• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.211-216
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• 2004

In this thesis, we present the Takagi-Sugeno-Kang (TSK) fuzzy model based adaptive controller and adaptive identification for a general class of uncertain nonlinear dynamic systems. We use an estimated model for the unknown plant model and use this model for designing the controller. The hybrid adaptive control combined direct and indirect adaptive control based on TSK fuzzy model is constructed. The direct adaptive law can be showed by ignoring the identification errors and fails to achieve parameter convergence. Thus, we propose an TSK fuzzy model based hybrid adaptive (HA) law combined of the tracking error and the model ins error to adjust the parameters. Using a Lyapunov synthesis approach, the proposed hybrid adaptive control is proved. The hybrid adaptive law (HA) is better than the direct adaptive (DA) method without identifying the model ins error in terms of faster and improved tracking and parameter convergence. In order to show the applicability of the proposed method, it is applied to the inverted pendulum system and the performance is verified by some simulation results.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.10
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• pp.969-976
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• 2008

A position control problem is studied for UEV(Unmanned Expedition Vehicles), which is to follow pre-determined paths via fixed way-points. Hybrid control systems are used for position control of UEV depending on the operating condition. Speed control consists of three controllers: PID control, adaptive PI control, and neural network. Heading control consists of two controllers, PID and adaptive PID control. The controllers are selected based on the changes of road conditions. We suggest an adaptive PI control algorithm for speed control and an transition management algorithm among the controllers. The algorithm adapts the road conditions and variation of vehicle dynamical characteristics and selects a suitable controller.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.6
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• pp.558-565
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• 2011

Various robot platforms have been designed and developed to perform given tasks in a hazardous environment for surveillance, reconnaissance, search and rescue, etc. We considered a terrain-adaptive and transformable hybrid robot platform that is equipped with rapid navigation capability on flat floors and good performance in overcoming stairs or obstacles. The navigation mode transition is determined and implemented by adaptive driving mode control of the mobile robot. In order to maximize the usability of wheel-track hybrid robot platform, we propose a terrain-adaptive and user-friendly remote controller and verify the efficiency and performance through its navigation performance experiments in real and test-bed environments.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.145-145
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• 2000

In this paper, we propose a hybrid d-step predictor which is composed of an adaptive predictor and a Kalman predictor. We prove the performance limit of the proposed predictor. Simulation is conducted to examine the performance of the proposed predictor. Simulation results show that the proposed combined predictor is superior to the adaptive predictor and the Kalman predictor. Proposed predictor is used for prediction of gun tip vibration of k1 tank. The result is compared with that of conventional adaptive predictor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1400-1406
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• 2016

This paper presents a new adaptive converter control approach for electric motor systems whose voltage source is excited from photovoltaic (PV) power generators. First, an electric model is represented with dynamic states and output velocity of such DC motor systems. We propose a hybrid converter control law in which a state feedback control is applied as an auxiliary control framework. Moreover, control parameter estimation is derived to realize adaptive converter systems for effective control performance against stochastic PV power excitation in practice. We carry out stability analysis for such converter system by using a well-known eigenvalue theory. Lastly, numerical simulation is conducted to test reliability of the proposed converter control approach and prove its superiority in the control point of view.

• Journal of Power Electronics
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• v.19 no.2
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• pp.549-559
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• 2019

This paper proposes an adaptive sliding mode control (ASMC) strategy with an enhanced optimal reaching law (EORL) for the robust current tracking control of the boost converter based hybrid power source (HPS) in an electric vehicle (EV). A conventional ASMC strategy based on state observers and the hysteresis control method is used to realize the current tracking control for the boost converter based HPS. Then a novel enhanced exponential reaching law is proposed to improve the ASMC. Moreover, an enhanced exponential reaching law is optimized by particle swarm optimization. Finally, the adaptive control factor is redesigned based on the EORL. Simulations and experiments are established to validate the ASMC strategy with the EORL. Results show that the ASMC strategy with the EORL has an excellent current tracking control effect for the boost converter based HPS. When compared with the conventional ASMC strategy, the convergence time of the ASMC strategy with the EORL can be effectively improved. In EV applications, the ASMC strategy with the EORL can achieve robust current tracking control of the boost converter based HPS. It can guarantee the active and stable power distribution for boost converter based HPS.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.11a
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• pp.431-436
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• 2000

