Input Scaling Factors Research Articles

A New Self-tuning Design for PID-type Fuzzy Logic Controllers

In this study, a new method is proposed for tuning the coefficients of PID type fuzzy logic controllers. The new method adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller using a fuzzy inference mechanism in an on-line manner. The fuzzy inference mechanism that adjusts the related coefficients has two inputs one of which is called “normalized acceleration” and the other one is the classical “error”. The “normalized acceleration” gives the “relative rate” information about the fastness or slowness of the system response. An appropriate rule-base is generated for the adaptation of the derivative coefficient of the PID type fuzzy logic controller using these two input variables. The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations. The simulations are done on a second order system with varying parameters and time delay.

Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer

In this study, a new method is proposed for tuning the coefficients of PID-type fuzzy logic controllers (FLCs). The new method adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID-type FLC using a fuzzy inference mechanism in an on-line manner. The fuzzy inference mechanism that adjusts the related coefficients has two inputs, one of which is called “normalized acceleration” and the other one is the classical “error”. The “normalized acceleration” gives the “relative rate” information about the fastness or slowness of the system response. An appropriate rule-base is generated for the adaptation of the derivative coefficient of the PID-type FLC using these two input variables. The integral coefficient is then updated as the reciprocal of the derivative coefficient. The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations. The simulations are done on a second-order system with varying parameters and time delay.

Robust fuzzy controlled photovoltaic power inverter with Taguchi method

This paper presents design and implementation of a robust fuzzy controlled photovoltaic (PV) power inverter with Taguchi tuned scaling factors. To achieve fast transient response, small steady-state error and system robustness, a robust fuzzy controller is adopted, in which its input and output scaling factors are determined efficiently by using the Taguchi-tuning algorithm. The proposed system can operate in different modes, grid-connection mode and stand-alone mode, and can accommodate wide load variations. Simulation results and hardware measurements obtained from a prototype with a microcontroller (Intel 80196KC) are presented to verify the theoretical discussions, and its adaptivity, robustness and feasibility.

Application of Self-Tuning Fuzzy Logic Controller on Process Control

An advanced self-tuning fuzzy logic controller (STFC) is reported in this paper. The STFC is basically an FLC but with the addition of performance measure and inference step functions. The performance measure is used to evaluate the system response and the inference step is used to apply reasoning that base on a set of fuzzy “meta rules”. The key function of the STFC is to provide automatic tuning of input scaling factors to the fuzzy logic controller. After simulation, the algorithm of the STFC is tested on the PT326 Process Trainer. The performance of the PID, FLC and STFC are compared and analyzed.

Genetic synthesis of fuzzy logic controllers in turning

The paper presents an optimal fuzzy logic controller design using efficient robust optimization techniques called genetic algorithms. It is shown that genetic algorithms can automatically search input and output scaling factors, membership functions, and fuzzy rules of the fuzzy logic controllers based on a fitness function. As a result, fuzzy logic controllers with optimal control performance can be systematically constructed instead of using a time-consuming trial and error approach. An adaptive force control system in turning operations is then used to illustrate the proposed method. It is shown that the developed fuzzy logic controller can achieve an automatic adjustment of feed rate to optimize the production rate with a constant cutting force in turning operations.

Fractional Fourier optics

There exists a fractional Fourier-transform relation between the amplitude distributions of light on two spherical surfaces of given radii and separation. The propagation of light can be viewed as a process of continual fractional Fourier transformation. As light propagates, its amplitude distribution evolves through fractional transforms of increasing order. This result allows us to pose the fractional Fourier transform as a tool for analyzing and describing optical systems composed of an arbitrary sequence of thin lenses and sections of free space and to arrive at a general class of fractional Fourier-transforming systems with variable input and output scale factors.

Scaling of fuzzy controllers using the cross-correlation

The paper deals with the optimal adjustment of input scaling factors for fuzzy controllers (FCs). The method is based on the assumption that in the stationary case an optimally adjusted input scaling factor meets a specific statistical input output dependence. A measure for the strength of statistical dependence is the correlation function and the correlation coefficient, respectively. Without loss of generality, the adjustment of input scaling factors using correlation functions is pointed out by means of a single input-single output (SISO)-system. First, the paper deals with the so-called equivalent gain which is closely connected to the cross-correlation of the controller input and the defuzzified controller output. Next, it considers the computation of correlation functions and their representation inside the FC. The paper concludes with an example of a system of fuzzy rules controlling a redundant robot manipulator. >

Fuzzy Logic Control of Steam Generator Water Level in Pressurized Water Reactors

In this paper a fuzzy logic controller is applied to control the steam generator water level in a pressurized water reactor. The method does not require a detailed mathematical mode of the object to be controlled. The design is based on a set of linguistic rules that were adopted from the human operator's experience. After off-line fuzzy computation, the controller is a lookup table, and thus, real-time control is achieved. Shrink-and-swell phenomena are considered in the linguistic rules, and the simulation results show that their effect is dramatically reduced. The performance of the control system can also be improved by changing the input and output scaling factors, which is convenient for on-line tuning.

Approximate Synthesis of Plane Four-Bar Mechanism for Function Generation

By extending the five point approximation and the fifth order approximation presented by F. Freudenstein, an analytical method is established for the synthesis of a plane four-bar mechanism to generate approximately a desired founction over a finite interval. This method may be regarded as a general method in the synthesis where the input and output scale factors are specified in advance. Moreover, in order to reduce the structural error of the function generator designed by using this method, a new numerical procedure for respacing precision points is suggested on the basis of the tenth degree polynomial closely approximating the structuralerror.