• 대한기계학회논문집A
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• 제27권7호
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• pp.1146-1151
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• 2003

In this paper, results of an analysis of driving behavior characteristics and a driver-adaptive control algorithm for adaptive cruise control systems have been described. The analysis has been performed based on real-world driving data. The vehicle longitudinal control algorithm developed in our previous research has been extended based on the analysis to incorporate the driving characteristics of the human drivers into the control algorithm and to achieve natural vehicle behavior of the adaptive cruise controlled vehicle that would feel comfortable to the human driver. A driving characteristic parameters estimation algorithm has been developed. The driving characteristics parameters of a human driver have been estimated during manual driving using the recursive least-square algorithm and then the estimated ones have been used in the controller adaptation. The vehicle following characteristics of the adaptive cruise control vehicles with and without the driving behavior parameter estimation algorithm have been compared to those of the manual driving. It has been shown that the vehicle following behavior of the controlled vehicle with the adaptive control algorithm is quite close to that of the human controlled vehicles. Therefore, it can be expected that the more natural and more comfortable vehicle behavior would be achieved by the use of the driver adaptive cruise control algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.7-12
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• 1993

This paper presents a generalized truncated least, squares adaptive algorithm and a two-stage design method. The proposed algorithm is directly derived from the normal equation of the generalized truncated least squares method (GTLSM). The special case of the GTLSM, the truncated least squares (TLS) adaptive algorithm, has a distinct features which includes the case of minimum steps estimator. This algorithm seemed to be best in the deterministic case. For real applications in the presence of disturbances, the GTLS adaptive algorithm is more effective. The two-stage design method proposed here combines the adaptive control system design with a conventional control design method and each can be treated independently. Using this method, the validity of the presented algorithms are examined by the simulation studies of an indirect adaptive control.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1994년도 추계학술대회 논문집
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• pp.330-335
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• 1994

In this paper, adaprive control and sliding mode control are combined to implement the proposed adaptive sliding mode control(ASMC) algorithm which is new approach to the control of industrial robot manipulator with external disturbances and parameter uncertainties. Adaptive control algorithm is designed by using the principle of the model reference adaptive control method based upon the hyperstability theory. The contribution of this method is that the parameters of the sliding surface are replaced by time varying parameters whose are calculated by an adaptation algorithm, which forces the errors to follow the behavior of a reference error model. Simulation results show that the proposed method not only improves the performance of the system but also reduces the chattering problem of sliding mode control. Consequently, it is expected that the new adaptive sliding mode control algorithm will be suited for various practical applications.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.341-344
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• 1993

This paper presents the general MRAC algorithm design, it's real time implementation and investigates the effect of purely deterministic disturbances to adaptive control algorithm. The design of adaptive control algorithm to reject the disturbances properly is also presented. In real time application, adaptive control algorithm is considered to investigate its performance by using DC motor. Disturbance rejection algorithm is investigated in simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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• pp.331-334
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• 1996

It is a well-known problem that the adaptive control has a poor transient response. In order to improve this problem, the scheme that model-reference adaptive control (MRAC) uses the genetic algorithm (GA) in the search for parameters is proposed. Use genetic algorithm (GA) in the searching for controller's parameters set and conventional gradient method for fine tuning. And show the reduction of the oscillations in transient response comparing with the conventional MRAC.

• 제어로봇시스템학회논문지
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• 제13권1호
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• pp.19-25
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• 2007

The MFC(Mass Flow Controller) is an equipment that measures and controls mass flow rates of fluid. Most of the HFC system is still using the PID algorithm. The PID algorithm shows superior performance on the MFC system. But the PID algorithm in the MFC system has a few problems as followed. The characteristic of the MFC system is changed according to the operating environment. And, when the piezo valve that uses the control valve is assembled in the MFC system, a coupling error is generated. Therefore, it is very difficult to find out the exact parameters of MFC system. In this paper, we propose adaptive PID algorithm in order to compensate these problems of a traditional PID algorithm. The adaptive PID algorithm estimates the parameters of MFC system using LMS(Least Mean Square) algorithm and calculates the coefficients of PID controller. Besides, adaptive PID algorithm shows better transient response because adaptive PID algorithm includes a feedforward. And we implement MFC system using proposed adaptive PID algorithm with self-tuning and Ziegler and Nickels's method. Finally, comparative analysis of the proposed adaptive PID and the traditional PID is shown.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1993년도 한국자동제어학술회의논문집(국제학술편); Seoul National University, Seoul; 20-22 Oct. 1993
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• pp.378-384
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• 1993

In this paper, an intelligent adaptive controller is proposed for the process with unmodelled dynamics. The intelligent adaptive controller consists of the numeric adaptive controller and the intelligent tuning part. The continuous scheme is used for the numeric adaptive controller to avoid the problems occurred in the discrete time schemes. The adaptive controller is adopted to the process with time delay. It is an implicit adaptive algorithm based on GMV using the emulator. The tuning part changes the design parameters in the control algorithm. It is a multilayer neural network trained by robustness analysis data. The proposed method can improve the robustness of the adaptive control system because the design parameters are tuned according to the operating points of the process. Through the simulation, robustnesses are shown for intelligent adaptive controller. Finally, the proposed algorithms are implemented on the electric furnace temperature control system. The effectiveness of the proposed algorithm is shown from experiments.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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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.

• 제어로봇시스템학회논문지
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• 제2권2호
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• pp.67-72
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• 1996

This paper presents a parameter adaptive control law that stabilizes and asymptotically regulates any single-input, linear time-invariant, controllable and observable, discrete-time system when only the upper bounds on the order of the system is given. The algorithm presented in this paper comprises basically a nonlinear state feedback law which is represented by functions of the state vector in the controllable subspace of the model, an adaptive identifier of plant parameters which uses inputs and outputs of a certain length, and an adaptive law for feedback gain adjustment. A new psedu-inverse algorithm is used for the adaptive feedback gain adjustment rather than a least-square algorithm. The proposed feedback law results in not only uniform boundedness of the state vector to zero. The superiority of the proposed algorithm over other algorithms is shown through some examples.

• 전기학회논문지
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• 제60권1호
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• pp.150-155
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• 2011

In an active noise control systems, an IIR filter may cause a problem in stability beacause of its poles. For IIR filter, its poles goes sometimes out of a unit circle in a z-plane in the transition state, where the adaptive algorithm converges to the optimum value, which causes the system to diverge. Fuzzy LMS algorithm has a better convergence property than conventional LMS algorithms, but is not applicable to IIR filter because of the reasons. Stabilized adaptive algorithm could be improves stability by moving the pole of IIR filer toward the origin forcibly in the transient state, and by introducing forgetting factor to maintain the optimum convergence when it reaches to the steady state. In this paper, We proposed stabilized adaptive fuzzy LMS algorithms with IIR filter structures, for single channel active noise control with ill conditioned signal case. Computer simulations were performed to show the effectiveness of a proposed algorithm.