Electro-hydraulic System Research Articles

Research on Compliance Control of Electro-Hydraulic Loading Experimental System

This article discusses the challenges in preventing workpiece damage due to impacts in electro-hydraulic loading systems, especially in unknown environments. We propose an innovative compliance control strategy, synergizing a series elastic actuator with impedance control to significantly mitigate impact forces between the mechanism and test workpieces. The controller consists of two loops: an internal loop and an outer loop. The internal loop integrates a position loop utilizing a radial basis function observer within a backstepping control framework, effectively countering the nonlinear dynamics of hydraulic actuators and ensuring precise trajectory tracking. The outer loop advances traditional impedance control by adaptively modifying the damping coefficient, resulting in a straightforward and easily implementable damping control law. For the unknown environment parameters, our system employs a parameter estimation law to estimate the unknown environmental stiffness and position parameters. The effectiveness of this strategy has been verified through comparative simulation with traditional impedance control, indicating that the proposed method can not only effectively reduce contact shock in unknown environments, improve response speed, and reduce overshoot, but also improve steady-state accuracy. We provided a feasible control scheme for similar systems to ensure precise and safe operation.

State Feedback Control for Vehicle Electro-Hydraulic Braking Systems Based on Adaptive Genetic Algorithm Optimization

In traditional state feedback control, the difficulty in determining the coefficient matrix is a significant factor that prevents achieving optimal control. To address this issue, this paper proposes the integration of adaptive genetic algorithms with state feedback control. The effectiveness of the proposed algorithm is validated via an electro-hydraulic braking system. Firstly, a model of the electro-hydraulic braking system is introduced. Next, a state feedback controller optimized by parameter-adaptive genetic algorithm is designed. Additionally, a penalty term is introduced into the fitness function to suppress overshoots. Finally, simulations are conducted to compare the convergence speed of parameter-adaptive genetic algorithm with genetic algorithm, ant colony optimization, and particle swarm optimization. Furthermore, the performance of the proposed algorithm, the state feedback control, and the proportional-integral control are also compared. The comparison results show that the proposed algorithm effectively accelerates the settling time of the electro-hydraulic braking system and suppresses the overshoots.

Atomic physics-inspired atom search optimization heuristics integrated with chaotic maps for identification of electro-hydraulic actuator systems

Electro-hydraulic actuator system (EHAS) has imposed a challenge in the research community for accurate mathematical modeling and identification due to non-linearities. In this paper, autoregressive exogenous (ARX) structure is used for EHAS modeling and identification is performed by exploiting the competency of atomic physics-based chaotic atom search optimization (CASO) that adapts ten chaotic maps (Chebyshev, Circle, Gauss, Iterative, Logistic, Piecewise, Sine, Singer, Sinusoidal and Tent) in position update of atom search optimization (ASO). The fitness/merit function of the EHAS model is developed in mean-square error (MSE) sense between desired and approximated values. Simulations and analysis show that ASO with a chaotic logistic map (CASO5) performs better than the ASO and its other chaotic variants, as well as other recently introduced metaheuristics for diverse variations in the system model. Statistics based on MSE, learning plots, results of autonomous trials and average fitness analyses verify the consistency and reliability of the CASO5 for the identification of the EHAS model.

Intelligent fault diagnosis for electro-hydrostatic actuator based on multisource information convolutional residual network

The electro-hydrostatic actuator (EHA), known for its advantages such as minimal throttling loss, high efficiency, and a significant volume-to-power ratio, has found extensive application in the fields of aeronautics and astronautics. However, ensuring the safety of aircraft that utilize EHAs requires efficient fault diagnosis due to the demanding operational conditions and prolonged usage. Traditional diagnostic approaches face challenges such as intricate fault modeling, complex multi-channel monitoring data, and a limited number of fault samples within the electro-hydraulic system. To overcome these challenges, we propose an intelligent diagnosis method based on a multi-source information convolutional residual network. Specifically, a multis-cale kernel is implemented to capture features at different scales, enhancing model expressiveness. The efficiency channel attention mechanism dynamically focuses on relevant channel features to improve feature learning ability. The residual network adaptively recalibrates features at each layer to facilitate fault feature learning. Additionally, the activate or not activation function is introduced to selectively activate shallow features, thereby improving the feature representation and generalization capability of the model. Experimental data from the EHA system validates the superiority of the proposed method, demonstrating a significant enhancement in the diagnostic accuracy of EHAs with limited samples.

