Identification of Pneumatic Cylinder Friction Parameters Using Genetic Algorithms

Wang, J.; Wang, J.D.; Daw, Nouri Ali

doi:10.1109/tmech.2004.823883

Cited by 49 publications

(19 citation statements)

References 9 publications

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“…Compared with more recently developed particle swarm optimization (PSO), GA has a better chance of finding a more qualified solution, since the mutation operation can make the population cluster around several "good" solutions instead of one "good" solution [17]. Moreover it has been demonstrated that GA is robust in the parameter identification problem and can achieve good results [18,19]. GA processes are well explained elsewhere [20], and we present the identification process of GA in Figure 3.…”

Section: Parameter Identificationmentioning

confidence: 99%

Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

Liang

et al. 2018

Energies

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Fuel preparation is the control bottleneck in coal-fired power plants due to the unmeasurable nature or inaccurate measurement of key controlled variables. This paper proposes an inferential multi-model predictive control scheme based on moving horizon estimation for the fuel preparation system in coal-fired power plants, i.e., the pulverizing system, aimed at improving control precision of key operating variables that are unmeasurable or inaccurately measured, and improving system tracking performance across a wide operating range. We develop a first principle model of the pulverizing system considering the nonlinear dynamics of primary air, and then employ the genetic algorithm to identify the unknown model parameters. The outputs of the identified first principle model agree well with measured data from a real pulverizing system. Thereafter we derive a moving horizon estimation approach to estimate the desired, but unmeasurable or inaccurately measured, controlled variables. Estimation constraints are explicitly considered to reduce the influence of measurement uncertainty. Finally, nonlinearity of the pulverizing system is analyzed and a multi-model inferential predictive controller is developed using the extended input-output state space model to achieve offset-free performance. Simulation results show that the proposed soft sensor can provide improved estimates than conventional extended Kalman filter, and the proposed inferential control scheme can significantly improve performance of the pulverizing system.

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Section: Parameter Identificationmentioning

confidence: 99%

Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

Liang

et al. 2018

Energies

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“…In Wang et al (2004), a genetic algorithm technique is used to identify the friction effects in a servopneumatic precision system. The presented method provides a friction map with Coulomb, static and viscous effects.…”

Section: -Introductionmentioning

confidence: 99%

Identification of friction effects in a linear positioning servopneumatic system

Trentini¹,

Campos²,

Silveira³

et al. 2017

Cieng

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It is presented, in this paper, a methodology to identify friction effects in servopneumatic systems using the Karnopp model. In spite of servopneumatic advantages in relation to electric and hydraulic systems, as cleanliness and low cost, its main disadvantages are related to nonlinearities caused by friction effects. Therefore, friction identification plays an important role in servopneumatic research, since the controller design depends on an accurate friction model. In this work, the experimental data are acquired via testbed experiments. The testbed comprises a pneumatic actuator, a potentiometric displacement sensor, two pressure sensors, a manual valve and an acquisition system. The experimental data are used to depict friction in the system using the Karnopp model. Unknown mathematical system coefficients are identified using Least Squares method, where an accuracy percentage of 99.04% between the mathematical and experimental models is reached. Keywords: Friction identification, Servopneumatic systems, Karnopp model, Least Squares method. RESUMO É apresentada, neste trabalho, uma metodologia para a identificação dos efeitos de atrito em sistemas servopneumáticos utilizando o modelo de Karnopp. Apesar das vantagens destes sistemas em relação à sistemas elétricos e hidráulicos, como limpeza e baixo custo, suas principais desvantagens são relacionadas às não-linearidades referente ao atrito. Por este motivo, a identificação do atrito é uma importante área de pesquisa na servopneumática, já que o projeto de controladores para estes sistemas depende diretamente de um modelo de atrito fiel ao sistema real. Neste trabalho, uma bancada de testes é desenvolvida a fim de obter os dados experimentais do sistema. A bancada é composta por um atuador pneumático, um sensor de deslocamento potenciométrico, dois sensores de pressão, uma válvula manual e um sistema de aquisição de dados. Os dados experimentais obtidos são utilizados para representar o atrito do sistema através do modelo de Karnopp. Os coeficientes desconhecidos do modelo matemático são identificados através do método dos Mínimos Quadrados, onde é obtido um percentual de assertividade do modelo matemático de 99,04% em relação aos resultados experimentais. Palavras-chave: Identificação do atrito, Sistemas servopneumáticos, Modelo de Karnopp, Método dos Mínimos Quadrados.

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“…The condition of the pneumatic and hydraulic cylinders (Wang et al, 2004), and digitally controlled valves (Karpenko et al, 2003) were the main focus of the studies. Some of the other considered faults were leakage of the seals (Nakutis & Kaškonas, 2005Yang, 2006;Sepasi & Sassani, 2010), friction increase (Wang et al, 2004;Nogami et al, 1995) and other malfunctions (Bouamama et al, 2005). The monitored signals can be divided into two groups according to their frequencies.…”

Section: Introductionmentioning

confidence: 99%

“…Acoustic emission is an excellent example of high frequency monitoring signal (Yang, 2006;Chena et al, 2007). The frequency of the pressure, flow, and timing signals are low (Sepasi & Sassani, 2010;Nogami et al, 1995;Bouamama et al, 2005;Nakutis & Kaškonas, 2005Wang et al, 2004;Karpenko et al, 2003;Li & Kao, 2005;McGhee et al, 1997). The gathered signals are encoded to obtain their most descriptive features.…”

Section: Introductionmentioning

confidence: 99%

“…The gathered signals are encoded to obtain their most descriptive features. The encoded signals were classified by using various classification techniques such as ANNs (Karpenko et al, 2003;Nakutis & Kaškonas, 2003;Sepasi & Sassani, 2010;Nogami et al, 1995;McGhee et al, 1997), fuzzy method (Mendonca et al, 2009;, neuro fuzzy method (Shi & Sepehri, 2004;, statistical technique (Song et al, 2003), bond graphs (Bouamama et al, 2005), genetic programming (Wang et al, 2004;Yang, 2006), and expert/intelligent systems (Chen & Mo, 2004). It is not difficult to develop programs for classification of the sensory signals of pneumatic systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms

Demetgül¹,

Taşkın²,

Tansel³

2011

Artificial Neural Networks - Industrial and Control Engineering Applications

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Identification of Pneumatic Cylinder Friction Parameters Using Genetic Algorithms

Cited by 49 publications

References 9 publications

Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

Nonlinear Modeling and Inferential Multi-Model Predictive Control of a Pulverizing System in a Coal-Fired Power Plant Based on Moving Horizon Estimation

Identification of friction effects in a linear positioning servopneumatic system

Conditioning Monitoring and Fault Diagnosis for a Servo-Pneumatic System with Artificial Neural Network Algorithms

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