• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.3
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• pp.236-242
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• 2009

When two-link flexible arm is rotated about an joint axis, transverse vibration may occur. In this paper, vibration dynamics of flexible robot arm is modeled by using Bernoulli-Euler beam theory and Lagrange equation. Using the fact that matrix $\dot{D}$-2C is skew symmetric, new controllers which have a simplified structure with less computational burden is proposed. Lyapunov stability theory is applied to achieve a stable deterministic nonlinear controller for the regulation of joint angle.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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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.

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.5
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• pp.558-568
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• 2021

The existing underwater vehicle controller design is applied by linearizing the nonlinear dynamics model to a specific motion section. Since the linear controller has unstable control performance in a transient state, various studies have been conducted to overcome this problem. Recently, there have been studies to improve the control performance in the transient state by using reinforcement learning. Reinforcement learning can be largely divided into value-based reinforcement learning and policy-based reinforcement learning. In this paper, we propose the roll controller of underwater vehicle based on Deep Deterministic Policy Gradient(DDPG) that learns the control policy and can show stable control performance in various situations and environments. The performance of the proposed DDPG based roll controller was verified through simulation and compared with the existing PID and DQN with Normalized Advantage Functions based roll controllers.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.346-352
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• 1998

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 stochastic 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 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 canoni al LQ-Neural controller.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.10
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• pp.881-887
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• 2000

There are significant nonlinearities and uncertainties in hard disk drive actuators. In particular, pivot bearing nonlinearity and repeatable run-out make track-following control difficult as track density increases. In this paper, we design a robust track-following controller using a robust deterministic control scheme in which the pivot bearing nonlinearity and repeatable run-out are considered as uncertainties. Simulation study is conducted to evaluate the control performance of the proposed control scheme.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.183-187
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• 2003

When a flexible arm is rotated by a motor about an axis through the arm's fixed end, transverse vibration may occur. The motor torque should be controlled in such a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible arm so that it is arrested at the end of rotation. In this paper, we propose nonlinear observer for one-link flexible am. Then based on the error dynamic equation between the plant dynamic equation and the nonlinear observer dynamic equation of the flexible one-link am, Lyapunov candidate function is applied to achieve a stable deterministic nonlinear feedback controller for the regulation of joint angle.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1139-1144
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• 2012

The ration of payload to weight of industrial robot amounts form 1:10 to 1:30. Compared with man who have a ration of 3:1, it is very low. One of the goals for the next generation of robots will be a ration. This might be possible only by developing lightweight robots. When two-link flexible arm is rotated about an joint axis, transverse vibration may occur. In this paper, vibration dynamics of flexible arm is modeled by using Bernoulli-Euler beam theory and Lagrange equation. Using the fact that matrix $\dot{D}-2C$ is skew symmetric, new controllers which have a simplified structure with less computational burden is proposed by using Lyapunov stability theory. We propose deterministic and adaptive control laws for two link flexible arm, and the validity of the proposed control scheme is shown in computer simulation for two-link flexible arm.

• Smart Structures and Systems
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• v.3 no.2
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• pp.189-200
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• 2007

As a functional material, shape memory alloy (SMA) has attracted much attention and research effort to explore its unique properties and its applications in the past few decades. Some of its properties, in particular the electrical resistance (ER) based self-sensing property of SMA, have not been fully studied. Electrical resistance of an SMA wire varies during its phase transformation. This variation is an inherent property of the SMA wire, although it is highly nonlinear with hysteresis. The relationship between the displacement and the electrical resistance of an SMA wire is deterministic and repeatable to some degree, therefore enabling the self-sensing ability of the SMA. The potential of this self-sensing ability has not received sufficient exploration so far, and even the previous studies in literature lack generality. This paper concerns the utilization of the self-sensing property of a spring-biased Nickel-Titanium (Nitinol) SMA actuator for two applications: ER feedback position control of an SMA actuator without a position sensor, and estimation of the opening of a SMA actuated valve. The use of the self-sensing property eliminates the need for a position sensor, therefore reducing the cost and size of an SMA actuator assembly. Two experimental apparatuses are fabricated to facilitate the two proposed applications, respectively. Based on open-loop testing results, the curve fitting technique is used to represent the nonlinear relationships between the displacement and the electrical resistance of the two SMA wire actuators. Using the mathematical models of the two SMA actuators, respectively, a proportional plus derivative controller is designed for control of the SMA wire actuator using only electrical resistance feedback. Consequently, the opening of the SMA actuated valve can be estimated without using an extra sensor.