• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.3
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• pp.73-78
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• 2001

This paper presents sliding mode control(SMC) method using target variation rate of Lyapunov Function. SMC keeps the response of structure in sliding surface where structure is stable. It can design both linear controller and bang-bang controller. Linear control of previous research, however, can not make most of the performance of controller, because it is designed to satisfy the condition that the variation rate of Lyapunov function is minus. Also, in case of bang-bang controller, unnecessary large control force is generated. Presented method can utilize the capacity of controller efficiently by prescribing the target variation rate of Lyapunov function. Numerical simulation results indicate that the presented control methods can reduce the peak response larger than linear control, and it has control performance equivalent to bang-bang control.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.3-12
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• 1996

Sliding mode controller(SMC) is a simple but powerful nonlinear controller, because it guarantees the stability and the robustness. However, it leads to the high frequency chattering of the control input. Although the phenomenon can be avoided by introducing a thin boundary layer to the sliding surface, the method results in a steady state: error proportional to the boundary layer thickness. In this paper, we proposed a new sliding mode controller with self-tuning the thickness of a boundary layer. It uses a fuzzy rule base for tuning the thickness of a boundary layer. That is, the thickness is increased to some degree to reject a discontinuous control input at the initial state and then it is decreased as the states approaches to the steady states for improving the tracking performance. In order to assure the control performance, we perf'ormed the computer simulation using an inverted pendulum system as a controlled plant.

• Journal of Power Electronics
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• v.8 no.3
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• pp.280-290
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• 2008

This paper deals with an investigation and evaluation of the performance of a state observer based Permanent Magnet Synchronous Motor (PMSM) drive controlled by PI (Proportional Integral), PID (Proportional Integral and Derivative), SMC (sliding mode control), ANN (Artificial neural network) and FLC (Fuzzy logic) speed controllers. A detailed study of the steady state and dynamic performance of estimated speed and angle is given to demonstrate the capability of the controllers.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2624-2626
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• 2000

The stabilization controls of coupled tank system and ball-beam system are difficult control tasks because of their high order time delay, nonlinearity and structural unstability. Fuhermore, a series of classical methods such as a conventional PID and a full state feedback controller(FSFC) based on the local linearizations have narrow stabilizable regions. Therefore, in this paper, in order to stabilize two representative nonlinear system mentioned above, a Sliding Mode Controller based on a Real Variable Elitist Genetic Algorithm(RVEGA SMC) was proposed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.416-422
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• 2017

The robot control with safety consideration is required since robots and human work together in the same space more frequently in these days. For safety, robots must have compliance to human force and robust tracking performance with high impednace for the nonhuman disturbances. The novel idea is proposed to achieve the compliance and high impedance with one controller structure. For the compliance, the ISMC(Integral Sliding Mode Control) and HDOB(Human Disturbance Observer) The human force is identified by using the human band pass filter and its output is sent to the sliding surface. The sliding mode dynamic is affected by human disturbance and the compliance for human is achieved. The disturbances besides human frequencies are decoupled by the ISMC and the robust tracking is achieved. The additional LDOB(Low Frequency Disturbance Observer) decreases the maxim nonlinear gain and leads low chattering. The introduction of human disturbance into the sliding mode dynamic is the main novel idea of this paper.

• ETRI Journal
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• v.40 no.5
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• pp.653-663
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• 2018

Patients with spinal cord injuries cannot move their limbs using their intact muscles. A suitable controller can be used to move their arms by employing the functional electrical stimulation method. In this article, a fuzzy exponential sliding-mode controller is designed to move a musculoskeletal human arm model to track an optimal trajectory in the sagittal plane. This optimal arm trajectory is obtained by developing a policy for the central nervous system. In order to specify the optimal trajectory between two points, two dynamic and static optimal criteria are applied simultaneously. The first dynamic objective function is defined to minimize the joint torques, and the second static optimization is offered to minimize the muscle forces at each moment. In addition, fuzzy logic is used to tune the sliding-surface parameter to enable an appropriate tracking performance. Simulation results are evaluated and compared with experimental data for upward and downward movements of the human arm.

• Journal of Drive and Control
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• v.21 no.3
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• pp.28-35
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• 2024

This study compared performances of PID (Proportional Integral Derivative), SMC (Sliding Mode Control), and MPC (Model Predictive Control) strategies applied to a 2DOF (Degree Of Freedom) drone tracking robot. The developed 2DOF robot utilized a depth camera with an IMU (Inertial Measurement Unit), laser pointers, and servo motors to rapidly detect and track objects. Image processing was conducted using the YOLO deep learning model. Through this setup, controllers were attached to the robot to track random drone movements, comparing performances in terms of accuracy and energy consumption. This study revealed that while SMC demonstrated precise tracking without deviating from the path, both PID and MPC controllers showed deviations. Performance-wise, SMC is superior. However, considering economic aspects, PID is more advantageous due to its lower power consumption and relatively minor tracking errors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.70-75
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• 2010

The objective of this paper is to propose a new velocity measurement method for an electro-mechanical fin actuator. The model of the electro-mechanical fin actuator includes uncertainties such as unknown disturbances and parameter variations in flight condition. So, an electro-mechanical fin actuator system needs robust control algorithm which requires not only position information but also velocity information. Usually, analog tachometers have been used for velocity feedback in an electro-mechanical fin actuator. However, using these types of sensors have problems such as the cost, space, and malfunction. These problems lead to propose a new velocity measurement method using linear type Hall-effect sensor. In order to verify the proposed method, several experiments are performed using Model Following Sliding Mode Controller(MFSMC). It is shown that the MFSMC with a new velocity measurement method using linear type Hall-effect sensor can satisfy the requirements without using of velocity sensor.

• Journal of Electrical Engineering and Technology
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• v.11 no.5
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• pp.1423-1432
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• 2016

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.15 no.3
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• pp.200-207
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• 2015

This paper proposes an active damping control method for a grid-side converter that has an LCL grid filter in the back-to-back converter. To remove the resonant frequency components produced by the LCL filter, it is necessary to measure the grid current. To do this, sensors must be added. However, it is not necessary to add sensors because the grid current is estimated by designing a suboptimal observer. In order to remove the nonlinearity and to gain fast response of control, both feedback linearization and sliding mode control are applied. The proposed method is verified through a simulation.