Modelling study, analysis and robust servocontrol of pneumatic cylinder actuator systems

Wang, J.; Wang, D. J. D.; Moore, Philip; Pu, Junsheng

doi:10.1049/ip-cta:20010238

Cited by 70 publications

(32 citation statements)

References 8 publications

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“…(7) to (9)) is satisfied. It should be mentioned that in the following equations, mass flow rates are calculated in one PWM cycle and the valves' on/off delays are not taken into account.…”

Section: Modified Pwm Schemementioning

confidence: 89%

“…Given the induced duty cycle of the valve, the final pressure can be derived using Eqs. (7) to (9). Note that the calculated pressure should also satisfy the flow regime condition of that equation.…”

Section: Modified Pwm Schemementioning

confidence: 99%

“…To this end, many studies were conducted on this trend using different nonlinear control approaches to overcome aforementioned difficulties. Advanced nonlinear control approaches, such as adaptive [4,5], sliding mode [6][7][8], robust [9], and adaptive fuzzy-PD [10] control, have been used to address the nonlinear characteristics of pneumatic systems.…”

Section: Introductionmentioning

confidence: 99%

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An experimental comparison of PWM schemes to improve positioning of servo pneumatic systems

Shiee

Sharifi

Fathi

et al. 2015

Int J Adv Manuf Technol

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Distinct advantages of the combination of the pneumatic actuated plants with "on/off" solenoid valves have motivated many researchers to conduct research in this scope. Pulse-width modulation (PWM) is a tool to use such combination in servo tasks. This paper studies the most frequent and well-reported PWM schemes. These PWM schemes have been applied for positioning tasks of the pneumatic actuators using solenoid on/off valves. In this study, the positioning performance of the servo pneumatic system, utilizing different PWM schemes, is investigated through several experiments with step and sinusoidal reference inputs. Rise time, overshoot, and steady-state error, in step input tests, and tracking performance in sinusoidal tests demonstrate the effectiveness of the deployed PWM schemes in the pneumatic system behaviors. Moreover, the closed-loop results demonstrate that the system robustness against the increase of the system's mass is associated with the applied PWM scheme. Based on the openloop results, two factors are found to be affecting PWM schemes performance, namely, the difference in the crosssectional areas on each side of the piston and the piston position, in which these factors have not been considered in the design procedure of the former studies. Taking into account the former factor, a modified version of the PWM schemes is proposed. Steady-state errors in closed-loop tests verify the effectiveness of the developed modified PWM schemes.

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“…(7) to (9)) is satisfied. It should be mentioned that in the following equations, mass flow rates are calculated in one PWM cycle and the valves' on/off delays are not taken into account.…”

Section: Modified Pwm Schemementioning

confidence: 89%

Section: Modified Pwm Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An experimental comparison of PWM schemes to improve positioning of servo pneumatic systems

Shiee

Sharifi

Fathi

et al. 2015

Int J Adv Manuf Technol

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“…However, pneumatic actuators are characterized by high-order time-variant dynamics, nonlinearities due to the compressibility of air, internal and external disturbances, and payload variations [2][3][4]. It is difficult to build an accurate dynamic model for describing pneumatic servodrive behaviour.…”

Section: Robuste Identifikation Von Pneumatischen Servo-aktuatoren Inmentioning

confidence: 99%

Robust identification of pneumatic servo actuators in the real situations

Filipović

Nedić

Stojanović

2011

Forsch Ingenieurwes

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Intensive research in the field of mathematical modelling of the pneumatic cylinder has shown that its mathematical model is nonlinear and that a lot of important details cannot be included in the model. Selection of the model and the identification method have been conditioned by the following facts:(a) The nonlinear model of the system can be approximated by a linear model with time-variant parameters. (b) There is the influence of the combination of heat coefficient, unknown discharge coefficient and change of temperature on the pneumatic cylinder model. Therefore it is assumed that the parameters of the pneumatic cylinder are random (stochastic parameters). (c) In practical conditions, observations have a non-Gaussian distribution.Due to the abovementioned reasons, it is assumed that the pneumatic cylinder model is a linear stochastic model with variable parameters. The Masreliez-Martin filter (robust Kalman filter) was used for identification of parameters of the model. For the purpose of increasing the practical value of the filter, the following two heuristic modifications were performed:(1) It was adopted that T (k) = 1 holds for the scalar transformation of residuals. (2) Fisher information was approximated by a derivative of the Huber's function.The proposed modifications were confirmed through intensive simulations. In order to provide persistent excitation, V. Filipovic · N. Nedic · V. Stojanovic ( ) the autocovariance function "1/f " of the signal was used.The behaviour of the new approach to identification of the pneumatic cylinder is illustrated by simulations. Robuste Identifikation von pneumatischen Servo-Aktuatoren in der realen SituationenZusammenfassung Intensive Forschung auf dem Gebiet der mathematischen Modellierung des pneumatischen Zylinders hat gezeigt, dass sein mathematisches Modell nichtlinear ist und dass viele wichtige Details nicht in das Modell einbezogen werden können. Die Auswahl des Modells und die Art der Identifikation werden durch folgende Tatsachen bedingt: (a) Das nichtlineare Modell des Systems kann durch ein lineares Modell mit zeitvarianten Parametern angenähert werden. (b) Es besteht ein Einfluss der Kombination von Wärme-durchgangs-Koeffizient, unbekanntem Durchflusskoeffizienten und Änderungen der Temperatur auf das pneumatischen Zylinder-Modell. Es wird daher angenommen, dass die Parameter des pneumatischen Zylinders zufälligen Charakters sind. (c) Unter praktischen Bedingungen haben die Beobachtungsergebnisse eine nicht-Gaußsche Verteilung. Aufgrund der vorgenannten Gründe wird davon ausgegangen, dass das Pneumatikzylinder Modell ein lineares, stochastisches Modell mit variablen Parametern sein muss. Der Masreliez-Martin-Filter (robust Kalman-Filter) wurde für die Identifizierung von Parametern des Modells verwendet. Zur Erhöhung des praktischen Werts des Filters, wurden die beiden folgenden heuristischen Modifikationen durchgeführt: 184 Forsch Ingenieurwes (2011) 75:183-196 (1) Es wird angenommen, dass T (k) = 1 für das skalare Transformation der Residuen hält. (2...

