• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.203-207
• /
• 1992

In this paper, a new GPC(Generalized Predictive Control) algorithm which is robust to disturbances isproposed. This controller minimizes the LQ cost function when the disturbance maximizes this cost function. The solution is obtained from the min-max problem which can be solved by differential game theory and has the non-recursive form which does not use the Riccati equation. Its another solution for state space models is investigated.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.1
• /
• pp.27-36
• /
• 2014

This paper presents an adaptive wide-area damping controller (WADC) based on generalized predictive control (GPC) and model identification for damping the inter-area low frequency oscillations in large-scale inter-connected power system. A recursive least-squares algorithm (RLSA) with a varying forgetting factor is applied to identify online the reduced-order linearlized model which contains dominant inter-area low frequency oscillations. Based on this linearlized model, the generalized predictive control scheme considering control output constraints is employed to obtain the optimal control signal in each sampling interval. Case studies are undertaken on a two-area four-machine power system and the New England 10-machine 39-bus power system, respectively. Simulation results show that the proposed adaptive WADC not only can damp the inter-area oscillations effectively under a wide range of operation conditions and different disturbances, but also has better robustness against to the time delay existing in the remote signals. The comparison studies with the conventional lead-lag WADC are also provided.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.318-322
• /
• 1996

In this study, a controller design method is proposed for controlling the discrete-time chaotic systems efficiently. Our proposed control method is based on Generalized Predictive Control and uses NARMAX models as a controlled model. In order to evaluate the performance of our proposed controller design method, a proposed controller is applied to Henon system which is a discrete-time chaotic system, and then the control performance of the proposed controller are compared with those of the previous model-based controllers through computer simulations. Through simulations, it is shown that the control performance of the proposed controller is superior to that of the conventional model-based controller.

• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.5
• /
• pp.139-147
• /
• 1996

PID control has been widely used for real control systems. Particularly, there are many researches on control schemes of tuning PID gains. However, to the best of our knowledge, there is no result for discrete-time systems with unknown time-delay and unknown system parameters. On the other hand, Generalized predictive control has been reported as a useful self-tuning control technique for systems with unknown time-delay. So, in this study, based on minimization of a GPC criterion, we present a self-tuning PID control algorithm for unknown papameters and unknown time-delay system. A numerical simulation was presented to illustrate the effectiveness of this method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.11 no.1
• /
• pp.19-26
• /
• 2005

This paper is concerned with the design of a predictive PID controller which has similar features to the model-based predictive controller. A PID type control structure is defined, which includes prediction of the outputs and the recalculation of new set points using the future set point data. The optimal values of the PID gains are precalculated using the values of gains calculated from an unconstrained generalized predictive control algorithm. Simulation studies demonstrate the performance of the proposed controller and the results are compared with the conventional PID and fuzzy control algorithms.

• 제어로봇시스템학회:학술대회논문집
• /
• 1987.10a
• /
• pp.734-740
• /
• 1987

Various kinds of predictive control design methods such as MAC(Model Algorithmic Control), DMC(Dynamic Matrix Control), MC(Extended Horizon Adaptive Control), GPC(Generalized Predictive Control), RHTC(Receding Horizon Tracking Controller), and PVC(PreView Controller) are surveyed and compared in this paper. In addition, stability properties of these control laws known to date are summarized and some new stability results are presented.

• 제어로봇시스템학회:학술대회논문집
• /
• 1990.10a
• /
• pp.225-228
• /
• 1990

The major difficulty in distillation column control lies in executing the set point tracking and the disturbance rejection, because of continuous changes in model order and dead time. For that, generalized predictive control(GPC) was applied to distillation column control. Recursive least square method was used to adjust the changes of model order and dead time. Quadratic progamming(QP) was used to solve the constraint problems in control action and the rate of control action. As a result of the simulation on the dynamic simulator(SPEEDUP) and the experiment on pilot plant, the ability of the set point tracking and the disturbance rejection was acceptable to apply to the real distillation column.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.7
• /
• pp.66-75
• /
• 1994

In this paper, we present a new GPC algorithm for plants with known disturbances. Also, based on the above algorithm unidirectional and bidirectional predictive controllers have been proposed concerning plants with input couplings between subprocesses. These predictive control algorithms can be applied to many industrial processes as a multiloop controllers. Simulation results show that the proposed algorithm has improved performance and computing time compared to conventional GPC algorithm.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.172.4-172
• /
• 2001

In this paper, the Generalized Predictive Control(GPC) method based on Fuzzy Neural Networks(FNNs) is presented for the control of chaotic nonlinear systems without precise mathematical models. In our method, FNNs is used as the predictor whose parameters are tuned by the error between the actual output of nonlinear chaotic system and that of FNNs model. The parameters of GPC controller are adjusted via the gradient descent method where the difference between the actual output and the reference signal is used as a control error. Finally, computer simulation on the representative continuous-time chaotic system(Duffing system) is presented to demonstrate the effectiveness of our chaos control method.

• Journal of the Korean Society for Precision Engineering
• /
• v.18 no.11
• /
• pp.58-66
• /
• 2001

GPC has been reported as a useful self-tuning control algorithm for systems with unknown time-delay and parameters. GPC is easy to understand and implement, and thus has won popularity among many practicing engineers. Despite its success, GPC does not guarantee is nominal stability. So, in this paper, GPC is rederived in frequency domain instead of in the time domain to guarantee its nominal stability. Derivation of GPC in frequency domain involves spectral factorization and Diophantine equation. Frequency domain GPC control law is stable because the zeros of characteristic polynomial are strictly Schur. Recursive least square algorithm is used to identify unknown parameters. To see the effectiveness of the proposed controller, the controller is simulated for a numerical problem that changes in dead-time, in order and in parameters.