• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.379-387
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• 2007

In this paper, a nonlinear anti-sway controller for container cranes with load hoisting is investigated. The considered container crane involves a planar motion in conjunction with a hoisting motion. The control inputs are two (trolley and hoisting forces), whereas the variables to be controlled are three (trolley position, hoisting rope length, and sway angle). A novel feedback linearization control law provides a simultaneous trolley-position regulation, sway suppression, and load hoisting control. The performance of the closed loop system is shown to be satisfactory in the presence of disturbances at the payload and rope length variations. The advantage of the proposed control law lies in the full incorporation of the nonlinear dynamics by partial feedback linearization. The uniform asymptotic stability of the closed-loop system is assured irrespective of variations of the rope length. Simulation and experimental results are compared and discussed.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.253-263
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• 2002

This paper presents an adaptive feedback linearization control scheme for induction motors using stator fluxes. By using stator flukes as states, overparameterization is prevented and control inputs can be determined straightforwardly unlike in existing schemes. This approach leads to the decrease of the relative degree for the flux modulus and thus yields a simpler control algorithm than the prior results. In this paper. adaptation schemes are suggested to compensate for the variations of stator resistance. rotor resistance and load torque. In particular, the adaptation to the variation of stator resistance with a feedback linearization control is a new trial. In addition, to improve the convergence of rotor resistance estimation, the differences between stator currents and its estimates are used for the parameter adaptation. The simulations show that torque and flux are controlled independently and that the estimates of stator resistance, rotor resistance, and load torque converge to their true values. Actual experiments on a 3.7㎾ induction motor verify the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2672-2674
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• 2000

In this paper. feedback linearization and the sliding mode control(SMC) are used together for uncertain nonlinear system. An advantage of feedback linearization technique is to make linear control theories can be used for nonlinear system and the SMC have the robustness. But the dynamics of the SMC has the dynamics lower order than that of the original system. Therefore the linear control theory can not be used with the SMC. The novel sliding surface of the SMC can have the dynamics of the nominal non linear system controlled by the feedback linearization. The proposed method can be used for the control of induction motors.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1253-1256
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• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.127-137
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• 2000

This paper presents a novel neural based controller which controls the water level of the nuclear power plant steam generator. The controller consists of a model reference feedback linearization controller and a PI controller for stabilizing the feedback linearization controller. The feedback linearization controller consists of a neural network model and an inversing module which uses the neural network model for computing the control input to the steam generator. We chose Piecewise Linearly Trained Network(PLTN) and Recurrent Neural Netwrok(RNN) for an approximator of the plant and used these approximators in calculating the input from the feedback linearization controller. Combining the above two controllers gives a result of better performance than the case which uses only a PI controller Each control result of PLTN and RNN is given.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.377-384
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• 2007

This paper studies on the relation between linearization methods and controller gains in the looper ILQ(lnverse Linear Quadratic optimal control) system for hot strip finishing mills. Firstly, two linear models arc respectively derived by a linearization method using Taylor's series expansion and a static state feedback linearization method, respectively, and the linear models are compared with the nonlinear model. Secondly, the looper servo controllers are respectively designed on the basis of two linearization models. Finally, the relation between the performances of two ILQ servo controllers and the linearization methods, and the structures and control gains of two controllers are evaluated by a computer simulation.

• Journal of Mechanical Science and Technology
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• v.17 no.11
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• pp.1608-1615
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• 2003

This paper studies on the linearization of a looper system in hot strip mills, that plays an important role in regulating a strip tension or a strip width. Nonlinear dynamic equations of the looper system are analytically linearized by a static feedback linearization algorithm with a compensator. The proposed linear model of the looper is validated by a comparison with a linear model using Taylor's series. It is shown that the linear model by static feedback well describes nonlinearities of the looper system than one using Taylor's series. Furthermore, it is shown from the design of an ILQ controller that the linear model by static feedback is very useful in designing a linear controller of the looper system.

• Journal of Power Electronics
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• v.19 no.2
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• pp.569-579
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• 2019

A robust controller is designed based on feedback linearization and sliding mode control to damp sub-synchronous control interaction (SSCI) in doubly fed induction generator (DFIG) wind power plants (WPPs) interfaced with the grid. A feedback-linearized sliding mode controller (FLSMC) is developed for the rotor-side converter (RSC) through feedback linearization, design of the sliding mode controller, and parameter tuning with the use of particle swarm optimization. A series-compensated 100-MW DFIG WPP is adopted in simulation to evaluate the effectiveness of the designed FLSMC at different compensation degrees and wind speeds. The performance of the designed controller in damping SSCI is compared with proportional-integral controller and conventional sub-synchronous resonance damping controller. Besides the better damping capability, the proposed FLSMC enhances robustness of the system under parameter variations.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1221-1224
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• 1996

As in most industrial processes, the dynamic characteristics of an electric power system are subject to changes. Amongst those effects which cause the system to be uncertain, faults on transmission lines are considered. For the stabilization of the power system, we present an indirect adaptive control method, which is capable of tracking a sudden change in the effective reactance of a transmission line. As the plant dynamics are nonlinear, an input-output feedback linearization method is combined with an identification algorithm which estimates the effect of a fault.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.216-220
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• 1999

In this paper, the feedback linearization technique is used with the sliding mode control for nonlinear system. The combination of these two control techniques can be achieved by proposing a novel sliding surface which has the nonminal dynamics of the original system controlled by feedback linearization technique. The noble design of the sliding surface is based on the augmented system whose dynamics have a higher order than that of the original system. The reaching phase is removed by using an initial virtual state which makes the initial sliding function equal to zero