Friction modeling in servo machines: a review

Niranjan, Prabha; Karinka, Shashikantha; Sairam, K. V. S. S. S. S.; Upadhya, Anusha; Shetty, Srinidi

doi:10.1007/s40435-017-0353-3

Cited by 18 publications

(11 citation statements)

References 48 publications

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“…where F c is kinetic friction; F s is static friction; v s is Stribeck velocity; B is the viscous friction coefficient. d is a modeling parameter that should be determined empirically; here, this value is set to 2 from Niranjan et al, 13 which is the Gauss model. The friction torque is usually used in the identification process, and thus the Stribeck friction model can be modified as in equation (14).…”

Section: Parameter Identification Of State-dependent Friction Modelmentioning

confidence: 99%

“…where F c is kinetic friction; F s is static friction; v s is Stribeck velocity; B is the viscous friction coefficient. δ is a modeling parameter that should be determined empirically; here, this value is set to 2 from Niranjan et al, 13 which is the Gauss model.…”

Section: State-dependent Friction Model Of Ddfmsmentioning

confidence: 99%

“…Stribeck friction model can well represent friction behavior at low velocities, and parameter identification is convenient. 13 The Stribeck friction model contains Coulomb friction force which accounts for a large portion of the total friction of the feed system is not constant due to different loads on the two feed drives. 14,15 LuGre friction model can effectively illustrate the friction characteristics of the pre-sliding regimes and friction memory.…”

Section: Introductionmentioning

confidence: 99%

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A method to improve position accuracy for the dual-drive feed machines by state-dependent friction compensation

Liu

Lü

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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Due to high stiffness and quick response, dual-drive feed machines (DDFMs) are widely used in many precision CNC machines. The tracking errors of the two feed drives directly determine the machines’ accuracy. The performance of the position accuracy can be easily affected by the difference in the friction dynamic characteristics of the two axes. This paper proposes the state-dependent friction compensation (SFC) as an improved DDFMs error compensation method that can achieve better accuracy under unequal friction on the two feed drives. It is theoretically analyzed the influence of friction on the transmission stiffness of DDFMs. Moreover, the effects of time-varying factors (feed rates and mechanical structures) on the friction of dual axes are discussed. The innovation of the paper is that the SFC can adaptively take into account the influence of the variable dual-drive structure and feed rate on DDFMs’ position accuracy, and the compensation method combined with cross-coupled strategy is suitable for general dual-drive structure. Simulation and experiment results show that the proposed method can effectively improve the synchronization accuracy and tracking accuracy in a variety of feed operations (different feed rates, starting, and reversing).

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Section: Parameter Identification Of State-dependent Friction Modelmentioning

confidence: 99%

Section: State-dependent Friction Model Of Ddfmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A method to improve position accuracy for the dual-drive feed machines by state-dependent friction compensation

Liu

Lü

Zhang

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…Under no-load operation, friction is the only effective force. The well-known Tustin model for it is adopted from Niranjan et al 40 as…”

Section: System Modelingmentioning

confidence: 99%

Energy-saving design of variable-displacement bi-directional pump-controlled electrohydraulic system

Hati¹,

Mandal

Sanyal

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Losses in control valves drag down the average overall efficiency of electrohydraulic systems to only about 22% from nearly 75% for standard pump-motor sets. For achieving higher energy efficiency in slower systems, direct pump control replacing fast-response valve control is being put in place through variable-speed motors. Despite the promise of a quicker response, displacement control of pumps has seen slower progress for exhibiting undesired oscillation with respect to the demand in some situations. Hence, a mechatronic simulation-based design is taken up here for a variable-displacement pump–controlled system directly feeding a double-acting single-rod cylinder. The most significant innovation centers on designing an axial-piston pump with an electrohydraulic compensator for bi-directional swashing. An accumulator is conceived to handle the flow difference in the two sides across the load piston. A solenoid-driven sequence valve with P control is proposed for charging the accumulator along with setting its initial gas pressure by a feedforward design. Simple proportional–integral–derivative control of the compensator valve is considered in this exploratory study. Appropriate setting of the gains and critical sizing of the compensator has been obtained through a detailed parametric study aiming low integral absolute error. A notable finding of the simulation is the achievement of the concurrent minimum integral absolute error of 3.8 mm s and the maximum energy saving of 516 kJ with respect to a fixed-displacement pump. This is predicted for the combination of the circumferential port width of 2 mm for the compensator valve and the radial clearance of 40 µm between each compensator cylinder and the paired piston.

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“…The design of a servo system suppressing friction disturbances combines several aspects, mainly: friction modeling, identification of friction in real systems, implementation of friction compensators and compensated controllers, selection of appropriate sensors. The up-to-date review of friction modeling in servo machines [ 2 ] reveals that it is a very complex issue. The correct friction model and its compensation can significantly improve servo tracking accuracy, especially at low motion velocity.…”

Section: Introductionmentioning

confidence: 99%

Discovering Stick-Slip-Resistant Servo Control Algorithm Using Genetic Programming

Bożek

2022

Sensors

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The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analyzed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important.

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Friction modeling in servo machines: a review

Cited by 18 publications

References 48 publications

A method to improve position accuracy for the dual-drive feed machines by state-dependent friction compensation

A method to improve position accuracy for the dual-drive feed machines by state-dependent friction compensation

Energy-saving design of variable-displacement bi-directional pump-controlled electrohydraulic system

Discovering Stick-Slip-Resistant Servo Control Algorithm Using Genetic Programming

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