Adaptive feed forward control algorithms based largely upon LMS approach have developed in recent two decades, and they have been widely applied to practical sound and vibration control problems in the case of the reference signal is available. Feedforward control can be applied only when reference signals can be measured or regenerated, while feedback controllers are used to reduce; sound and vibration when reference signals are not available. In recent years, hybrid control schemes in which adaptive feed forward controllers are combined with feedback ones have been studied based on simulations and experiments. The results have shown that the hybrid control may have better control performances in convergence speed and steady state error than the single control schemes. Hybrid control has the advantages of improving stability and performance as well as the disturbance rejection property. However, little effort has been made to the analysis or interpretation of hybrid control systems. In this study, we discussed the feedback controller effects on the stability of feed forward control algorithm in the presence of uncertain error path and a simple example showed that a stable feedback controller could make the feedforward controller unstable. A design criterion of feedback controllers is proposed in order to guarantee the stability of feedforward algorithms in the presence of error paths with uncertainties.

• Proceedings of the KIEE Conference
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• summer
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• pp.1245-1247
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• 2004

This paper considers the design and implementation of novel technique of speed estimation and control for IPMSM using hybrid intelligent control. The hybrid combination of neural network and adaptive fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of IPMSM using adaptive neural network fuzzy(A-NNF) and estimation of speed using artificial neural network(ANN) controller. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.37-43
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• 2005

This paper presents a hybrid control algorithm for the active noise control in the rectangular enclosure using an active/passive foam actuator. The hybrid control composes of the adaptive feedforward with feedback loop in which the adaptive feedforward control uses the well-known filtered-x LMS(least mean square) algorithm and the feedback loop consists of the sliding mode controller and observer. The hybrid control has its robustness for both transient and persistent external disturbances and increases the convergence speed due to the reduced variance of the jiltered-x signal by adding the feedback loop. The sliding mode control (SMC) is used to incorporate insensitivity to parameter variations and rejection of disturbances and the observer is used to get the state information in the controller deign. An active/passive smart foam actuator is used to minimize noise actively using an embedded PVDF film driven by an electrical input and passively using an absorption-foam. The error path dynamics is experimentally identified in the form of the auto-regressive and moving-average using the frequency domain identification technique. Experimental results demonstrate the effectiveness of the hybrid control and the feasibility of the smart foam actuator.

• Smart Structures and Systems
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• v.31 no.5
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• pp.437-454
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• 2023

Real-time hybrid simulation (RTHS), which has the advantages of a substructure pseudo-dynamic test, is widely used to investigate the rate-dependent mechanical response of structures under earthquake excitation. However, time delay in RTHS can cause inaccurate results and experimental instabilities. Thus, this study proposes a model-based adaptive control strategy using a Kalman filter (KF) to minimize the time delay and improve RTHS stability and accuracy. In this method, the adaptive control strategy consists of three parts-a feedforward controller based on the discrete inverse model of a servohydraulic actuator and physical specimen, a parameter estimator using the KF, and a feedback controller. The KF with the feedforward controller can significantly reduce the variable time delay due to its fast convergence and high sensitivity to the error between the desired displacement and the measured one. The feedback control can remedy the residual time delay and minimize the method's dependence on the inverse model, thereby improving the robustness of the proposed control method. The tracking performance and parametric studies are conducted using the benchmark problem in RTHS. The results reveal that better tracking performance can be obtained, and the KF's initial settings have limited influence on the proposed strategy. Virtual RTHSs are conducted with linear and nonlinear physical substructures, respectively, and the results indicate brilliant tracking performance and superb robustness of the proposed method.