Reducing Operation Emissions and Improving Work Efficiency Using a Pure Electric Wheel Drive Tractor

In response to the problems of excessive greenhouse-gas and particulate emissions and the low traction efficiency of conventional diesel tractors in the field, a purely electric wheel-side drive tractor was studied, including an electric motor drive system, a battery ballast system, and an electro–hydraulic suspension system. This paper develops a dynamics model of an electric tractor-ploughing unit under complex soil conditions, leading to the proposal of an active control method for drive wheel torque and a joint control method for the traction force of the suspension system and the front- and rear-axle loads of a tractor. Finally, the tractor is prototyped and assembled, and ploughing tests are carried out. The ploughing results show that the active torque-distribution control method proposed in this study reduces the tractor slip by 14.83% and increases the traction efficiency by 10.28% compared with the average torque-distribution mode. Compared with the conventional traction control mode, the joint control method for traction and ballast proposed in this paper results in a 3.7% increase in traction efficiency, a 15.05% decrease in slip, and a 4.9% reduction in total drive motor energy consumption. This study will help to improve the operation quality and traction efficiency of electric tractors in complex soil conditions.

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.

Framework for Offline Data-Driven Aircraft Fault Diagnosis

This paper proposes a framework for aircraft fault diagnosis based on the offline analysis of flight data. It overcomes the limitations of current data-driven approaches by combining steps based on both real data, obtained from aircraft flight data records, and simulated data, generated from aircraft models. The framework explores unsupervised and supervised methods, resulting in a proactive approach to flight safety and speeding the learning of fault cases. The influence of both temporal data representation and sensor selection on fault diagnosis performance is analyzed. The framework is organized into four phases (initial, training, operation, and improvement) that cover the aircraft system lifecycle. We used the hierarchical clustering algorithm in the unsupervised part and an ensemble of three algorithms (k-nearest neighbors, decision trees, and neural networks) in the supervised one. The framework is evaluated using an aircraft electrohydraulic actuating system as the case study, for which we obtained a balanced accuracy of 96% in the operation phase and of 90.4% in the improvement phase. The contribution of the framework is also accessed through a comparison with results obtained using only supervised methods. It confirms that the combination of supervised and unsupervised methods improves the performance of the fault diagnosis system.

A Review of One-Box Electro-Hydraulic Braking System: Architecture, Control, and Application

With the development of automobile electrification and intelligence, new requirements have been put forward for automotive braking technologies. Under this background, the One-box EHB (Electro-Hydraulic Braking system) brake-by-wire technology has emerged, which combines the electric booster and wheel-cylinder control module into one box and can realize vehicle stability and comfort functions such as service brake, pedal feel simulation, brake decoupling, failure backup, active braking, and wheel-cylinder pressure control. This article reviews the current research of key technologies of One-box EHB, including system architecture design and applications under high-level autonomous driving, master cylinder pressure control algorithm design, wheel-cylinder pressure control algorithm design, and electro-hydraulic composite braking control algorithm design. Finally, this article summarizes the current research status of One-box EHB key technologies and puts forward suggestions for future research directions.

Optimizing power consumption and position control in an electro-hydraulic system with cylinder bypass and NN-MPC

This study introduces an innovative approach to enhance the energy efficiency and position control performance of electro-hydraulic systems, employing a comprehensive comparative analysis. It presents and evaluates three control techniques: Proportional-Integral-Derivative (PID) control, Model Predictive Control (MPC), and Neural Network Model Predictive Control (NN-MPC). These methods are systematically assessed across varying load conditions. Notably, our research unequivocally establishes the exceptional performance of the NN-MPC approach, even when confronted with load variations. Furthermore, the study conducts an exhaustive examination of energy consumption by comparing a conventional system, where a flow control valve is not utilized as a hydraulic cylinder bypass, with a proposed system that employs a fully open Flow Control Valve (FCV). The results underscore the remarkable energy savings achieved, reaching up to 9% at high load levels.

Research on Pump-Controlled AGC Micro-Displacement Position Control of Lithium Battery Pole Strip Mill Based on Friction Compensation Control Strategy of Imoroved LuGre Pattern

Accurate mathematical patterning of friction has always been a significant research project in the domains of machinery and control, and has played a crucial role in the analysis, control and compensation of mechanical systems containing friction. For high-property electrohydraulic servo control systems, friction compensation is an urgent problem to be solved. The LuGre friction pattern can represent most frictional behaviors, but the LuGre friction pattern is piecewise-continuous, making it non-differentiable. Therefore, the question of how to combine the LuGre friction pattern, enhancing its tracking capacity and robust performance problem, to friction perturbation in hydraulic backstep devices is an important focus for research. In this study, the conventional LuGre friction pattern was enhanced using the continuous differentiability of a friction of rest pattern that laid the foundation of a smooth tangent function. Laying the foundation of an electrohydraulic servo pump-controlled hydraulic roll-gap thickness automatic control system (pump-controlled AGC) pattern, a self-adapting friction compensation controller laid the foundation of the enhanced LuGre pattern. The gradual tracking capacity was determined academically using conditions of parameter, uncertainty and nonlinear friction, and the position control precision of the pump-controlled AGC system was enhanced. The steady-state error of the self-adapting friction compensation control system, which laid the foundation of the enhanced LuGre pattern, attained ±0.1 μm, and the tracking capacity was better than the LuGre pattern control and the conventional PID control strategy at low input speed.

