• International Journal of Control, Automation, and Systems
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• 제4권3호
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• pp.302-307
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• 2006

The aim of applying Functional Electrical Stimulation (FES) is to restore a person's motor function by directly supplying the controlled electrical currents to the site of the paralyzed muscles. However, most clinically utilized FES systems have adapted an open-loop control scheme. Recently the closed-loop control scheme has been considered for setting up the FES system, but due to the inherent nonlinearities in the musculoskeletal system, the nonlinearities were not fully compensated and it caused the oscillatory responses for tracking the output variables. In this study, a nonlinear controller model that has two inverse compensation units is proposed with the compromising feedback linearization method and this will eventually be used to design the FES control system for stimulating a knee joint musculoskeletal system.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권9호
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• pp.417-422
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• 2001

The brushless DC motor(BLDCM) is widely used in many applications. One of the application of the BLDCM is the stage which is one of process in the semiconductor manufacturing processes. Very high performance is required in the stage process. In this paper, the 1 degree of freedom positioning system for the basic technology of the stage is studied. The linearization method is proposed to make the controller design procedures easy by measuring the thrust force ripple using the strain gauge. And through the experiments, it is proved that the inner velocity control loop is necessary to make more precise positioning control system.

• 제어로봇시스템학회논문지
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• 제8권9호
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• pp.747-756
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• 2002

The automatic steering system for unmanned vehicle was developed. The magnet and MR (Magnetoresistive) sensors are used for the tue detecting system. The lateral distance between sensor and the center line of the road is determined by the linearization of the distance according to the output. The PD control theory is used for the design of the controller to compare with $H_\infty$ control theory. The $H_\infty$ control theory is used for the design of the controller to reduce the disturbance. The performance of the PD controller and $H_\infty$ controller is compared in simulations and tests. The PD controller is easy to tune in the test site. The $H_\infty$ controller is robust far the disturbances in the test results.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2006년도 춘계학술발표논문집
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• pp.307-310
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• 2006

We consider the problem of constructing observers for nonlinear systems with unknown inputs. It is shown that under some conditions, there exists an observer estimating the states of nonlinear systems with unknown inputs. Nonlinear observer design method using observer error linearization and the design technique of unknown input observer(UIO) for linear systems are used to derive conditions. Some illustrative examples are included. In this paper, a direct controller for nonlinear plants using a neural network is presented. The controller is composed of an approximate controller and a neural network auxiliary controller.The approximate controller gives the rough control and the neural network controller gives the complementary signal to further reduce the output tracking error. This method does not put too much restriction on the type of nonlinear plant to be controlled. In this method, a RBF neural network is trained and the system has a stable performance for the inputs it has been trained for. Simulation results show that it is very effective and can realize a satisfactory control of the nonlinear system

• 드라이브 ㆍ 컨트롤
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• 제18권3호
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• pp.1-7
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• 2021

In a hydraulic control system, since a hydraulic cylinder drives a relatively large mass of an object, an external load force acts as a disturbance on the control performance of the system. Additionally, as the hydraulic system is used for a long period, there are disturbances that occur gradually, such as a drop in supply pressure because of abrasion of the pump, oil leakage from a valve, and oil leakage from a cylinder. In this study, a state feedback controller based on a linearization technique is applied. To prevent the performance degradation of the controller from the load disturbance, an Extended Luenberger observer (ELO) is used for the Extended system. The case of using the proportional controller, which is a representative linear controller, and the result of using the controller designed in this study are compared and reviewed through simulation. Also, we propose an experimental gain-setting method for a state feedback controller that can be used at industrial sites, and examine how the stability and control performance of the system changes because of the disturbance inputs through the experimental results.

• Journal of Computational Design and Engineering
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• 제3권4호
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• pp.312-321
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• 2016

Dynamic behaviour of a slider-crank mechanism associated with a smart flexible connecting rod is investigated. Effect of various mechanisms' parameters including crank length, flexibility of the connecting rod and the slider's mass on the dynamic behaviour is studied. Two control schemes are proposed for elastodynamic vibration suppression of the flexible connecting rod and also obtaining a constant angular velocity for the crank. The first scheme is based on feedback linearization approach and the second one is based on a sliding mode controller. The input signals are applied by an electric motor located at the crank ground joint, and two layers of piezoelectric film bonded to the top and bottom surfaces of the connecting rod. Both of the controllers successfully suppress the vibrations of the elastic linkage.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 학술대회 논문집 정보 및 제어부문
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• pp.462-463
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• 2008

This paper proposes a method for dynamic output-feedback controller design for stochastic time-delay systems. Based on recent results on time-delay systems control, a tractable and delay-dependent design condition is proposed, which provides a dynamic output-feedback controller to render the closed-loop stochastic time-delay systems to be asymptotically stable in the mean-square sense. The feasibility problem of the proposed condition is recast into a cone complementarity problem. An algorithm adopting cone complementarity linearization is presented to solve the resulting problem.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.44-47
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• 1996

Bang-bang control law provides the optimal solution for a minimum-time control problem, but ignores the intermediate path except for the initial and final points. In this paper, a near minimum-time suboptimal fuzzy logic controller is introduced that can control the intermediate path. A dynamic model for a system is established using the average dynamics method of linearization. System model is continuously updated over the control time periods. This makes it suitable for high speed or variable payload applications. Bang-bang control theory is modified and used to derive the preliminary control law. A fuzzy logic algorithm is then applied to adjust and find the best solution. The solution will provide the suboptimal minimum-time control law which can avoid obstacles in the workspace.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1146-1151
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• 2005

This paper deals with the control problem of a natural circulation water tube boiler with constraint conditions. Some linearized models for the water tube boiler are proposed around some operating points, and the model based predictive control law is adopted to control the plant accounting for constraints. In this controller, the Kalman filter is used for the state estimation, and the controller is designed based on the linearized model. The control performance of the designed controller is exemplified via some nonlinear simulations around the operation point, which show it works well.

• 제어로봇시스템학회논문지
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• 제6권3호
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• pp.263-272
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• 2000

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.