Multi Objective Optimization of Multi Component Isothermal Liquid-Phase Kinetic Sequence using Multivariable PI Control

In this paper, an optimal tuned saturated PI type controller with anti-windup structure is used for process control. In first step, a single objective genetic algorithm is used to find the optimal values of controller parameters. To show the difference between optimal and non-optimal control, we use this controller to track the square pulse. The results show that by choosing the control parameters randomly the output cannot track the reference signal but by optimizing the control parameters, the error, and settling time decreases significantly and efficiency of control increases but it needs more control effort. To find the optimal control parameters with lower control input, a multi objective genetic algorithm is used in next step and three points in Pareto front are analysed. It is shown that this method increases the control efficiency and needs lower control input than obtained by single objective genetic algorithm

Performance of optimal hierarchical type 2 fuzzy controller for load–frequency system with production rate limitation and governor dead band

AbstractControlling load–frequency is regarded as one of the most important control-related issues in design and exploitation of power systems. Permanent frequency deviation from nominal value directly affects exploitation and reliability of power system. Too much frequency deviation may cause damage to equipment, reduction of network loads efficiency, creation of overload on communication lines and stimulation of network protection tools, and in some unfavorable circumstances, may cause the network collapse. So, it is of great importance to maintain the frequency at its nominal value.It would be useful to make use of the type 2 fuzzy in modeling of uncertainties in systems which are uncertain. In the present article, first, the simplified 4-block type-2 fuzzy has been used for modeling the fuzzy system. Then, fuzzy system regulations are reduced by 33% with the help of hierarchy fuzzy structure. The simplified type-2 fuzzy controller is optimized using the Cuckoo algorithm. Eventually, the performance of the proposed controller is compared to the Mamdani fuzzy controller in terms of the ISE, ITSE, and RMS criteria

Design of Combined Sliding Mode Controller Back Stepping Using Genetic Algorithm

This research has tried to achieve a new robust controller with back stepping control and sliding mode control method. Also as we know, in all analytical controllers there are constant coefficients like the back stepping and sliding mode controllers, redesigning the Lyapunov and the feedback linearization, - and so forth. There are two major problems in their set: firstly, the adjustment is cumbersome and time-consuming. Secondly, assuming that these parameters can be adjusted to workability, a designer can never tell exactly what are the parameters chosen to be optimal. To resolve this problem, the numerical algorithm which is a genetic algorithm is used here and we have the optimal parameters of the proposed controller. That genetic algorithm (GA) has been used to solve difficult engineering problems that are complex and difficult to solve by conventional optimization methods, and at the end of this section, we apply a new robust controller on ball and beam system. Simulation results are expressed

Synchronisation of 6D hyper-chaotic system with unknown parameters in the presence of disturbance and parametric uncertainty with unknown bounds

In this paper, adaptive-sliding mode control method is proposed for synchronisation of two 6D hyper-chaotic systems in the presence of external disturbance and parametric uncertainty and unknown parameters in the slave system. In the first section of this paper, two 6D integer order hyper-chaotic systems in the presence of external disturbance signal, parametric uncertainty and unknown parameters in the slave system are studied. In the second part of this paper, after identifying chaos in fractional order dynamic of the mentioned system, synchronisation of two 6D fractional derivative hyper-chaotic systems in the presence of external disturbance signal, parametric uncertainty and unknown parameters in the slave system is investigated, in which fractional order Riemann-Liouville derivative is used; a new fractional order sliding surface is defined for the hyper-chaotic system to determine the proper active control. Proper adaptive control laws are used to estimate the uncertainty bound, unknown disturbance signal and system parameters. Stability of the closed-loop control system is proved using Lyapunov theory in both modes. Simulation results in MATLAB show the desired application of the proposed controllers in the presence of disturbance and parametric uncertainty

Enhancing Fingerprint Image Recognition Algorithm Using Fractional Derivative Filters

One of the most important steps in recognizing fingerprint is accurate feature extraction of the input image. To enhance the accuracy of fingerprint recognition, an algorithm using fractional derivatives is proposed in this paper. The proposed algorithm uses the definitions of fractional derivatives Riemann-Liouville (R-L) and Grunwald-Letnikov (G-L) in two sections of direction estimation and image enhancement for the first time. Based on it, new mask of fractional derivative Gabor filter is calculated. The proposed fractional derivative-based method enhances the image quality. This method enhances the structure of ridges and grooves of fingerprint, using fractional derivatives. The efficiency of the proposed method is studied in images of FVC2004 (DB1, DB2, DB3 and DB4) database and the results are evaluated using the criteria including entropy, average gradient, and edge intensity. Also, performance of the proposed method is compared with other technical methods such as Gabor filter. Based on the obtained results from the tests, the method is able to enhance the quality of fingerprint images significantly