• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.414-427
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• 2006

This paper presents an adaptive feedback linearization control scheme for induction motors with simultaneous variation of rotor and stator resistances. Two typical modeling techniques, rotor flux model and stator flux model, have been developed and successfully applied to the controller design and adaptive observer design, respectively. By using stator fluxes as states, over-parametrization in adaptive control can be prevented and control strategy can be developed without the need of nonlinear transformation. It also decrease the relative degree for the flux modulus by one, thereby, yielding, a simple control algorithm. However, when this method is used for flux observer, it cannot guarantee the convergence of flux. Similarly, the rotor flux model may be appropriate for observers, but it is not so for adaptive controllers. In addition, if these two existing methods are merged into overall adaptive control system, it brings about structural complexies. In this paper, we did not use these two modeling methods, and opted for the airgap flux model which takes on only the positive aspects of the existing rotor flux model and stator flux model and prevents structural complexity from occuring. Through theoretical analysis by using Lyapunov's direct method, simulations, and actual experiments, it is shown that stator and rotor resistances converge to their actual values, flux is well estimated, and torque and flux are controlled independently with the measurements of rotor speed, stator currents, and stator voltages. These results were achieved under the persistent excitation condition, which is shown to hold in the simulation.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.35 no.7
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• pp.119-126
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• 2019

The active living of the elderly leads to improve their lives and enhance social networks. In the view of the active living, the walkability is an essential factor for the elderly's daily life. To support the active living, making age-friendly environment is important. Considering that the elderly mainly carry out activities through walking, making the age-friendly walking environment is a preliminary action. The existing studies applied various methods such as surveys by experts. In spite of the benefits in theirs, there is still a limitation that current walkability measurement methods did not incorporate the actual elderly's walking activity. Thus, the purposes of this study is to measure the elderly's walking quantitatively using a wearable sensor, and to investigate the feasibility of comparing several walking environments based on the data collected from the actual elderly's walking. To do this, experiment was conducted in four types environments with 22 senior subjects. The walkability was measured by walking stability represented quantitatively as Maximum Lyapunov Exponent (MaxLE). Through the experiment results, it was confirmed that the stability of the elderly walking was different according to the walking environment, which also meant that bodily responses (walking stability) is highly related to walkability. The results will provide an opportunity for the continuous diagnosis of walking environments, thereby enhancing the active living of the elderly.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.208-222
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• 2021

Managing with the presence of sea ice is the primary challenge in the operation of floating platforms in the Arctic region. It is widely accepted that offshore structures operating in Arctic conditions need station-keeping methods as well as ice management by icebreakers. Dynamic Positioning (DP) is one of the station-keeping methods that can provide mobility and flexibility in marine operations. The presence of sea ice generates complex external forces and moments acting on the vessel, which need to be counteracted by the DP system. In this paper, an icevaning control algorithm is proposed that enables Arctic offshore vessels to perform DP operations. The proposed icevaning control enables each vessel to be oriented toward the direction of the mean environmental force induced by ice drifting so as to improve the operational safety and reduce the overall thruster power consumption by having minimum external disturbances naturally. A mathematical model of an Arctic offshore vessel is summarized for the development of the new icevaning control algorithm. To determine the icevaning action of the Arctic offshore vessel without any measurements and estimation of ice conditions including ice drift, task and null space are defined in the vessel model, and the control law is formulated in the task space. A backstepping technique is utilized to handle the nonlinearity of the Arctic offshore vessel's dynamic model, and the Lyapunov stability theory is applied to guarantee the stability of the proposed icevaning control algorithm. Experiments are conducted in the ice tank of the Korea Research Institute of Ships and Ocean Engineering to demonstrate the feasibility of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.477-486
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• 2001

In this paper , we study the problem of designing TS(Takagi-Sugeno) fuzzy controllers for the systems that can be approximated or represented by the TS fuzzy model. The main strategy used in this paper is the inverse optimal approach, in which the cost function is determined later than the Lyapunov function and its corresponding control input satisfying the design requirements such as stability, decay rate, and robustness against uncertainty. This approach is useful because it yields controllers satisfying the inherent robustness of optimal controllers as well as the considered design goals. The design procedures established in this paper are all in the from of solving LMIs(Iinear matrix inequalities). Since the LMIs arising in the design procedures can be solved within a given tolerance by the interior point methods. the design method of the paper are efficient in practice. The applicability of the proposed design procedures is demonstrated by design examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.499-505
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• 2011

In this paper, we proposed an adaptive fuzzy sliding mode control for nonlinear systems without parameter projection method. By modifying the controller structure, the parameters of the estimated input gain function are guaranteed not being identically zero and it is shown that the control scheme will not cause any implementation problem even if the estimated value of input gain function is zero at any moment during on-line operations. Except for the input gain function which an approximate estimate for its lower bound is needed, the proposed control scheme does not assume a priori the exact values of the bounding parameters. Based on Lyapunov synthesis methods, the overall control system guarantees that the tracking error asymptotically converges to zero and that all signals involved in controller are uniformly bounded. This can be illustrated by the simulation results for an inverted pendulum system.

• Journal of Auto-vehicle Safety Association
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• v.5 no.2
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• pp.24-31
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• 2013

This paper describes a coordinated control algorithm for rear-side collision avoidance. In order to assist driver actively and increase driver's safety, the proposed coordinated control algorithm is designed to combine lateral control using a steering torque overlay by Motor Driven Power Steering (MDPS) and differential braking by Vehicle Stability Control (VSC). The main objective of a combined control strategy is twofold. The one is to prevent the collision between the subject vehicle and approaching vehicle in the adjacent lanes. The other is to limit actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort. In order to achieve these goals, the Lyapunov theory and LMI optimization methods has been employed. The proposed coordinated control algorithm for rear-side collision avoidance has been evaluated via simulation using CarSim and MATLAB/Simulink.

• Journal of Intelligence and Information Systems
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• v.4 no.1
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.168-180
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• 2018

In order to achieve the high quality output voltage of single-phase voltage source inverters, in this paper an Adaptive Complementary Sliding Mode Control (ACSMC) is proposed. Firstly, the dynamics model of the single-phase inverter with lumped uncertainty including parameter variations and external disturbances is derived. Then, the conventional Sliding Mode Control (SMC) and Complementary Sliding Mode Control (CSMC) are introduced separately. However, when system parameters vary or external disturbance occurs, the controlling performance such as tracking error, response speed et al. always could not satisfy the requirements based on the SMC and CSMC methods. Consequently, an ACSMC is developed. The ACSMC is composed of a CSMC term, a compensating control term and a filter parameters estimator. The compensating control term is applied to compensate for the system uncertainties, the filter parameters estimator is used for on-line LC parameter estimation by the proposed adaptive law. The adaptive law is derived using the Lyapunov theorem to guarantee the closed-loop stability. In order to decrease the control system cost, an inductor current estimator is developed. Finally, the effectiveness of the proposed controller is validated through Matlab/Simulink and experiments on a prototype single-phase inverter test bed with a TMS320LF28335 DSP. The simulation and experimental results show that compared to the conventional SMC and CSMC, the proposed ACSMC control strategy achieves more excellent performance such as fast transient response, small steady-state error, and low total harmonic distortion no matter under load step change, nonlinear load with inductor parameter variation or external disturbance.