A novel order-separated generalized feedforward design for motion control in energy-efficient electrohydraulic system with proportional and integral feedback

Existing feedforward, or FF, controllers are based on differentially flat models. For double-acting single-rod cylinders, more compact than the double-rod ones, flat models could be constructed either under an incompressible-flow assumption for low-pressure, slow-response applications or for a matched valve-cylinder combination. Contrasting the existing models tackling only a single nonlinear aspect like valve leakage, oil compressibility or friction, a novel FF controller, generalized for different types of valve-cylinder-pump combinations, has been developed here. It is based on the highest-order assumption of incompressible flow that sustains the piston motion. An FFPI controller has been implemented in a laboratory setup for displacement tracking against sinusoidal demands up to 9 Hz frequency. The FF parameters has been estimated by minimizing the deviation of simulation prediction from an offline experimental time-varying response. The PI gains have been selected through a stability analysis by extending Routh criteria for linearized time-variant error dynamics. Comparison against a PI-only controller with identical gains has established the energy-saving potential of the FFPI controller both working with the same variable-displacement pump. In comparison to existing nonlinear adaptive controllers, more precise and smoother responses have been obtained over wider frequency range at lower control expenses, admittedly with occasional marginal penalty in the phase variation. The FFPI controller has exhibited the shortest transient and the highest resilience against measurement noise.

Analysis of the Control Characteristics of the Electro-Hydraulic Vibration System Based on the Single-Neuron Control Algorithm

This paper proposes an electro-hydraulic vibration control system based on the single-neuron PID algorithm, which improves the operating frequency of the electro-hydraulic fatigue testing machine and the control accuracy of the load force. Through mathematical modeling of the electro-hydraulic vibration system (EVS), a MATLAB/Simulink simulation, and experimental testing, this study systematically analyzes the output waveform of the EVS as well as the closed-loop situation of load force amplitude and offset under the action of the single-neuron PID algorithm. The results show that: the EVS with a 2D vibration valve as the core, which can control the movement of the spool in the two-degrees-of-freedom direction, can realize the output of an approximate sinusoidal load force waveform from 0 to 800 Hz. The system controlled by the single-neuron PID algorithm is less complex to operate than the traditional PID algorithm. It also has a short rise time for the output load force amplitude curve and a maximum control error of only 1.2%. Furthermore, it exhibits a rapid closed-loop response to the load force offset. The range variability of the load force is measured to be 1.43%. A new scheme for the design of EVS is provided in this study, which broadens the application range of electro-hydraulic fatigue testing machines.

Electrohydraulic System Analysis of Variable Recruitment Fluidic Artificial Muscle Bundles With Interaction Effects

Abstract This study investigates the system-level performance of variable recruitment (VR) fluidic artificial muscle (FAM) actuator bundles using a model that incorporates FAM interaction effects. A VR bundle combines multiple FAMs to act as one actuator in which the FAMs are sequentially recruited to increase overall efficiency. In a VR bundle, inactive/low-pressure FAMs are compressed beyond their free strains, exerting resistive forces opposing that of active FAMs. A recent model that captures this behavior is used to simulate sinusoidal contraction of a VR bundle with a hanging mass load. The implications of inter-FAM effects on the force–strain space of a VR bundle are discussed and a method of recruitment state transition required to track a sinusoid is proposed. The dynamics of the electrohydraulic subsystems are presented and used to evaluate its system efficiency and bandwidth limits. Three different electrohydraulic configurations are considered: (1) continuous motor operation with constant pump displacement, (2) intermittent motor operation with constant pump displacement, and (3) continuous motor operation with variable pump displacement. Simulation results show the superior bandwidth capabilities of VR bundles by demonstrating its ability to track sinusoids with amplitudes up to 16% strain at frequencies greater than 0.5 Hz, compared to that of a single equivalent cross section area motor unit (SEMU). In addition to increased bandwidth limit, system efficiencies averaged over a range of amplitudes show up to 170% increase when comparing a VR bundle using variable pump displacement to a SEMU using constant pump displacement.

