Active Control Strategy Research Articles

Adaptive neural chattering suppression pseudo inverse active control for a class of micromilling systems with multiaxis piezoelectric actuators

For a multi-axis nonlinear milling system with piezoelectric actuators, a pseudo inverse dynamic surface active control scheme with adaptive fuzzy output-feedback is developed. The main contributions can be characterized as following: (1) to overcome the hysteresis non-linearities in the piezoelectric actuators, the new hysteresis pseudo inverse compensators are established to replace the conventional hysteresis direct-inverse model construction, which is a very challenging or even impossible work. The hysteresis pseudo inverse means that the hysteresis direct-inverse model is no longer essential any more, instead, a search algorithm for the actual control signal from the designed hysteretic temporary control laws are proposed to alleviate the hysteresis nonlinearities effectively; (2) to realized high-precision tracking control and mitigate chattering suppression in the micromilling systems, the fuzzy logic systems, the novel dynamic surface control algorithms and the modified high-gain states observer are designed to approximise the unknown continuous functions of the micromilling systems, design the high performance controller and estimate the immeasurable states in the control system, respectively; (3) the hardware-in-loop simulation platform is constructed and the experiments of tracking control and chattering suppression are implemented to validate the effectivity of the developed control scheme.

Single closed-loop active disturbance rejection control strategy of permanent magnet synchronous motor based on compensation function

Single closed-loop active disturbance rejection control strategy of permanent magnet synchronous motor based on compensation function

Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System

The widespread adoption of the power grid has led to increased attention to load frequency control (LFC) in power systems. The LFC strategy of multi-source hybrid power systems, including hydroelectric generators, Wind Turbine Generators (WTGs), and Photovoltaic Generators (PVGs), with thermal generators is more challenging. Existing methods for LFC tasks pose challenges in achieving satisfactory outcomes in hybrid power systems. In this paper, a novel method for the multi-source hybrid power system LFC task by using an optimal active disturbance rejection control (ADRC) strategy is proposed, which is based on the combination of the improved linear quadratic regulator (LQR) and the ADRC controller. Firstly, an established model of a hybrid power system is presented, which incorporates multiple regions and multiple sources. Secondly, utilizing the state space representation, a novel control strategy is developed by integrating improved LQR and ARDC. Finally, a series of comparative simulation experiments has been conducted using the Simulink model. Compared with the LQR with ESO, the maximum relative error of the maximum peaks of frequency deviation and tie-line exchanged power of the hybrid power system is reduced by 96% and 83%, respectively, by using the proposed strategy. The experimental results demonstrate that the strategy proposed in this paper exhibits a substantial enhancement in control performance.

Linear Active Disturbance Rejection Control System for the Travel Speed of an Electric Reel Sprinkling Irrigation Machine

The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery.

Active Composite Control of Disturbance Compensation for Vibration Isolation System with Uncertainty

The pointing and positioning accuracy of precision instruments in aerospace are often disturbed by low-frequency vibrations. An active/passive vibration isolation system is a feasible solution to suppress low-frequency vibrations. However, the vibration isolation performance of the active control strategy is seriously affected by the uncertainty of the system and the difficulty to meet the higher requirements of new-generation equipment. This paper proposes an active composite control (ACC) strategy for vibration isolation systems with uncertainty. The proposed ACC integrates feedforward control based on known systems and feedback control based on the Kalman filter for systems with uncertainty. Further, the derivation and stability analyses of the proposed ACC algorithm are provided, and the influence of system uncertainty on vibration isolation performance based on the proposed ACC is analyzed. Experimental verification is conducted and the experimental results confirm that the proposed ACC can effectively realize the low-frequency and wide-band vibration isolation for the system with uncertainty. Starting from 30 Hz, the vibration isolation performance of the proposed ACC with uncertainty is significantly improved than that of the ACC completely based on a deterministic system model.

Wheel loader seat damping control research based on ADRC

This paper proposes a new type of damped multi-mode switching damper based on a high-speed switching solenoid valve. The structural design of the damper is completed, and its working principle of realizing different damping modes is analyzed. A damping characteristic model of the damper is constructed, and the change rule of the damping characteristic of the multi-mode switching damper is analyzed. The seat suspension of a wheel loader is equipped with a damper that is controlled by a damping strategy. A 10-degree-of-freedom mixed-logic dynamic model of the seat suspension system is established using HYSDEL, and a multi-mode switching damper is employed. The weight coefficients are adjusted using a self-immunity control strategy, and the hybrid model predictive control method is used to complete the damping control strategy. Simulation results show that, compared with the fuzzy control strategy, the designed wheel loader seat suspension damping active disturbance rejection control (ADRC) strategy not only effectively reduces the root-mean-square (RMS) value of the driver’s vertical vibration acceleration up to more than 40% at different speeds and shock conditions, but also effectively reduces the driver’s vertical vibration acceleration. The ADRC strategy is a robust approach that achieves the desired control objective of the system.

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.

Learning active flow control strategies of a swept wing by intelligent wind tunnel

An intelligent wind tunnel using an active learning approach automates flow control experiments to discover the aerodynamic impact of sweeping jets on a swept wing. A Gaussian process regression model is established to study the jet actuator’s performance at various attack and flap deflection angles. By selectively focusing on the most informative experiments, the proposed framework was able to predict 3,721 wing conditions from just 55 experiments, significantly reducing the number of experiments required and leading to faster and cost-effective predictions. The results show that the angle of attack and flap deflection angle are coupled to affect the effectiveness of the sweeping jet. Meanwhile, increasing the jet momentum coefficient can contribute to lift enhancement; a momentum coefficient of 3% can increase the lift coefficient by at most 0.28. Additionally, the improvement effects are more pronounced when actuators are placed closer to the wing root.

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.