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“…The system has been applied in various fields, e.g. in biocybernetics [10], chemistry [9], control [16], power delivery [11], economy [1], and in signal processing [3].…”

Section: Introductionmentioning

confidence: 99%

On Hammerstein System Nonlinearity Identification Algorithms Based on Order Statistics and Compactly Supported Functions

Śliwiński

Wachel

Hasiewicz

2015

Springer Proceedings in Mathematics &Amp; Statistics

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Nonparametric algorithms recovering the nonlinearity in Hammerstein systems are examined. The algorithms are based on ordered measurements and on compactly supported functions. The contribution of the note consists in that the probability density function of the input signal does not need to be strictly bounded from zero but can vanish in a finite number of points. In this setting, the convergence is established for nonlinearities being piecewise-Lipschitz functions. It is also verified that for p times locally differentiable nonlinearities, the algorithms attain the convergence rate O(n −2p/(2p+1) ), the best possible nonparametric one. Noteworthy, the rate is not worsened by irregularities of the input probability density function. IntroductionIn system identification we deal with two kind of knowledge: a priori information, possessed before experiment in a form of the laws governing the behavior of the investigated system or object, and the empirical one, i.e., the measurement data obtained in the experiment. If the former is rich enough, the parametric algorithms are usually employed. Otherwise, one should apply the nonparametric technique (in case when the prior knowledge is not fully validated, one could also consider the semiparametric approach).Considered in this note Hammerstein system identification problem has been a subject of a thorough investigation for many years; see e.g. [6,12] We focus on the system nonlinearity recovery. The nonparametric identification algorithms are of the Gasser-Müller type and use compactly supported kernel or wavelet functions. We examine their convergence conditions for piecewiseLipschitz nonlinearities and convergence rates for the smoother ones. It is verified that the rates are the best possible and, contrary to the Nadaraya-Watson-type algorithms, are not worsened by irregularity of the input signal probability density function. Application of the Gasser-Müller estimate to the Hammerstein system nonlinearity recovery problem was proposed and thoroughly examined in [5]. Identification ProblemThe Hammerstein system is a cascade of a nonlinear static element followed by a linear dynamics; see Fig. 50.1. By m and {k i }, i = 0, . . . , ∞, we denote the nonlinear characteristic and the impulse response of the dynamic part, respectively. Our goal is to recover the nonlinearity from random input-output measurementsof the whole system (as the internal signal W n is, by assumption, not available for measurements). The following assumptions hold:A. The input signal {U n ; n = · · · − 1, 0, 1, 2, . . .} is a stationary white random process with an unknown probability density function f . We assume that −1 ≤ U n ≤ 1 and thatfor all u ∈ [−1, 1], except, maybe, of a finite number of points. B. The nonlinearity m satisfies locally a Lipschitz inequalityfor some unknown c, ε > 0. C. The dynamic subsystem is asymptotically stable, i.e.,. .} is a stationary white random process independent of the input signal and has zero mean and finite variance.The class of locally Lipschitz...

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Modelling study, analysis and robust servocontrol of pneumatic cylinder actuator systems

Cited by 70 publications

References 8 publications

An experimental comparison of PWM schemes to improve positioning of servo pneumatic systems

An experimental comparison of PWM schemes to improve positioning of servo pneumatic systems

Robust identification of pneumatic servo actuators in the real situations

On Hammerstein System Nonlinearity Identification Algorithms Based on Order Statistics and Compactly Supported Functions

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