• Journal of the Korea Society of Computer and Information
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• v.29 no.11
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• pp.57-66
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• 2024

In this paper, an approach based on deep learning and parameter dependent control is proposed for electronic throttle body(ETB) control which has variable parameters and nonlinear torques. Firstly we present parameter estimation method for ETB system using deep neural network. To estimate parameters of ETB, we design deep neural networks and train by use time response characteristic such as rise time, overshoot and settling time. Parameters of ETB are estimated through trained neural networks by using time response data. Secondly we design parameter dependent PID controller which is adjusted automatically with the estimated system parameter of ETB. To design optimal parameter dependent gain of PID controller, we use ITAE(Integral of time multiplied by absolute error) criteria. In addition, we design feed-forward controller to reject nonlinear torque. Finally we present simulation results of ETB syatem with parameter variation and nonlinear torque to verify controller design method.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.559-559
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• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• Journal of Ocean Engineering and Technology
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• v.3 no.2
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• pp.59-69
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• 1989

This paper deals with the robust controller design of robot manipulator to track a desired trajectory in spite of the presence of unmodelled dynamics in cause of nonlinearity and parameter uncertainty. The approach follwed in this paper is based on model reference adaptive control technique and convergence on hyperstability theory but it does away with the assumption that process is characterized by a linear model remaining time invariant during adaptation process. The performance of controller is demonstrated by computed simulation about position and speed control of six link manipulator in case of disturbance and payload variation.

• Journal of the Korean Society for Precision Engineering
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• v.6 no.4
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• pp.108-118
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• 1989

An adaptive control scheme has been recognized as an effective approach for a robot manipulator to track a desired trajectory in spite of the presence of nonliearity and parameter uncertainty in robot dynamics model. In this paper, an adaptive control scheme for a robot manipulator is proposed to design robust controller using model reference adaptive control technique and hyperstability theory but it does away with] assumption that the process is characterized by a linear model remaining time invariant during the adaptation process. The performance of controller is demonstrated by the simulation about position and speed control of a six-link manipulator with disturbance and payload variation.