Effects of multiplicative noise on the Duffing oscillator with variable coefficients and its integral of motion

Urenda-Cázares, E.; Gallegos, A.; Jaimes-Reátegui, R.

doi:10.1142/s0129183120500953

Cited by 6 publications

(6 citation statements)

References 33 publications

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“…The construction of fuzzy parameter self-tuning control structure is shown in Figure 7. In the debugging process, firstly, make the integral coefficient adjustment and the differential coefficient adjustment invalid, so as to adjust the proportional coefficient [11]. If the output of the fuzzy parameter is volatile, the scale factor is enlarged until the fuzzy parameter changes have regularity.…”

Section: Multi-motor Drive Controlmentioning

confidence: 99%

“…The four-point central difference does not directly use   ek , but takes the error average   ek at four different moments as the benchmark. The expression is given in formula (11).…”

Section:         1 Pd I De T U T K E T E T Dt T T Dtmentioning

confidence: 99%

“…There are also a few large wineries that utilize fully automatic retort filling processes [10]. The automatic upper-retort-robot paving operation mostly adopts the shaking type and the rotary type feeding; the shaking type feeding is based on the design of a general six-axis automatic industrial robot, and the retort paving is based on the shaking of the hopper [11]. The rotating upper retort allows the discharge port to rotate around the inside of the retort barrel, and the swing arm mechanism adjusts the rotation radius of the rotating mechanism [12].…”

Section: Introductionmentioning

confidence: 99%

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Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Xiao¹,

Kang²,

He³

et al. 2021

Preprint

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A research on the multi-motor drive control method of the upper-retort-robot based on machine vision is proposed in this paper for wine brewing automation to suffice the demand of military areas situated in cold regions as wine is recommended to keep the body temperature of soldiers normal in highly cold regions of China. Based on machine vision, the target is converted into an image signal by an image pickup device and is sent to the image processing system. The pixel distribution, brightness, color and other information are converted into digital signals and the target features are extracted to control the actions of the field equipment. The Monte-Carlo method is exploited to randomly generate joint variables within the variation range of each joint. The positive aspects of kinematics model are utilized and the working space of the upper-retort-robot is calculated using multi-motor drive control method. The multi-motor drive compensates the harmonic ripple torque, and establishes the fault-tolerant automatic control of the system to maintain quality of the liquor. The experimental results show that the robot arm can reach at any position in the barrel within the defined range. The robot will work in an automated mode to control the quality of the liquor. The transmission performance of the robot can meet the requirements of the automated quality control of the liquor during processing of wine from grapes. The results are obtained for robot transmission performance and robot dexterity which proves the robustness and viability of the proposed multi-motor drive control method (MMDCM).

show abstract

Section: Multi-motor Drive Controlmentioning

confidence: 99%

Section:         1 Pd I De T U T K E T E T Dt T T Dtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Xiao¹,

Kang²,

He³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…e automatic upper-retort-robot paving operation mostly adopts the shaking type and the rotary type feeding; the shaking type feeding is based on the design of a general six-axis automatic industrial robot, and the retort paving is based on the shaking of the hopper [11]. e rotating upper retort allows the discharge port to rotate around the inside of the retort barrel, and the swing arm mechanism adjusts the rotation radius of the rotating mechanism [12].…”

Section: Introductionmentioning

confidence: 99%

Multimotor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Xiao¹,

Kang²,

He³

et al. 2022

Mathematical Problems in Engineering

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In the cold season, wine aids in maintaining body temperature and is advised for military officers. This paper proposes a study on the multimotor drive control method of the upper-retort-robot based on machine vision for wine brewing automation to meet the demand of military areas located in cold regions, as wine is recommended to keep soldiers’ body temperatures normal in China’s extremely cold regions. Based on machine vision, the target is converted into an image signal by an image pickup device and is sent to the image processing system. Pixel distribution, brightness, color, and other data are transformed to digital signals, and target attributes are retrieved to control the field equipment’s operations. The Monte-Carlo approach is used to generate joint variables at random within each joint’s fluctuation range. The positive aspects of kinematics model are utilized and the working space of the upper-retort-robot is calculated using multimotor drive control method. The multimotor drive compensates the harmonic ripple torque and establishes the fault-tolerant automatic control of the system to maintain quality of the liquor. The results of the experiments reveal that the robot arm can reach any place within the barrel’s set range. To control the quality of the liquor, the robot will function in an automatic manner. The robot’s transmission performance is capable of meeting the requirements for automated liquor quality control during the production of wine from grapes. The results show that the suggested multimotor drive control (MMDCM) approach is robust and viable in terms of robot transmission performance and dexterity.

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“…However, in the collected attitude measurement signals, due to the variety of interference sources, the frequency distribution of the noise signal is very complex, as shown in Table 1 [ 6 ], which is bound to be close to the frequency of a useful signal. Therefore, while suppressing the noise, the useful signal will inevitably be suppressed or damaged [ 7 ]. Even because of the complex and changeable strong noise interference, it is difficult to detect the weak SNR signal which overlaps with the noise spectrum, which leads to the invalidity of MWD.…”

Section: Introductionmentioning

confidence: 99%

Research on the Cooperative Detection of Stochastic Resonance and Chaos for Weak SNR Signals in Measurement While Drilling

Yang

Zhang

et al. 2021

Sensors

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In the process of drilling, severe downhole vibration causes attitude measurement sensors to be erroneous; the errors will accumulate gradually during the inclination calculation. As a result, the ultimate well path could deviate away from the planned trajectory. In order to solve this problem, this paper utilized the stochastic resonance (SR) and chaos phase transition (CPT) produced by the second-order Duffing system to identify the frequency and estimate the parameters of the signal during measurement while drilling. Firstly, the idea of a variable-scale is introduced in order to reconstruct the frequency of the attitude measurement signal, and an SR frequency detection model based on a scale transformation Duffing system is established in order to meet the frequency limit condition of the SR. Then, an attitude measurement signal with a known frequency value is input into the Duffing chaos system, and the scale transformation is used again to make the frequency value meet the parameter requirement of chaos detection. Finally, two Duffing oscillators with different initial phases of their driving signal are combined in order to estimate the amplitude and phase parameters of the measurement signal by using their CPT characteristics. The results of the laboratory test and the field-drilling data demonstrated that the proposed algorithm has good immunity to the interference noise in the attitude measurement sensor, improving the solution accuracy of the inclination in a severe noise environment and thus ensuring the dynamic stability of the well trajectory.

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Effects of multiplicative noise on the Duffing oscillator with variable coefficients and its integral of motion

Cited by 6 publications

References 33 publications

Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Multi-motor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Multimotor Drive Control Method of Upper-Retort-Robot Based on Machine Vision

Research on the Cooperative Detection of Stochastic Resonance and Chaos for Weak SNR Signals in Measurement While Drilling

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