• Journal of Power Electronics
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• v.6 no.4
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• pp.347-355
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• 2006

A new control method for precision robust position control of a permanent magnet synchronous motor (PMSM) is presented. In direct drive motor systems, a load torque disturbance directly affects the motor shaft. The application of the load torque observer is published in using a fixed gain to solve this problem. However, the motor flux linkage cannot be determined precisely for a load torque observer. Therefore, an asymptotically stable adaptive observer base on a deadbeat observer is considered to overcome the problems of unknown parameters, torque disturbance and a small chattering effect. To find the critical parameters the system stability analysis is carried out using the Liapunov stability theorem.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.9
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• pp.423-429
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• 2001

This paper proposes adaptive impedance control algorithm using fuzzy inference when robot contacts with its environments. The characteristics of the adaptive impedance controller is to adapt with parametric uncertainty and nonlinear conditions. The control algorithm is to join impedance controller with fuzzy inference engine. The proposed control method overcomes the problem of impedance controller using gain-tuning algorithm of fuzzy inference engine. We implemented an experimental set-up consisting of environment-generated one-link robot system and DSP system for controller development. We apply the adaptive fuzzy impedance controller to one-link root system, and it shows the good performance on regulating the interactive force in case of contacting with arbitrary environment.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.963-968
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• 1990

An iterative learning control scheme for tracking control of a class of uncertain nonlinear systems is presented. By introducing a model reference adaptive controller in the learning control structure, it is possible to achieve zero tracking of unknown system even when the upperbound of uncertainty in system dynamics is not known apriori. The adaptive controller pull the state of the system to the state of reference model via control gain adaptation at each iteration, while the learning controller attracts the model state to the desired one by synthesizing a suitable control input along with iteration numbers. In the controller role transition from the adaptive to the learning controller takes place in gradually as learning proceeds. Another feature of this control scheme is that robustness to bounded input disturbances is guaranteed by the linear controller in the feedback loop of the learning control scheme. In addition, since the proposed controller does not require any knowledge of the dynamic parameters of the system, it is flexible under uncertain environments. With these facts, computational easiness makes the learning scheme more feasible. Computer simulation results for the dynamic control of a two-axis robot manipulator shows a good performance of the scheme in relatively high speed operation of trajectory tracking.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.512-515
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• 1997

In this paper the fuzzy-genetic controller for path-tracking of WMRs is proposed. Fuzzy controller is implemented to adaptive adjust the crossover rate and mutation rate, and genetic algorithm is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-genetic controller is effective.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.236-241
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• 1988

This note considers the class of controllers with integral action which arise directly from appropriate system models. Via internal model principle approach, a corresponding class of self-tuning controller is shown to have both integral action in controller and offset removal in the tuning algorithm. The key idea is to constrain the estimator in each step in order to ensure that dc gain of feedforward and feedback polynomial of adaptive controller are always equal, thus allowing the loop integrator to work properly.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.4
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• pp.220-225
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• 1988

A class of adaptive controllers with integral action is proposed, which may riject the offset due to any load disturbance on the plant. Effective integral action and robust identification against the offset can be achieved via the zero-gain predictor. The system is improved, in this paper, to be of more generalized structure, and the detuning control weight which can cope with nonminimum-phase systems is tuned on-line. Discrete-time versions of the improved system are developed, which may be more flexible for the choice of the design parameters. The resulting control systems may also be shown to be robust to the unmodelled dynamics.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.506-509
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• 2005

An adaptive ${\alpha}-{\beta}$ tracker is proposed for tracking maneuvering targets with a track-while-scan radar system. The tracker gain is updated on-line corresponding to the adjusted process noise variance which is obtained via time averaging of the process over a sliding window. The adjusted process noise variance is used to compute the maneuverability index for the tracker gain based on the steady-state Kalman filter equation for each epoch. It is shown via simulation that the proposed approach provides robust and accurate position estimates during the target maneuver while the performance of the conventional ${\alpha}-{\beta}$ tracker is shown much degraded.

• Journal of Mechanical Science and Technology
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• v.16 no.12
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• pp.1613-1626
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• 2002

This paper presents hybrid control of an active suspension system with a full-car model by using H$\sub$$\infty$/ and nonlinear adaptive control methods. The full-car model has seven degrees of freedom including heaving, pitching and rolling motions. In the active suspension system, the controller shows good performance: small gains from the road disturbances to the heaving, pitching and rolling accelerations of the car body. Also the controlled system must be robust to system parameter variations. As the control method, H$\sub$$\infty$/ controller is designed so as to guarantee the robustness of a closed-loop system in the presence of uncertainties and disturbances. The system parameter variations are taken into account by multiplicative uncertainty model and the system robustness is guaranteed by small gain theorem. The active system with H$\sub$$\infty$/ controller can reduce the accelerations of the car body in the heaving, pitching and rolling directions. The nonlinearity of a hydraulic actuator is handled by nonlinear adaptive control based on the back-stepping method. The effectiveness of the controllers is verified through simulation results in both frequency and time domains.

• International Journal of Control, Automation, and Systems
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• v.2 no.3
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• pp.374-383
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• 2004

This paper presents a neural network adaptive controller for autonomous diving control of an autonomous underwater vehicle (AUV) using adaptive backstepping method. In general, the dynamics of underwater robotics vehicles (URVs) are highly nonlinear and the hydrodynamic coefficients of vehicles are difficult to be accurately determined a priori because of variations of these coefficients with different operating conditions. In this paper, the smooth unknown dynamics of a vehicle is approximated by a neural network, and the remaining unstructured uncertainties, such as disturbances and unmodeled dynamics, are assumed to be unbounded, although they still satisfy certain growth conditions characterized by 'bounding functions' composed of known functions multiplied by unknown constants. Under certain relaxed assumptions pertaining to the control gain functions, the proposed control scheme can guarantee that all the signals in the closed-loop system satisfy to be uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed control scheme, and some practical features of the control laws are also discussed.

• Journal of Korean Port Research
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• v.14 no.3
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• pp.291-301
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• 2000

T his paper presents the lateral and longitudinal control algorithm for the driving of a 4WS AGV(Automated Guided Vehicle). The control law to the lateral and longitudinal control of the AGV includes adaptive agin tuning ability, that is the controller gain of the gravity compensated PD controller can be changed on a real-time. The gain tuning law is derived from the Lyapunov direct method using the output error of the reference model and the actual model, And to show the performance of the presented lateral and longitudinal control algorithm, we simulate toe nonlinear AGV equations of the motion by deriving the Newton-Euler Method, The read path is from quay yard area to docking position in loading yard area. The quay yard area is where the quay crane loads the container to the AGV and the docking position is where the container is transferred to the gantry crane. The road types are constructed in a straight line and J-turn. When driving the straight line, the driving velocity is 6㎧ and the J-turn is 3㎧.