Vibration suppression of a pneumatic drive flexible manipulator using adaptive phase adjusting controller

Qiu, Zhi-cheng; Zhao, Zhili

doi:10.1177/1077546313519156

Cited by 10 publications

(7 citation statements)

References 41 publications

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“…where polynomials A(q À 1 , k) and B(q À 1 , k) are defined by (2). In addition, y M (k) represents the noisefree output of the model:…”

Section: Recursive Estimation Of Time-varying Parameters Of Output Ermentioning

confidence: 99%

“…Pneumatic drives are broadly used in robotics and industrial applications because of their distinct characteristics of energy saving, cleanliness, simple structure and operation, and high efficiency and are suitable for working in a harsh environment [1,2]. These drives have a complex structure due to time-varying dynamics, the compressibility of air, nonlinearities (friction), internal and external disturbances, and payload variations [3].…”

Section: Introductionmentioning

confidence: 99%

“…1 The pneumatic servo cylinder PISTOR, Φ75-150 mm, which contains a nozzle flapper electropneumatic pressure valve. 2 The linear displacement transducer BTL-A that gives information on the piston position. The displacement transducer covers the measurement range from 0 to 500 mm.…”

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confidence: 99%

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Identification of time‐varying OE models in presence of non‐Gaussian noise: Application to pneumatic servo drives

Stojanović

Nedić

2016

Intl J Robust & Nonlinear

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Intensive research in the field of mathematical modeling of pneumatic servo drives has shown that their mathematical models are nonlinear in which many important details cannot be included in the model. Owing the influence of the combination of heat coefficient, unknown discharge coefficient, and change of temperature, it was supposed that parameters of the pneumatic cylinder are random (stochastic parameters). On the other side, it has been well known that the nonlinear model can be approximated by a linear model with time-varying parameters. Due to the aforementioned reasons, it can be assumed that the pneumatic cylinder model is a linear stochastic model with variable parameters. In practical conditions, in measurements, there are rare, inconsistent observations with the largest part of population of observations (outliers). Therefore, synthesis of robust algorithms is of primary interest. In this paper, the robust recursive algorithm for output error models with time-varying parameters is proposed. The convergence property of the proposed robust algorithm is analyzed using the methodology of an associated ordinary differential equation system. Because ad hoc selection of model orders leads to overparameterization or parsimony problem, the robust Akaike's criterion is proposed to overcome these problems. By determining the least favorable probability density for a given class of probability distribution represents a base for design of the robust version of Akaike's criterion. The behavior of the proposed robust identification algorithm is considered through intensive simulations that demonstrate the superiority of the robust algorithm in relation to the linear algorithms (derived under an assumption that the stochastic disturbance has a Gaussian distribution). The good practical values of the proposed robust algorithm to identification of the pneumatic cylinder are illustrated by experimental results.Recent research has shown that the nonlinear model of the system can be approximated, with great accuracy, by a time-varying linear system [10]. On the other hand, due to difficult quantification of phenomena described in (ii), one way to present them is the introduction of stochastic processes. Taking into account the previous considerations, it is assumed that the pneumatic cylinder is described by a linear stochastic output error (OE) model with variable parameters. It should be noted that the OE methods are maximum likelihood estimators as well as the equation error methods. The equation error method is used when the output contains both measurement noise and the disturbance (process noise), while the OE method is used when the output contains only the measurement noise [11].Description of stochastic disturbances by Gaussian process is not justified. Namely, in the population of observations, there are rare, large observations that are inconsistent with the majority of the population. Sometimes, complex phenomena are occurred in the system because of atypical changes of the process behavior, output noise, or s...

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“…where polynomials A(q À 1 , k) and B(q À 1 , k) are defined by (2). In addition, y M (k) represents the noisefree output of the model:…”

Section: Recursive Estimation Of Time-varying Parameters Of Output Ermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of time‐varying OE models in presence of non‐Gaussian noise: Application to pneumatic servo drives

Stojanović

Nedić

2016

Intl J Robust & Nonlinear

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show abstract

“…Some recent solutions to damp the vibrations of FLR are: the control based on Port-Hamiltonian modeling [5], the sliding mode control [6], the adaptive control [7], the reinforcement learning control [8] and the fuzzy neural network control [9]. Moreover, multi-loop schemes have been used to control FLR like in [10,11], in which the inner loop carried out the position control and the outer loop the vibration attenuation.…”

Section: Introductionmentioning

confidence: 99%

Fractional Control of a Lightweight Single Link Flexible Robot Robust to Strain Gauge Sensor Disturbances and Payload Changes

2021

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In this paper, a method to control one degree of freedom lightweight flexible manipulators is investigated. These robots have a single low-frequency and high amplitude vibration mode. They hold actuators with high friction, and sensors which are often strain gauges with offset and high-frequency noise. These problems reduce the motion’s performance and the precision of the robot tip positioning. Moreover, since the carried payload changes in the different tasks, that vibration frequency also changes producing underdamped or even unstable time responses of the closed-loop control system. The actuator friction effect is removed by using a robust two degrees of freedom PID control system which feeds back the actuator position. This is called the inner loop. After, an outer loop is closed that removes the link vibrations and is designed based on the combination of the singular perturbation theory and the input-state linearization technique. A new controller is proposed for this outer loop that: (1) removes the strain gauge offset effects, (2) reduces the risk of saturating the actuator due to the high-frequency noise of strain gauges and (3) achieves high robustness to a change in the payload mass. This last feature prompted us to use a fractional-order PD controller. A procedure for tuning this controller is also proposed. Simulated and experimental results are presented that show that its performance overcomes those of PD controllers, which are the controllers usually employed in the input-state linearization of second-order systems.

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“…Adaptive input shaping control has been investigated in vibration control [8]. Adaptive phase adjusting controller is proposed by Qiu and Zhao [9] for a pneumatic driven flexible manipulator vibration control system to deal with the long time delay introduced by the actuator.…”

Section: Introductionmentioning

confidence: 99%

Vibration control of a pneumatic driven piezoelectric flexible manipulator using self-organizing map based multiple models

Zhao

Qiu

Zhang

et al. 2016

Mechanical Systems and Signal Processing

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Vibration suppression of a pneumatic drive flexible manipulator using adaptive phase adjusting controller

Cited by 10 publications

References 41 publications

Identification of time‐varying OE models in presence of non‐Gaussian noise: Application to pneumatic servo drives

Identification of time‐varying OE models in presence of non‐Gaussian noise: Application to pneumatic servo drives

Fractional Control of a Lightweight Single Link Flexible Robot Robust to Strain Gauge Sensor Disturbances and Payload Changes

Vibration control of a pneumatic driven piezoelectric flexible manipulator using self-organizing map based multiple models

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