High-Precision Hydraulic Pressure Control Based on Linear Pressure-Drop Modulation in Valve Critical Equilibrium State

To improve the control precision of the drive system of hydraulic tracked vehicles, we established a mathematical model of the drive system based on the analysis of structural characteristics of the high-clearance hydraulic tracked vehicles and the dual-pump dual-motor drive system and developed a control strategy based on the quantitative feedback theory. First, the mutual independence of the two motor channels was achieved through channel decoupling. Then, the loop-shaping controller and the pre-filter were designed for the two channels. The result of a simulation experiment indicates that the proposed control method is very effective in suppressing external uncertainties and smoothening the speed-switching process of the hydraulic motor. Finally, an hydraulic tracked vehicle steering experimental test was carried out. The results show that under two different steering modes, the maximum standard deviation of the output speeds of the inner and outer motors of the hydraulic tracked vehicle is only 0.42, which meets the performance requirement on the hydraulic motor speed. The average steering track radii of the geometric centers of the inner and outer tracks are 1.828 and 0.033 m, respectively, and the relative errors are 1.56% and 3.19%, respectively. This demonstrates that the proposed control method achieves satisfactory results in the robust control of the hydraulic tracked vehicle drive system. It provides some references for the future control research of the hydraulic servo drive system of the high-clearance hydraulic tracked vehicles.

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“…When the tracked vehicle is turning at a radius of R ø B/2, the driving force can be calculated using equations (15) and (16)…”

Section: The Shearing Resistance and Bulldozing Resis-mentioning

confidence: 99%

Robust control of hydraulic tracked vehicle drive system based on quantitative feedback theory

Zhu

Zhang

Wang

et al. 2020

International Journal of Distributed Sensor Networks

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“…S one of the typical mechatronics applications in cyberphysical systems, automated vehicle has become an increasingly popular area of interest for both academia and industry [1][2][3][4][5][6]. Prior to transitioning to fully autonomous driving, highly automated driving will play an important role in the development of automated vehicle technologies [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Characterization of Driver Neuromuscular Dynamics for Human–Automation Collaboration Design of Automated Vehicles

Wang

Cao

et al. 2018

IEEE/ASME Trans. Mechatron.

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In order to design an advanced human-automation collaboration system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analyzed. In order to identify the key parameters of the driver-steering-wheel interacting system and investigate the system properties under different situations, experiments with driver-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the experimental results, key parameters of the transfer function model are identified by using the Gauss-Newton algorithm. Based on the estimated model with identified parameters, investigation of system properties is then carried out. The characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These experimental results with identified system properties provide a good foundation for the development of a haptic take-over control system for automated vehicles.

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“…In an electric powertrain, the controller, representing the "cyber" world, the physical plant, the driver, the "human", and the environment, are highly coupled and interacted, determining vehicle's overall performance jointly [7,8]. These complex subsystems with strong uncertainties, hard nonlinearities and multi-disciplinary interactions make the estimation, control and optimization of electrified powertrains very difficult [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Observation of Hybrid States for Cyber-Physical Systems: A Case Study of Electric Vehicle Powertrain

2018

IEEE Trans. Cybern.

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As a typical cyber-physical system (CPS), electrified vehicle becomes a hot research topic due to its high efficiency and low emissions. In order to develop advanced electric powertrains, accurate estimations of the unmeasurable hybrid states, including discrete backlash nonlinearity and continuous half-shaft torque, are of great importance. In this paper, a novel estimation algorithm for simultaneously identifying the backlash position and half-shaft torque of an electric powertrain is proposed using a hybrid system approach. System models, including the electric powertrain and vehicle dynamics models, are established considering the drivetrain backlash and flexibility, and also calibrated and validated using vehicle road testing data. Based on the developed system models, the powertrain behavior is represented using hybrid automata according to the piecewise affine property of the backlash dynamics. A hybrid-state observer, which is comprised of a discrete-state observer and a continuous-state observer, is designed for the simultaneous estimation of the backlash position and half-shaft torque. In order to guarantee the stability and reachability, the convergence property of the proposed observer is investigated. The proposed observer are validated under highly dynamical transitions of vehicle states. The validation results demonstrates the feasibility and effectiveness of the proposed hybrid-state observer.

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High-Precision Hydraulic Pressure Control Based on Linear Pressure-Drop Modulation in Valve Critical Equilibrium State

Cited by 121 publications

References 34 publications

Robust control of hydraulic tracked vehicle drive system based on quantitative feedback theory

Robust control of hydraulic tracked vehicle drive system based on quantitative feedback theory

Characterization of Driver Neuromuscular Dynamics for Human–Automation Collaboration Design of Automated Vehicles

Simultaneous Observation of Hybrid States for Cyber-Physical Systems: A Case Study of Electric Vehicle Powertrain

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