Experimental System Identification and Black Box Modeling of Hydraulic Directional Control Valve

Tørdal, Sondre Sanden; Klausen, Andreas; Bak, Morten K.

doi:10.4173/mic.2015.4.3

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…Combine formula (11) and formula (12), formula (13) (14), as shown at the bottom of the next page: where: r q is the flow gain of the valve core; r ce is the coefficient of total flow and pressure; h is the displacement of the directional valve core.…”

Section: A Hydraulic Cylinder Modelingmentioning

confidence: 99%

“…The PLC alters the displacement of the proportional directional valve core to control the flow rate and implement the left movement, right movement and keep still of hydraulic cylinder piston rod. In the hydraulic system, the output displacement of the proportional directional valve is proportional to the input signal [12]- [14].…”

Section: B Mathematical Model Of Solenoid Proportional Directional Vmentioning

confidence: 99%

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Active-Passive Combined Control System in Crane Type for Heave Compensation

et al. 2019

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For the purpose of researching the displacement control system of heave compensation for offshore drilling platform, a set of crane type active and passive combined heave compensation device are designed on the basis of the similarity principle. As it known that platform heaving will be caused by the wind, sea wave and ocean current when conducting the drilling operations on the offshore drilling platform, which then will disturb the drilling operation. Therefore, the compensation device must be adopted to keep the vertical relative motion between the drill string system and the drilling platform to zero during the operation. Meanwhile, to improve the real-time performance of the heave compensation, the control system is optimized by establishing the simulation model of the active-passive combined crane, and LS-SVM(Least Squares Support Vector Machine) is improved by the artificial immune algorithm to predict the motion trend of offshore platforms. Eventually, in order to acquire the best control scheme, the Proportion Integration Differentiation (PID), fuzzy PID, BP neural network PID control method are utilized to carry out the simulation analysis, and the BP neural network PID control is found to be the optimum. Experiments showed that after using the BP neural network PID control algorithm, the displacement compensation rate of hook for active-passive combined crane device is more than 90%, the performance of the heave compensation is better, and the control is in time. INDEX TERMS Heave compensation, least squares support vector machine, BP neural network, PID control. FIGURE 1. Schematic diagram of crane type active-passive combined heave compensation device.

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Section: A Hydraulic Cylinder Modelingmentioning

confidence: 99%

Section: B Mathematical Model Of Solenoid Proportional Directional Vmentioning

confidence: 99%

Active-Passive Combined Control System in Crane Type for Heave Compensation

et al. 2019

View full text Add to dashboard Cite

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“…Diferentes aplicações envolvendo a modelagem do tipo caixa-opaca podem ser encontradas. Por exemplo, a modelagem de quadricóptero (Stanculeanu e Borangiu, 2011), motor de relutância comutado (Wahid et al, 2019), e de uma válvula hidráulica de controle direcional (Tørdal et al, 2015). Todavia,é possível que as variáveis do modelo não apresentem significado físico ou que o modelo seja composto por um número excessivo de parâmetros (Pearson e Pottmann, 2000).…”

Section: Introductionunclassified

Implementação de evolução diferencial modificada para identificação de sistemas a partir da resposta em frequência

Santos

Lobato

Assis

2020

Anais Do Congresso Brasileiro De Automática 2020

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O algoritmo de Evolução Diferencial (ED) configura-se como umas das importantes técnicas evolutivas empregadas para a resolução de problemas de otimização mono e multi-objetivos. Apesar disto, o algoritmo pode estagnar em torno de um ótimo local. Para evitar que isso ocorra, o presente trabalho utiliza modelos caóticos de busca para atualização dos parâmetros da ED associado a um operador para controle da perda de diversidade. Esse novo algoritmo foi denominado de Evolução Diferencial associado a Modelos Caóticos de Busca e Diversidade (ED-MCBD). A metodologia proposta foi empregada na identificação de sistemas em um método caixa-translúcida, isto é, identificação paramétrica a partir de dados de entrada-saída e uma estrutura pré-definida. Os resultados mostram que, em sistemas com um maior número de parâmetros a serem identificados, o algoritmo ED-MCBD produz melhores resultados em comparação com o método clássico. Entretanto, os novos operadores introduzidos podem impedir a convergência do algoritmo em sistemas com poucos parâmetros a serem estimados.

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“…In order to realize the automatic steering of intelligent vehicles, a more accurate driver model needs to be established to conduct following accurate control of the reference trajectory that has been known. Since the 1970s, traditional control methods such as the recognition of system models 15 and the automatic control of model references 16,17 have received in-depth research.…”

Section: Introductionmentioning

confidence: 99%

Model predictive driver model considering the steering characteristics of the skilled drivers

Jiang

Tian

Hua

2019

Advances in Mechanical Engineering

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First, the experienced drivers with good driving skills are used as objects of learning and road steering test data of skilled drivers are collected in this article. To better simulate human drivers, skilled drivers' steering characteristics are analyzed under different steering conditions. Vehicle trajectories of skilled drivers are fitted by general regression neural network, and the ideal path trajectory is obtained. Second, the model predictive control algorithm is used to build the driver model. According to the requirements of quickly and steadily tracking the track of skilled drivers, vehicle kinematics model is established. The objective function and the corresponding constraint conditions of the driver model based on model predictive control were determined. Finally, numerical simulations results demonstrate that the driver model based on model predictive control can accurately track the reference trajectory of skilled drivers under the four typical steering conditions, and the tracking effect is better than the traditional single-point preview driver model and path tracking method based on a b-spline curve.

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Experimental System Identification and Black Box Modeling of Hydraulic Directional Control Valve

Cited by 11 publications

References 12 publications

Active-Passive Combined Control System in Crane Type for Heave Compensation

Active-Passive Combined Control System in Crane Type for Heave Compensation

Implementação de evolução diferencial modificada para identificação de sistemas a partir da resposta em frequência

Model predictive driver model considering the steering characteristics of the skilled drivers

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