• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.5
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• pp.582-589
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• 2003

This paper presents the robust stability analysis and design methodology of the fuzzy feedback linearization control systems. Uncertainty and disturbances with known bounds are assumed to be included Un the Takagi-Sugeno (TS) fuzzy models representing the nonlinear plants. $L_2$ robust stability of the closed system is analyzed by casting the systems into the diagonal norm bounded linear differential inclusions (DNLDI) formulation. Based on the linear matrix inequality (LMI) optimization programming, a numerical method for finding the maximum stable ranges of the fuzzy feedback linearization control gains is also proposed. To verify the effectiveness of the proposed scheme, the robust stability analysis and control design examples are given.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.379-380
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• 2011

• Journal of Ocean Engineering and Technology
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• v.28 no.6
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• pp.546-551
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• 2014

This paper addresses a feedback linearization control problem for the nonlinear dynamics of an underwater glider system. We consider the buoyancy and moment as control inputs, which come from the mass variation and elevator control, respectively. Moment-to-force coupling increases the nonlinearities, which make the controller design difficult. By using a feedback linearization technique, we convert the nonlinear underwater glider to an equivalent linear model and design a linear controller. The controller for the equivalent converted linear system is designed using sufficient conditions in terms of linear matrix inequalities. Then, the control input of the nonlinear model of an underwater glider is formulated from the linear control input. An experimental examination is implemented to verify the effectiveness of the proposed technique.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.38 no.12
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• pp.1170-1176
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• 2010

This study addresses adaptive control of UAVs(Unmanned Aerial Vehicles) pitch-axis maneuver. The MRAC(Model Referenced Adaptive Control) approach is employed to accommodate uncertainties which are introduced by feedback linearization of pitch attitude control by elevator input. The model uncertainty is handled by adaptation laws which update model parameters while the UAV is under control by the feedback control law. Steady-state pitch attitude achieved by the stabilizing control law is derived to provide insight on the closed-loop behavior of the controlled system. The proposed idea is free of linearization, gain-scheduling procedures, so that one can design high maneuverability of UAVs for pitching motion in the presence of significant model uncertainty.

• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.523-530
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• 2007

Crane systems are very important in industrial fields to carry heavy objects such that many investigations about control of the systems are actively conducted for enhancing its control performance. This paper presents an adaptive control approach using the model matching for a complex 3-DOF nonlinear crane system. First, the system model is linearized through feedback linearization method and then PD control is applied in the approximated model. This linear model is considered as nominal to derive corrective control law for a perturbed crane model using Lyapunov theory. This corrective control is primitively aimed to compensate real-time control deviation due to partially known perturbation. We additionally study stability analysis of the crane control system using Lyapunov perturbation theory. Evaluation of our control approach is numerically carried out through computer simulation and its superiority is demonstrated comparing with the classical control.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.12
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• pp.1289-1295
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• 2012

An adaptive digital predistortion (PD) technique is proposed for linearization of power amplifiers (PAs) in multiple-input multiple-output (MIMO) transmitters. We consider a PD structure equipped with only one combined feedback path while conventional systems have multiple feedback paths. Hence, the proposed structure is much simpler than that of multiple feedback paths. Based on the structure, a new PD algorithm is derived. The simulation results show that linearization performance of the proposed method is almost the same as the conventional multiple feedback technique while the former is much simpler to implement than the latter.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.192-197
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• 2011

In this paper, the output feedback control of inverted pendulum systems is experimented for the practicality verification of the linear state observer. For the experiment, a pendulum system, CEM-IP-01 of Cemware Inc. is used and Lagrange equation and Jacobian linearization are adopted for the dynamic analysis of the pendulum system. In addition, the output responses of the state feedback control and the output feedback control of the pendulum system are compared before the experiment by Matlab. Finally, we directly verify the practical use of the linear state observer by recognizing and solving some real problem to control the inverted pendulum system in practice.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.474-477
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• 1995

In this paper, the nonlinear model of axial electro-magnetic suspension(EMS) system is presented. The characteristic of attracyion force is analyzed by FEM. Some simulation is given to compare the sliding mode control based on the input-output linearization with the classical linear control using Taylor approximation. Real result of regulating control, transient response comparison, and robustness control with disturbance using the sliding mode method is presented.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.936-938
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• 1999

In this Paper, the design of a feedback linearization controller using multilayer neural network is proposed. The Proposed feedback linearization control scheme is designed by finding Lie derivatives from an identified neural networks. Lie derivatives are expressed as a combination of weights and neuron outputs. The proposed method is applied to an antenna arm problem and the simulation results show performance comparisons between the ordinary feedback linearization and the Proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.1 no.2
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• pp.49-55
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• 2000

In this paper, we show that our previously proposed second-order iterative learning control method with feedback is more effective and has better convergence performance than the second-order iterative learning control method without feedback, particularly in the case of the existence of initial condition errors. Also the convergence proof of the proposed method is given. And through the simulation result of applying the proposed method to the linear time-varying system, we show that our proposed method has enhanced robustness and stability in case of the existence of initial condition errors.