• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.176-181
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• 1992

In this paper, an adaptive generalized predictive control(GPC) algorithm which minimizes a N-stage cost function is proposed. The resulting controller is based on GPC algorithm and can be used in unknown plant parameters as the parameters of one step ahead predictor are estimated by recursive least squares method. The estimated parameters are extended to G,P, and F amtrix which contain the parameters of N step ahead predictors. And the minimization of cost function assuming no constraints on future controls results in the projected control increment vector. Hence this adaptive GPC algorithm can be used for either unknown system or varing system parameters, and it is also shown through simulations that the algorithm is robust to the variation of system parameters. This adaptive GPC scheme is shown to have the same stability properties as the deterministic GPC, and requires small amount of calculation compared to other adaptive algorithms which minimize N-stage cost function. Especially, in case that the maximum output horizon is 1, the proposed algorithm can be applicable to direct adaptive GPC.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.10
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• pp.563-571
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• 2000

In this paper, a nonlinear adaptive control method for an EMS(Electro-Magnetic Suspension) system with mass uncertainty is proposed. Using the coordinate transformation and feedback linearizing control, EMS system has been transformed into the form of parametric strict-feedback system with unknown virtual control coefficients. With this transformed system, tuning functions approach, which is an advanced from of adaptive backstepping, has been applied in order to stabilize the system against mass uncertainty. Computer simulation is also carried out in order to compare the performance of the proposed controller with that of feedback linerizing controller.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.381-385
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• 1992

In this paper, dynamic modeling and adaptive control problems for a space robot system are discussed. The space robot consist of a robot manipulator mounted on a free-floating base where no attitude control is applied. Using an extended robot model, the entire space robot can be viewed as an under-actuated robot system. Based on nonlinear control theory, the extended space robot model can then be decomposed into two subsystems: one is input-output exactly linearizable, and the other is unlinearizable and represents an internal dynamics. With this decomposition, a normal form-augmentation approach and an augmented state-feedback control are proposed to facilitate the design of adaptive control for the space robot system against parameter uncertainty, unknown dynamics and unmodeled payload in space applications. We demonstrate that under certain conditions, the entire space robot can be represented as a full-actuated robot system to avoid the inclusion of internal dynamics. Based on the dynamic model, we propose an adaptive control scheme using Cartesian space representation and demonstrate its validity and design procedure by a simulation study.

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.694-699
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• 2001

This paper presents a control of active suspension system for quarter-car model with two-degree-of-freedom using $H_{inf}$ and nonlinear adaptive robust control method. Suspension dynamics is linear and treated by $H_{inf}$ method which guarantees the robustness of closed loop system under the presence of uncertainties and minimizes the effect of road disturbance to system. An Adaptive Robust Control (ARC) technique is used to design a force controller such that it is robust against actuator uncertainties. Simulation results are given for both frequency and time domains to verify the effectiveness of the designed controllers.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.688-693
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• 2007

The INS/GPS integration system is designed by employing an adaptive filter that can estimate the measurement noise variance using the residual of the filter. To verify the efficiency of the proposed loosely-coupled INS/GPS integration system, simulation is performed by assuming that GPS information has large position errors. Simulation results show that the proposed integration system with the adaptive filter is more effective in estimating the position and attitude errors than those with the Extended Kalman Filter.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.840-843
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• 1996

This paper presents an adaptive cutting force controller for milling process, which can be attached to most commercial CNC machining centers in a practical way. The cutting forces of X,Y and Z axes measured indirectly from the use of currents drawn by AC feed-drive servo motors. A typical model for the feed-drive control system of a horizontal machining center is developed to analyze cutting force measurement from the drive motor. The pulsating milling forces can be measured indirectly within the bandwidth of the current feedback control loop of the feed-drive system. It is shown that indirectly measured cutting force signals can be used in the adaptive controller for cutting force regulation. The robust controller structure is adopted in the whole adaptive control scheme. The conditions under which the whole scheme is globally convergent and stable are presented. The suggested control scheme has been implemented into a commercial machining center, and a series of cutting experiments on end milling and face milling processes are performed. The adaptive controller reveals reliable cutting force regulating capability under various cutting conditions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.3
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• pp.71-79
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• 2006

ABS is a safety device for preventing wheel locking in a sudden baking. Its control methods are classified into three types; deceleration control, wheel slip control and deceleration/acceleration control. The braking force takes the influence of the friction coefficient between road and tire, which in turn depends on the wheel slip as well as road conditions. In this paper, it has been proposed the wheel slip control system to apply the adaptive control method at the ABS. To maintain wheel slip to desired wheel slip, it have been done the linearization and designed the adaptive controller to apply gradient method based on the reference model. It is illustrated by computer simulations that the proposed control system gives good performances and adaptation to parameter variation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.2
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• pp.38-47
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• 2001

In this paper, a new scheme of adaptive-neuro control system is presented to implement real-time control of robot manipulator using Digital Signal Processors. Digital signal processors, DSPs, are micro-processors that are particularly developed for fast numerical computations involving sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of therir prices. These features make DSPs a viable computational tool in digital implementation of sophisticated controllers. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust perfor-mance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method.The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for the implementation of real-time control of robot system by the simulation and experi-ment.

• Journal of Biosystems Engineering
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• v.14 no.2
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• pp.80-84
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• 1989

In order to improve the combustion efficiency of the agricultural engine, an ignition timing control system was developed and tested. The control system was composed of the CDI ignition circuit, the microcomputer and the interfacing devices. In this study, the simplicity of the control system and the flexibility of the control strategy were emphasized for the precision, the applicability and the economical efficiency. The hardware was consisted in almost the same compositions as those of the automobile engine. The softwares of the control algorithms were developed to three types depending on the combination of the quasi-adaptive control and the open loop control which had the different spark advance equations according to the input variables such as engine speed, exhaust gas temperature and brake torque. The test results were summarized as follows: 1. By using the computer control system, the fuel consumption efficiency could be improved and the fuel consumption could be reduced by 0 to 57% compared to that of the fixed spark advance system. 2. The fuel consumption of the control mode with the quasi-adaptive algorithm was reduced by average 0.8% compared to that of the control mode without quasi-adaptive algorithm. 3. It was found that the control mode with the quasi-adaptive algorithm adopting single input of engine speed had most applicability and economical efficiency among three types of the control algorithms.