• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.350-355
• /
• 1990

A pressure tracking of hydroforming process is considered in this paper. To account for nonlinearities and uncertainty of the process. A fuzzy SOC based iterative learning control algorithm is proposed. A series of experimentals were performed for the pressure tracking control of the process. The experimental results show that regardless of inherent nonlinearties and uncertainties associated with hydraulic system. A good pressure tracking control performance is obtained using the proposed fuzzy learning control algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.303-306
• /
• 1996

We propose a robust tuning method of the quadratic criterion based iterative learning control(Q-ILC) algorithm for discrete-time linear batch system. First, we establish the frequency domain representation for batch systems. Next, a robust convergence condition is derived in the frequency domain. Based on this condition, we propose to optimize the weighting matrices such that the upper bound of the robustness measure is minimized. Through numerical simulation, it is shown that the designed learning filter restores robustness under significant model uncertainty.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1997.10a
• /
• pp.82-87
• /
• 1997

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.7
• /
• pp.1411-1417
• /
• 2009

In this paper, we consider the design of $H_{\infty}$ control of linear discrete-time systems having no mathematical model. The basic approach is to use Q-learning which is a reinforcement learning method based on actor-critic structure. The model-free control design is to use not the mathematical model of the system but the informations on states and inputs. As a result, the derived iterative algorithm is expressed as linear matrix inequalities(LMI) of measured data from system states and inputs. It is shown that, for a sufficiently rich enough disturbance, this algorithm converges to the standard $H_{\infty}$ control solution obtained using the exact system model. A simple numerical example is given to show the usefulness of our result on practical application.

• Journal of Ocean Engineering and Technology
• /
• v.11 no.1
• /
• pp.84-95
• /
• 1997

This paper presents a new approach to the design speed and azimuth control of a mobile robot with drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frmework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simple the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.4
• /
• pp.75-86
• /
• 1996

In this paper, we propose a new fuzzy-neural network control scheme for the speed and azimuth control of a mobile robot. The proposed control scheme uses a gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frame-work of the specialized learning architecture. It is proposed a learning controller consisting of two fuzzy-neural networks based on independent reasoning and a connection net woth fixed weights to simply the fuzzy-neural network. The effectiveness of the proposed controller is illustrated by performing the computer simulation for a circular trajectory tracking of a mobile robot driven by two independent wheels.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10a
• /
• pp.299-302
• /
• 1996

A control algorithm is proposed for nonlinear multi-input multi-output(MIMO) batch processes by combining quadratic iterative learning control(Q-ILC) with model predictive control(MPC). Both controls are designed based on output feedback and Kalman filter is incorporated for state estimation. Novelty of the proposed algorithm lies in the facts that, unlike feedback-only control, unknown sustained disturbances which are repeated over batches can be completely rejected and asymptotically perfect tracking is possible for zero random disturbance case even with uncertain process model.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2001.05a
• /
• pp.43-46
• /
• 2001

We, in this paper, design the Sugeno-models fuzzy controller by using the membership function modification algorithm and ANFIS, which are clustering and learning the input-output data. The membership function modification algorithm constructs the more concrete fuzzy controller by clustering the input-output data from the fuzzy inference system. ANFIS construct the Sugeno-models fuzzy controller by learning the input-output data from the above controller. We showed that the fuzzy controller designed by our method could have the stable learning and the enhanced performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.309-314
• /
• 2007

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

• 제어로봇시스템학회:학술대회논문집
• /
• 1989.10a
• /
• pp.172-176
• /
• 1989

In this paper, a simple method for designing iterative learning control scheme is proposed. The proposed learning algorithm is designed based on series expansion of inverse plant model. The proposed scheme has simple structure and fast convergency so that it is suitable for implementing it on conventional micro processor based controllers. The effectiveness of the proposed algorithm is investigated through a series of computer simulations.