Modeling and Model Predictive Control of Nonlinear Hydraulic System

Chalupa, Petr; Novák, Jakub

doi:10.1007/978-3-642-33227-2_12

Cited by 6 publications

(5 citation statements)

References 6 publications

(6 reference statements)

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“…where k q is the flow gain coefficient of the servovalve, k x is a positive constant, p s is the supply pressure of the pump, p t is the tank pressure, ρ is the oil density, Q 1 is the supply flow rate to the forward chamber, Q 2 is the return flow rate from the return chamber, and u is the input signal of PDV. e pressure dynamics of the piston chamber and rod chamber can be written as [39] _…”

Section: System Modellingmentioning

confidence: 99%

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Energy-Efficient Robust Control for Direct Drive and Energy Recuperation Hydraulic Servo System

Wei-ping

Zhao

2020

Complexity

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During the vertical cyclic actuation process of heavy materials handling, the gravitational potential energy will be converted to heat in the form of throttle in the traditional hydraulic system. Therefore, large energy dissipation is inevitable is this process. In order to achieve energy recuperation, as well as precise trajectory tracking, a direct drive and energy recuperation system is developed. To be specific, the function of direct drive and energy recovery is realized via the three-chamber actuator and a hydraulic accumulator. Moreover, the optimized parameters are obtained by the simulated annealing algorithm. To further reduce the energy consumption, the variable supply pressure control circuit is introduced into the system. Furthermore, the prescribed tracking performance is guaranteed by the proposed robust controller. To compensate for the uncertainties, both in the variable supply pressure control circuit and the robust controller, the RBF neural network is employed to approximate the unknown function. The presented approach theoretically possesses the ability to minimize the energy consumption while maintaining satisfied tracking accuracy. The results demonstrate that the proposed approach can save nearly 90 percent of the energy, and the maximum tracking error is 2 mm.

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Section: System Modellingmentioning

confidence: 99%

“…In the forward motion ( _ x d ≥ 0), the ideal valve orifice equation can be expressed as follows by neglecting the uncertainties such as leakage [39]:…”

Section: Principle Of Vspcmentioning

confidence: 99%

Energy-Efficient Robust Control for Direct Drive and Energy Recuperation Hydraulic Servo System

Wei-ping

Zhao

2020

Complexity

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“…Since, we have already a linear based model for the IEHA in addition to the virtual model; a model predictive control algorithm seems adequate. The model predictive control is a strategy that is based on the explicit use of some kind of linear system model to predict the controlled variables over a certain time period [30]. In our case the linear model of the IEHA can be the predicting model.…”

Section: Predictive Controller Implementationmentioning

confidence: 99%

Modeling and Simulation of a New Integrated Electrohydraulic Actuator for Humanoid Robots

Alfayad

Amrollah²,

Abdellatif

et al. 2016

IJARA

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The work presented in this paper is an important step toward a better understanding of a compact hydraulic robotic actuator, based on the Integrated Electro-Hydraulic Actuator (IEHA) developed by Alfayad and Ouezdou [1]. The novel advantage of this actuator is being highly compact and autonomous (no need for central hydraulic source), while keeping a good power to weight ratio. In order to present and develop the working dynamics of this actuator, a highly detailed mathematical model for the system is presented. The proposed model is simulated using MATLAB-Simulink software to identify the effect of the internal system parameters on system dynamics and prepare an input-output test-bed model. Such test-bed model is used to obtain the transfer function of the system and its order. Analysis of the effects of the main parameters was carried out and a lower order of the system was identified. A linear model of the system is derived and validated using system identification technique. Finally, a robust motion controller is applied on the proposed linear model and the simulation results are presented. The distance between the shaft center + Piston contact point on the surface of the Housing [mm]; m: The end effectors mass [kg]; me: Mass of the carriage of the IEHA [kg]; N: Number of micropistons [−]; Prp: The pressure of the oil at the intake channel of the radial pump [bar]; Pc: The pressure difference between the two Cylinder chambers A and B [bar]; Ps: High pressure line [bar]; PA: Pressure in chamber A of the micro-pistons [bar]; PB: Pressure in chamber B of the micro-pistons [bar]; Pi: The micropistons of the IEHA [-]; Qmac: The average macroscopic flow of the N Micro-pistons [m3/s]; Qmic: The average microscopic flow of the N Micro-pistons [m3/s]; Qe: The flow from microvalve into the carriage Chambers [m3/s]; Qeleak: Leakage flow from the carriage micro-Pistons to the body of the actuator [m3/s]; Q: The flow from the micro-pump [m3/s]; QleakA: The leakage between the carriage and output Cylinder + the one between the hydraulic Chamber to the micro pumps body [m3/s]; Qpileak: The leakage flow of the micro-pistons in the Radial pump [m3/s]; Qleak: The internal leakage between the two Chambers of the cylinder [m3/s]; Rb: The radius of the carriage [mm]; rtig: The micro-valve radius [mm]; rrp: The radius of the in-out opening section of the micro-pump [mm]; Sc: The surface area of the linear hydraulic Cylinder's piston [cm2]; Se: The surface area of the carriage chambers [cm2]; Spi: The active area of a single piston [mm2]; V: Fluid volume [m3]; ve: The volume of the chamber of the carriage [m3]; vpi: The volume of the IEHA micro-pistons [m3]; vc: The volume of the chamber of the output Cylinder [m3]; X: Micro-valve input displacement [mm]; Y: The end effector (output load cylinder) Position [mm]; β: Bulk modulus of elasticity [MPa]; Ɵ: The angle between the piston and the Reference axe [degrees]; ω: Rotational speed of the shaft [rad/sec]; ρ: The density of the oil [kg/m3]; ζ: The actual pressure in the piston chamb...

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“…However, because of several factors such as the oil compressibility, nonlinear friction, time variation, possible load disturbances, and unpredictable parameter perturbation, the hydraulic servo system may not fulfill its purpose of high-precision control. 7,8 To explore how to enable robust control with the hydraulic servo system under the negative influence of model uncertainty, some scholars have developed adaptive fuzzy proportional-integral-derivative (PID) algorithm for online real-time adjustment of PID parameters, 9 the genetic algorithm for adaptive adjustment of PID control parameters, 10 the anti-interference adaptive control scheme based on full-state feedback, 11 the sliding mode controller which is immune from the influence of parameter variation and external disturbances of electro-hydraulic servo system, 12 the model reference adaptive controller (MRAC) based on mathematical model, 13 and the adaptive neural network controller, 14 effectively improving the steady-state control precision and dynamic response speed. There also have been developed some hybrid controllers such as an adaptive integral back-stepping controller, 15 a novel sliding mode controller, 16 and predictive controllers.…”

Section: Introductionmentioning

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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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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Modeling and Model Predictive Control of Nonlinear Hydraulic System

Cited by 6 publications

References 6 publications

Energy-Efficient Robust Control for Direct Drive and Energy Recuperation Hydraulic Servo System

Energy-Efficient Robust Control for Direct Drive and Energy Recuperation Hydraulic Servo System

Modeling and Simulation of a New Integrated Electrohydraulic Actuator for Humanoid Robots

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

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