Robust Hybrid Control Based on PD and Novel CMAC With Improved Architecture and Learning Scheme for Electric Load Simulator

Yang, Bo; Bao, Rong; Han, Hua

doi:10.1109/tie.2014.2301717

Cited by 32 publications

(21 citation statements)

References 26 publications

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“…Furthermore, it is suitable for a system that has uncertain parameters. Considering that the electric load simulator needs to follow the exhaust valve passively, the PID controller was selected as the control algorithm of the electric load simulator [5,25], which can be described as Equation (10).…”

Section: Mathematical Model Of the Systemmentioning

confidence: 99%

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An Electric Load Simulator for Engine Camless Valvetrains

et al. 2019

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Camless valvetrains have become a promising direction to improve the performance of internal combustion engines. In this paper, an electric load simulator is proposed to simulate and implement gas force on the exhaust valve for camless valvetrains under semi-physical conditions. According to test data, the 1D gas-dynamic model was established to get boundary conditions and initial values for 3D finite element simulation. The 3D finite element simulation model was solved to obtain the gas force characteristics of the exhaust valve for camless valvetrains. The electromagnetic actuator was designed according to the system scheme and performance requirements of the electric load simulator. The PID (Proportion Integration Differentiation) algorithm was designed to control the output force of the electric load simulator and reproduce the gas force characteristics of the exhaust valve. It was found that the output force of the electric load simulator could follow the variation of the target gas force and meet the performance requirements of the electric load simulator based on simulation results and experimental results.

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Section: Mathematical Model Of the Systemmentioning

confidence: 99%

“…In the function, K s is the amplification factor of the power driver and T s is the lag time constant. The PID controller module was setup according to the transfer function Equation (10).…”

Section: Model Validationmentioning

confidence: 99%

An Electric Load Simulator for Engine Camless Valvetrains

et al. 2019

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“…As a typical passive loading system, the electric load simulator is disturbed by surplus torque generated on the shaft when the loading instruction is zero and no compensation or control measures are taken. 34 The definition of surplus torque is as follows: The torque output of electric load simulator caused by the motion of actuator, namely the value of surplus torque, equals the output value of electrical load simulator caused by the motion of actuator when the instruction torque is set at zero. Therefore, when modeling the load simulator system, it is necessary not only to consider the performance of the load simulator but also to consider the influence of the actuator.…”

Section: Modeling Of Electrical Load Simulatormentioning

confidence: 99%

A loading control strategy for electric load simulator based on new mapping approach and fuzzy inference in Cerebellar Model Articulation Controller

Yang

Liu

Zhang

2019

Measurement and Control

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To improve surplus torque suppression and loading performance of electric load simulators, this paper presents a loading control strategy based on the new mapping approach and fuzzy inference scheme in the fuzzy Cerebellar Model Articulation Controller. The proposed mapping approach and fuzzy inference scheme in the fuzzy Cerebellar Model Articulation Controller, designed free from the mathematical model of system, comprises a mapping fuzzy Cerebellar Model Articulation Controller and a fuzzy inference controller, in which the former is the main controller. By introducing the new mapping approach in mapping fuzzy Cerebellar Model Articulation Controller, the proposed control strategy is actually a global network with local weight updating and its continuity has been enhanced. The fuzzy inference controller is used as a fuzzy compensator. As a torque controlled system, electric load simulator takes the loading error as the performance index. The results of dynamic simulation and experiments indicate that the proposed loading control strategy can achieve favorable control performance.

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“…In 1972, Albus proposed a learning mechanism that imitates the structure and function of the cerebellum, called cerebellar model articulation controller (CMAC), which is designed based on a cerebellum neurophysiological model [40]. In comparison to the backpropagation neural network, the CMAC NN was adopted widely in modeling and control of robots system for its rapid learning speed, simple structure, insensitivity of data sequence, and easy implementation [39,41]. Figure 3 shows the basic structure of the CMAC neural network.…”

Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Jiang

Yang

et al. 2017

Complexity

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As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

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Robust Hybrid Control Based on PD and Novel CMAC With Improved Architecture and Learning Scheme for Electric Load Simulator

Cited by 32 publications

References 26 publications

An Electric Load Simulator for Engine Camless Valvetrains

An Electric Load Simulator for Engine Camless Valvetrains

A loading control strategy for electric load simulator based on new mapping approach and fuzzy inference in Cerebellar Model Articulation Controller

A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

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