• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.44-49
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• 2001

For minimum phase systems, the conventional fuzzy logic controllers (FLCs) use the error and the change-of-error as fuzzy input variables. Then the control rule table is a skew symmetric type, that is, it has UNLP (Upper Negative and Lower Positive) or UPLN property. This property allowed to design a single-input FLC (SFLC) that has many advantages. But its control parameters are not automatically adjusted to the situation of the controlled plant. That is, the adaptability is still deficient. We here design a single-input direct adaptive FLC (SDAFLC). In the AFLC, some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules are adjusted by an adaptive law. The SDAFLC is designed by a stable error dynamics. We prove that its closed-loop system is globally stable in the sense that all signals involved are bounded and its tracking error converges to zero asymptotically. We perform computer simulations using a nonlinear plant and compare the control performance between the SFLC and the SDAFLC.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1271-1279
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• 2017

A highly accurate model-free controller is proposed for trajectory tracking control of robot manipulators. The proposed controller incorporates time-delay estimation (TDE) to estimate and cancel continuous nonlinearities of robot dynamics, and exploits fuzzy logic systems to suppress the effect of the TDE error, which is due to discontinuous nonlinearities such as friction. To this end, integral sliding mode is defined using desired error dynamics, and a Mamdani-type fuzzy inference system is constructed. As a result, the proposed controller achieves the desired error dynamics well. Implementation of the proposed controller is easy because the design of the controller is intuitive and straightforward, and calculations of the complex robot dynamics are not required. The tracking performance of the proposed controller is verified experimentally using a 3-degree of freedom PUMA-type robot manipulator.

• Bulletin of the Korean Chemical Society
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• v.28 no.10
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• pp.1697-1704
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• 2007

We present results for transport properties of diatomic fluids by isothermal-isobaric (NpT) equilibrium molecular dynamics (EMD) simulations using Green-Kubo and Einstein formulas. As the molecular elongation of diatomic molecules increases from the spherical monatomic molecule, the diffusion coefficient increases, indicating that longish shape molecules diffuse more than spherical molecules, and the rotational diffusion coefficients are almost the same in the statistical error since random rotation decreases. The calculated translational viscosity decreases with the molecular elongation of diatomic molecule within statistical error bar, while the rotational viscosity increases. The total thermal conductivity decreases as the molecular elongation increases. This result of thermal conductivity for diatomic molecules by EMD simulations is again inconsistent with the earlier results of those by non-equilibrium molecular dynamics (NEMD) simulations even though the missing terms related to rotational degree of freedom into the Green-Kubo and Einstein formulas with regard to the calculation of thermal conductivity for molecular fluids are included.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.613-616
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• 2012

This paper proposes the state observer design for nonlinear systems with delayed output. It is shown that by considering a nonlinear term of error dynamics as an additional state variable, the nonlinear error dynamics with time delay can be transformed into the linear one with time delay. Sufficient conditions for existence of a state observer are characterized by linear matrix inequalities. Finally, an illustrative example is given in order to show the effectiveness of our design method.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2000.05a
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• pp.6-10
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• 2000

In this paper a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a low-pass prefilter the inverse dynamic model of a system and the PI controller. The low-pass prefilter prevents high frequency effects from the inverse dynamic model. The model is characterized by a nonlinear friction model. The PI controller regulates the error between the set-point and the system output which is caused by modeling error disturbances and variations f parameters. The parameters of the model and the PI controller are optimized offlinely by genetic algorithm. The experimental results on a DC motor system illustrate the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.14-19
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• 1993

In this paper, we proposed an indirect learning and direct adaptive control schemes using neural networks, i.e., composite adaptive neural control, for a class of continuous nonlinear systems. With the indirect learning method, the neural network learns the nonlinear basis of the system inverse dynamics by a modified backpropagation learning rule. The basis spans the local vector space of inverse dynamics with the direct adaptation method when the indirect learning result is within a prescribed error tolerance, as such this method is closely related to the adaptive control methods. Also hash addressing technique, similar to the CMAC functional architecture, is introduced for partitioning network hidden nodes according to the system states, so global neuro control properties can be organized by the local ones. For uniform stability, the sliding mode control is introduced when the neural network has not sufficiently learned the system dynamics. With proper assumptions on the controlled system, global stability and tracking error convergence proof can be given. The performance of the proposed control scheme is demonstrated with the simulation results of a nonlinear system.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.152-155
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• 1994

The presence of joint elasticity or the arm flexibility causes low damped oscillatory position error along a desired trajectory. We utilize a stochastic model for describing the fast dynamics and the approximation error. A second order shaping filter is synthesized such that its spectrum matches that of the fast dynamics. Augmenting the state vector of slow part with that of shaping filter, we obtain a nonlinear dynamics to which a Gaussian white noise is injected. This modeling approach leads us to the design of an extended Kalman filter(KEF) and a linear quadratic Gaussian(LQG) control scheme. We present the simulation results of this control method. The simulation results show us that our Kalman filtering approach is one of prospective methods in controlling the flexible arms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.289-296
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• 2015

In this paper, we propose the nonlinear control based on singular perturbation theory for position tracking and yaw regulation of planar motor. Singular perturbation theory is characterized by the existence of slow and fast transients in the system dynamics. The proposed method consists of auxiliary control to decouple error dynamics. We develop model reduction with control input. Also, we derIve decoupled error dynamics with auxiliary input. The controller is designed in order to guarantee the desired position and yaw regulation without current feedback or estimation. Simulation results validate the effect of proposed method.

• Proceedings of the KSME Conference
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• 2000.11a
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• pp.623-628
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• 2000

Since the influence coefficients method in balancing of rotors is developed with the basement of not the principle of rotor system dynamics, but the linear relationshop of between the measuring quantities and the unbalance quantities, field engineers can apply the method without additional understanding on the rotor dynamics. But the influence coefficients method is not robust to the measurement error. This paper proposes a new method for the two plane balancing of rigid rotor, based on the principle of rotor dynamics. And the kit for experiment is made by ourselves, and in order to measure in the same condition with it, we do a experiment three times. And then with the Response of gap sensor, the SNR(Signal and Noise) is compared and analyzed about measuring error between the influence coefficient method, and the new method, and it is proved that the new method is less robust than the influence coefficient method.

• Smart Structures and Systems
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• v.6 no.7
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• pp.793-810
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• 2010

This paper addresses the control issue of vibratory MEMS-based gyroscopes. This study considers a gyroscope that can be modeled by an inner mass attached to an outer mass by four springs and four dampers. The outer mass itself is attached to the rotating frame by an equal number of springs and dampers. In order to measure the angular rate of the rotating frame, a driving force is applied to the inner mass and the Coriolis force is sensed along the y-direction associated with the outer mass. Due to micro-fabrication imperfections, including anisoelasticity and damping effects, both gyroscopes do not allow accurate measurements, and therefore, it becomes necessary to devise feedback controllers to reduce the effects of such imperfections. Given an ideal gyroscope that meets certain performance specifications, a feedback control strategy is synthesized to reduce the error dynamics between the actual and ideal gyroscopes. For a dual-mass gyroscope, it is demonstrated that the error dynamics are remarkably decreased with the application of four actuators applied to both masses in the x and y directions. It is also shown that it is possible to reduce the error dynamics with only two actuators applied to the outer mass only. Simulation results are presented to prove the efficiency of the proposed control design.