Force Tracking Control of Lower Extremity Exoskeleton Based on a New Recurrent Neural Network

The lower extremity exoskeleton can enhance the ability of human limbs, which has been used in many fields. It is difficult to develop a precise force tracking control approach for the exoskeleton because of the dynamics model uncertainty, external disturbances, and unknown human–robot interactive force lied in the system. In this paper, a control method based on a novel recurrent neural network, namely zeroing neural network (ZNN), is proposed to obtain the accurate force tracking. In the framework of ZNN, an adaptive RBF neural network (ARBFNN) is employed to deal with the system uncertainty, and a fixed-time convergence disturbance observer is designed to estimate the external disturbance of the exoskeleton electrohydraulic system. The Lyapunov stability method is utilized to prove the convergence of all the closed-loop signals and the force tracking is guaranteed. The proposed control scheme’s (ARBFNN-FDO-ZNN) force tracking performances are presented and contrasted with the exponential reaching law-based sliding mode controller (ERL-SMC). The proposed scheme is superior to ERL-SMC with fast convergence speed and lower tracking error peak. Finally, experimental tests are conducted to verify the efficacy of the proposed controller for solving accurate force tracking control issues.

Recent advances in control strategies and algorithms for pilot-operated electro-hydraulic proportional directional valves

Pilot-operated electro-hydraulic proportional directional valve is a key hydraulic component to control the motion of large power equipment such as construction machinery and agricultural equipment. The control accuracy and response speed of the valve control element are very important to the performance of the hydraulic system. In this paper, control strategies for dead-zone control and valve control systems are reviewed. In order to eliminate the dead-zone of pilot-operated proportional valve control, the current control schemes for dead-zone detection and dead-zone compensation are summarized, and the control schemes are evaluated. The proportional–integral–derivative-related control, nonlinear control, and robust control of the pilot-operated electro-hydraulic proportional directional valve control system are reviewed, the relationship between the control strategies is analyzed, and the different control algorithms are evaluated. The shortcomings of the research program are analyzed, and the corresponding solutions are proposed. Based on the current research, future research progresses for eliminating the control dead-zone and optimizing control strategies are presented.

Artificial Intelligence Enabled Energy-Saving Drive Unit With Speed and Displacement Variable Pumps for Electro-Hydraulic Systems

In this paper, an artificial intelligence enabled energy-saving strategy (AIESS) is proposed to achieve high energy efficiency of hydraulic systems with a speed and displacement variable pump (SDVP). The AIESS, which integrates extreme gradient boosting (XGBoost) and genetic algorithm (GA), can generate the optimum combination of motor speed and pump displacement of SDVP in real time under actual working conditions. The XGBoost-based power model is used to accurately predict the input power of the SDVP considering environmental factors, e.g., oil temperature, then followed by GA for optimization, in which the XGBoost-based power model is the objective function. A test rig with a 16t hydraulic press and a stamping process is applied to validate the effectiveness of the proposed strategy. Compared to four other algorithm models, the XGBoost-based power model has the highest accuracy. The optimum combination can be obtained quickly by GA. The results show that the AIESS can reduce energy consumption by 28.6% during pressure-maintaining operation and by 11.2% during one stamping cycle compared to the previously proposed segmented control energy-saving strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Energy-saving hydraulic system is a topic in the automation industry. The low energy efficiency of electro-hydraulic systems is mainly caused by poor flow rate matching. A speed and displacement variable pump (SDVP) is used to address this problem, but it is difficult to find the matched speed and displacement for the highest energy conversion efficiency of the SDVP due to the inaccuracy energy model and the challenge of coupling variables. This paper presents an energy-saving strategy integrated with machine learning and a genetic algorithm to handle this challenge. Relative variables of the machine learning model are picked through analysis of the mathematical energy model, and the usage of machine learning can fit the high-nonlinear energy model caused by uncertainties in both components and environmental factors. The trained model is then used as an objective function for a GA-based optimization process to generate the optimum motor speed and pump displacement combination. Considering the processing time of GA, this combination can be optimized before the operation for known working conditions. Finally, we have established a 16t hydraulic press to validate this energy-saving strategy and pre-optimized five combinations for a stamping process. The proposed strategy can reduce energy consumption by 11.2% compared with the previous strategy. This energy-saving strategy can also be applied to other automated hydraulic equipment, such as injection machines.

Characterization Analysis of a New Energy Regenerative Electro-Hydraulic Hybrid Rotary System

Characterization Analysis of a New Energy Regenerative Electro-Hydraulic Hybrid Rotary System

Opportunities and challenges of electrohydraulic control systems in the electrification era of non-road mobile machinery

Opportunities and challenges of electrohydraulic control systems in the electrification era of non-road mobile machinery

Advancements in the development of Electro-hydraulic composite braking systems for electric vehicles

Advancements in the development of Electro-hydraulic composite braking systems for electric vehicles

Fast finite-time feedback linearization sliding mode position tracking control of electro-hydraulic systems with terminal sliding mode disturbance observer

Fast finite-time feedback linearization sliding mode position tracking control of electro-hydraulic systems with terminal sliding mode disturbance observer