical task in active flow control based on deep reinforcement learning (DRL). Although a scarcity of flow data captured by probes adversely impacts the control proficiency of the DRL agent, leading to suboptimal flow modulation, minimizing the use of redundant probes significantly reduces the overall implementation costs, making the control strategy more economically viable. In this paper, we propose an active flow control method based on physics-informed DRL. This method integrates a physics-informed neural network into the DRL framework, harnessing the inherent physical characteristics of the flow field using strategically placed probes. We analyze the impact of probe placement, probe quantity, and DRL agent sampling strategies on the fidelity of flow predictions and the efficacy of flow control. Using the wake control of a two-dimensional cylinder flow with a Reynolds number of 100 as a case study, we position a specific number of flow probes within the flow field to gather pertinent information. When benchmarked against traditional DRL techniques, the results are unequivocal: in terms of training efficiency, physics-informed DRL reduces the training cycle by up to 30 rounds. Furthermore, by decreasing the number of flow probes in the flow field from 164 to just 4, the physics-based DRL achieves superior drag reduction through more precise control. Notably, compared to traditional DRL control, the drag reduction effect is enhanced by a significant 6%.

Optimization of latching control for duck wave energy converter based on deep reinforcement learning

In the field of wave energy extraction, employing active control strategies amplifies the Wave Energy Converter's (WEC) response to wave motion. In this regard, a numerical simulation study was conducted on the interaction between Duck WEC and waves under discrete latching control, proposing an optimized latching duration strategy based on Deep Reinforcement Learning (DRL). The study utilized an improved Edinburgh Duck model to establish a numerical wave flume (NWF), verifying the motion of waves and the device. A DRL and Computational Fluid Dynamics (CFD) coupled framework was developed to implement the latching control strategy. In the limited trichromatic wave testing conditions provided in this paper, the latching control time is optimized using a DRL agent, and its capture efficiency is compared with two non-predictive latching control strategies. The DRL agent control showed a 13.4% improvement relative to reference threshold control, with less than 6.3% differences compared to optimal threshold latching results. Improvement of approximately 5.6% was observed compared to optimal constant latching control. For the first time in nonlinear fluid dynamics numerical simulation, this study demonstrates the efficacy of model-free reinforcement learning with discrete latching actions, establishing an environment-driven control strategy, and having the potential to become a general strategy for WEC latching control.