• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.619-624
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• 2011

This paper presents a controller design method for a robust control problem with multiple constraints using genetic algorithms and LMI design method. A robust $H_{\infty}$ constraint with loop shaping and pole placement is used to address disturbance attenuation with error limits and desired transient specifications, in spite of the plant uncertainties and disturbances. In addition, a loop gain constraint is considered so as not to enlarge the loop gain unnecessarily. The robust $H_{\infty}$ constraint and pole placement constraint can be expressed in terms of two matrix inequalities and the loop gain constraint can be considered as an objective function so that genetic algorithms can be applied. Accordingly, a robust controller can be obtained by integrating genetic algorithms with LMI approach. The proposed controller design method is applied to a track-following system of an optical disk drive and is evaluated through simulation results.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.4
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• pp.356-364
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• 2013

This paper presents a tracking gain-up controller design method to control effectively the vibration of tracking actuator caused by external shocks and remaining velocity after seek control. A pole placement constraint is considered to assure a desired transient response against the vibration of tracking actuator. A loop gain-up constraint is introduced to hold the tracking gain-up loop gain and control bandwidth within allowable bounds. The pole placement constraint is expressed by a matrix inequality and the loop gain-up constraint is considered as an objective function so that genetic algorithm can be applied. Finally, a tracking gain-up controller is obtained by integrating a genetic algorithm with LMI design approach. The proposed tracking gain-up controller design method is applied to the track-following system of a DVD recording device and its effectiveness is evaluated through the experimental results.

• International Journal of Automotive Technology
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• v.8 no.4
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• pp.445-453
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• 2007

This paper presents a robust controller design approach for improving vehicle dynamic roll motion performance and guaranteeing the closed-loop system stability in spite of vehicle parameter variations resulting from aging elements, loading patterns, and driving conditions, etc. The designed controller is linear parameter-varying (LPV) in terms of the time-varying parameters; its control objective is to minimise the $H_{\infty}$ performance from the steering input to the roll angle while satisfying the closed-loop pole placement constraint such that the optimal dynamic roll motion performance is achieved and robust stability is guaranteed. The sufficient conditions for designing such a controller are given as a finite number of linear matrix inequalities (LMIs). Numerical simulation using the three-degree-of-freedom (3-DOF) yaw-roll vehicle model is presented. It shows that the designed controller can effectively improve the vehicle dynamic roll angle response during J-turn or fishhook maneuver when the vehicle's forward velocity and the roll stiffness are varied significantly.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.3
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• pp.72-80
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• 1997

A new design method for Takagi-Sugeno (T-S in short) fuzzy controller which guarantees global asymptotic stability and satisfies a desired performance is proposed in this paper. The method uses LMI(Linear Matrix Inequality) approach to find the common symmetric positive definite matrix P and feedback fains K/sub i/, i= 1, 2,..., r, numerically. The LMIs for stability criterion which treats P and K'/sub i/s as matrix variables is derived from Wang et al.'s stability criterion. Wang et al.'s stability criterion is nonlinear MIs since P and K'/sub i/s are coupled together. The desired performance is represented as $ LMIs which place the closed-loop poles of $ local subsystems within the desired region in s-plane. By solving the stability LMIs and pole placement constraint LMIs simultaneously, the feedback gains K'/sub i/s which gurarntee global asymptotic stability and satisfy the desired performance are determined. The design method is verified by designing a T-S fuzzy controller for an inverted pendulum with a cart using the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.321-327
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• 1993

In order to maintain the cutting force at a desired level during peripheral end milling processes in spite of variation of the depth of cut and other machining conditions, a feedrate override. Apaptive Control Constraint (ACC) system are developed. Feedrate override was accomplished by a developed MMC board and PMC interface techniques. Nonlinear model of the cutting process was linearized as an adaptive model with time varying paramrters. Integral type estimators were introduced for on-line identification of cutting and control parameters in peripheral and milling processes. Zero Order Jold (ZOH) type degital control methodology which uses pole-placement concepts was applied for the ACC system. Performance of the developed ACC system was confirmed on the vertical machining center equipped with FANUC OMC for a large amount of experiment

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.663-668
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• 1990

This paper deals with the application of a Generalized Predictive Control (CPC) to a Pressurized Water Reactor (P.W.R) Nuclear Power Plant. Generalized Predictive Control is a sort of Explicit Self-Tuning Control. Current self-tuning algorithms lack robustness to prior choices of either dead-time (input time delay of a plant) or model order. GPC is shown by simulation studies to be superior to accepted self-tuning techniques such as minimum variance and pole-placement from the viewpoint that it is robust to prior choices of dead-time or model order. In this paper a GPC controller is designed to control the P.W.R. nuclear power rlant with varying dead-time and through the designing procedure the designer is free from the constraint of knowing the exact dead-time. The controller is constructed based on the 2nd order linear model approximated in the vicinity of operating point. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, model parameters are updated on-line with a Recursive Least Squares algorithm. Simulation results are successful and show the possibilities of the GPC control application to actual plants with varying or unknown dead-time.