• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.4
• /
• pp.509-518
• /
• 2014

Though various techniques have been studied as a way of adjusting parameters of PID controllers, no perfect method of determining parameters is available to date. Especially the deign of PID controller for unstable processes with dead time(UPWDT) is even more difficult due to various reasons. Generally the existing design procedures for UPWDT involve deriving formulas to meet gain and phase margin specifications, or using inner loop to stabilize UPWDT before applying PID controller. In this paper, the dual-input describing function(DIDF) method is proposed, by which the performance and robustness of the closed-loop system can be improved. The method is based on moving the critical point (-1+j0) of Nyquist stability to a new position arbitrarily selected on the complex plane. This can be done by determining appropriate coefficients of the DIDF. As a result, we can easily determine parameters of PID-type controller by using existing conventional tuning methods for stable or unstable systems. Simulation results are included to show the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.59 no.1
• /
• pp.184-191
• /
• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.41 no.4
• /
• pp.21-28
• /
• 2004

This paper presents a direct multivariable self-tuning controller using neural network which adapts to the changing parameters of the higher order multivariable nonlinear system with nonminimum phase behavior, mutual interactions and time delays. The nonlinearities are assumed to be globally bounded, and a multivariable nonlinear system is divided linear part and nonlinear part. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm the computer simulation is done to adapt the multivariable nonlinear nonminimm phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct multivariable adaptive controller using neural network.

• Proceedings of the KIEE Conference
• /
• 2015.07a
• /
• pp.1106-1107
• /
• 2015

A flux pump (FP) can inject the DC current into high temperature superconducting (HTS) field coils of a HTS rotating machine without slip ring and current lead. However, it has limits to improve the value of DC current, and has time constants of DC current according to inductances of the HTS field coils. When a large-scale HTS generator with the FP is designed, a proper point about the inductance, field current, and time constant is demanded to decide parameters of the generator. In this paper, a parameter tuning skill of a large-scale superconducting wind power generator for applying a FP has been proposed. The design of the FP has been fixed, and 12 MW HTS generators have been variously designed by adjusting parameters related with the inductance of the HTS field coil. The induced current values have been calculated based on the FP design. The time constants of the induced currents depending on the DC current values and inductances of the generator have been represented. The results of the parameter tuning of the HTS generator have been discussed in detail.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.39 no.6
• /
• pp.1-8
• /
• 2002

This paper presents a generalized minimum-variance self-tuning controller with a PID structure using neural network which adapts to the changing parameters of the nonlinear system with nonminimum phase behavior and time delays. The neural network is used to estimate the controller parameters, and the control output is obtained through estimated controller parameter. In order to demonstrate the effectiveness of the proposed algorithm, the computer simulation is done to adapt the nonlinear nonminimum phase system with time delays and changed system parameter after a constant time. The proposed method compared with direct adaptive controller using neural network.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10b
• /
• pp.265-271
• /
• 1992

It is the purpose of this paper to present a dialogical designing method for control system using a rough grasp of the unknown process property. We deal with a single-input single-output feedback control system with a fuzzy controller. The process property is roughly estimated by the step response, and the fuzzy controller is interactively modified according to the operator's requests. The modifying rules mainly derived from computer simulation are useful for almost every process, such as an unstable process and a non-minimum phase process. The fuzzy controller is tuned by taking notice of four characteristics of the step response: (1) rising time, (2) overshoot, (3) amplitude and (4) period of vibration. The tuning position of the controller is fourfold: (1) antecedent gain factor GE or GCE, (2) consequent gain factor GDU, (3) arrangement of the antecedent fuzzy labels and (4) arrangement of the control rules. The rules give an instance to the respective items of the controller in an effective order. The modified fuzzy PI controller realizes a good response of a stable process. However, because the GDU tuning becomes difficult for the unstable process, it is necessary to evaluate the stability of the process from the initial step response. The fuzzy PI controller is applied to the process whose initial step response converges with GDU tuning. The fuzzy PI controller with modified sampling time is applied to the process whose step response converges under the repeated application of the GDU tuning. The fuzzy PD controller is applied to the process whose step response never converges by the GDU tuning.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.9 no.3
• /
• pp.240-245
• /
• 2004

This paper presents a Switched Reluctance Motor(SRM) drive using the self-tuning control method to achieve the maximum torque. SRM has the difficulty to research it by an analytic method and to control the speed End torque because of the high nonlinearity. So, in this paper, the self-tuning control method is applied to relevantly controlling turn-on/off angle to operate at the maximum torque. Also, the feedback signals to control the turn-on/off angle are the encoder pulse and the increment of phase current. At first, n adequate turn-off angle is searched by itself and then a turn-on angle is done. As the relationship between turn-on and him-off angle is mutual dependent, the turn-on/off angle is controlled by a real time self-tuning control method in order to maintain the maximum torque. The proposed self-tuning Algorithm is verified by experiments.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.6
• /
• pp.875-882
• /
• 2023

In this paper, we propose a virtual tuning technique for a temperature controller using the PID algorithm. Virtual simulation on a computer was used using the mathematical expression of the control object. A technique for accurately calculating the gain of the PID algorithm was introduced through detailed computer data analysis, and superior performance compared to conventional experimental tuning results was verified. In addition, it has the advantage of replacing tuning experiments conducted on actual control subjects, so there are no temporal or spatial limitations. Tuning experiments that actually operate the control object do not show detailed data that appears during the process. The accuracy of the experiment could not be guaranteed, and the results could not be confirmed immediately. Through the proposed technique, the entire tuning process can be accurately checked on a computer and the cause of problems that occur can also be analyzed.

• The Transactions of the Korean Institute of Electrical Engineers
• /
• v.39 no.4
• /
• pp.364-370
• /
• 1990

In this paper, we propose a self-tuning PID controller for unknown systems with time-varying delay. Using pole placement equations, we derive the controller that can be extended to the multi-step time delay case. The time-varying delays are estimated by a prediction error delay method using multiple predictors. Since the order of the estimation vector is not increased, the persistant exciting condition of control input is alleviated. Since the least square method gives biased parameter estimates for colored noise cases, the recursive instrumental variable method is used to estimate system parameters. The computational burden of the proposed method is less than the conventional adaptive methods. Computer simulations are performed to illustrate the efficiency of the proposed method.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.1840-1841
• /
• 2006

Plants with long time-delays can not be often controlled effectively using a simple PID controller. The main reason for this is that the additional phase lag contributed by the time-delay tends to destabilize the closed-loop system. The stability problem can be solved by smith predictor. However, in this case responses are very sensitive to the estimated model errors. To reduce sensitive problem, this paper is presented based on virtual reference feedback tuning of the time delay plant using the closed-loop test to find parameters for a PID controller using the closed-loop test data.