International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 07 | July -2017
p-ISSN: 2395-0072
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Optimization of FUZZY controller by JAYA algorithm Mr.Jadhav Vaibhav Hari1, Mr.A.B.Patil2 PG Student Dept. of Electrical Engineering WCE Sangli Maharashtra, India Professor, Dept. of Electrical Engineering WCE Sangli, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract- This paper describes a new approach of designing an optimized fuzzy controller, Optimized by Jaya algorithm, In view of this discussion for optimization of Fuzzy controller, we should design a controller for nonlinear time-delay systems so that the performance of the dynamical system can be improved. We aim to use a set of fuzzy rules to describe a global nonlinear system into a set of local time-delay systems with uncertain nonlinear functions and optimization is done by JAYA algorithm.
to have optimum solution or near optimal fuzzy rule and membership function. However by applying JAYA Algorithm in the design process, optimal or near optimal fuzzy rules and membership function can be found without any priory knowledge. The designing of Fuzzy JAYA controller is different than designing of traditional fuzzy control system. In traditional fuzzy control system, the FAM rules and membership functions are determined by operator’s knowledge and after that parameters are fixed. But in case of Fuzzy JAYA controller the FAM rules and membership functions are adapted by JAYA algorithm that searches the parameter space for an optimal set of parameters based upon a specific parameters and population size. [4]
Keywords- Nonlinear system Continuous stirrer tank reactor (CSTR), T-S Fuzzy system Type-1 Fuzzy controller, JAYA optimization algorithm
The latter part of the paper is arranged in the following sequence. Section 2 presents type 1 fuzzy controller design and optimization for triangular membership functions and optimization by JAYA algorithm is presented in section 3. The results of hardware implementation of controllers are presented in section 4. The main conclusions are reached through analysis of results.
1. INTRODUCTION Many available system contain nonlinearity characteristics, such as microwave oscillation, chemical process, hydraulic system, etc. It important to study behaviour of nonlinear system. The most important part in nonlinear system is Optimization. The dynamic of non-linear system can be strongly depends on either one or more parameter since their operative condition remain stable only if the value of parameters are must be in specific limit. If these parameter gone out of range then the equilibrium point become unstable. Because of this reason, nonlinear controllers like Fuzzy logic controller are used to control such system because they are more robust than other controllers. [2]Fuzzy technique has been widely and effectively used now a days in nonlinear system modelling and control for more than two decades. In many of the model based fuzzy control approaches, the famous t-s fuzzy model is a popular and convenient tool in functional approximation. [3, 4] CSTR is one of the most commonly used non-linear system, which is mostly used in chemical industries, it offers a verity of researches in the area of chemical and control engineering. Due to non-linearity presents in the system, performance of the conventional controller may not be proper. Hence complexity of the system analysis increases. [6] It becomes difficult to have results under certain conditions. So here Type-1 fuzzy controller is used and optimized, for optimization, Jaya algorithm is used, because it is one of the best optimization algorithm which gives good performance and results. [1] Traditional fuzzy logic controller design is based upon a human operator’s experience or control engineer’s knowledge. Since this method uses trial and error to find better fuzzy rules and membership function, it is very time consuming method. Also this method doesn’t guarantee
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2. FUZZY CONTROLLER DESIGN Most of the available physical dynamical systems in real life, which are not possible to be represented by linear differential equations and have a nonlinear nature. On another side, linear control methods depends on the key assumption of small range of operation for the linear model and, acquired from linearizing the nonlinear system, to be valid. When the required operation range is large, a linear controller is unstable, because the nonlinearities in the plant cannot be properly dealt with the controller. One more assumption of the linear control is that the system model is definitely linearizable and the linear model is much accurate enough for building up the controller. However, the highly nonlinear and discontinuous nature of many system for example, mechanical and electrical systems does not allow linear approximation practically. As In the process of designing controllers, it is also necessary that the system model is well achievable through a mathematical model and the parameters of the system model are reasonably wellknown for controller design. For many practically available nonlinear plants i.e. chemical processes, building a mathematical model with mathematical equation is very difficult and only the input-output data yielded from running the process is accessible for the estimation. Many control problems involve uncertainties like parametric, dynamic etc.
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