Showing papers by "Mojtaba Ahmadieh Khanesar published in 2018"

A New Maximum Power Point Tracking Based on Modified Firefly Algorithm for PV System Under Partial Shading Conditions

Abstract: A photovoltaic (PV) system under partial shading condition (PSC) may experience several local maximum power points (MPP). Classical maximum power point tracking (MPPT) techniques, developed for uniform solar radiation on PV arrays, are incapable of discriminating between global and local maximum power points. In this paper, a modified firefly algorithm (MFA) is used and investigated with the objective of PV system MPP tracking under PSCs. A comprehensive evaluation among the proposed MFA, firefly algorithm (FA) particle swarm optimization (PSO), and perturbation and observation (P&O) method, as one of the classical methods of MPPT in uniform irradiance, is performed. Performances of the mentioned methods are studied under various PSCs in MATLAB/Simulink software environment. The obtained results show that under PSCs performances of the proposed method, PSO and FA methods in tracking the global MPP are very satisfactory. Furthermore, the proposed method has a higher tracking speed than FA and PSO methods under partial shading conditions.

Optimal synchronization of non-smooth fractional order chaotic systems with uncertainty based on extension of a numerical approach in fractional optimal control problems

Abstract: In this paper, a control mechanism is presented for optimal synchronization of two non-smooth fractional order chaotic systems with parametric uncertainty based on nonlinear fractional order proportional derivative (NLFPD) controller combined with optimal periodic control signals. Unlike synchronization methods based on FPID controllers, in this approach, optimal tuning of the NLFPD controller and determination of optimal periodic control signals for synchronization process are presented in the form of non-smooth fractional order optimal control (FOC) problems. Optimal periodic control signals are demonstrated as a generalized expansion in the sense of Fourier expansion that is used to accelerate the synchronization process. Using the generalization of a numerical method in nonlinear optimal control problems and the Grunwald-Letnikov(GL) fractional derivative definition, the synchronization problem based on the suggested mechanism is transformed into the form of a smooth FOC problem. By defining a base-time soft switch, a supervisory approach is added to the proposed control mechanism for desired and robust performance against parametric uncertainty in the slave system. Finally, to illustrate the proposed control mechanism, the synchronization of two identical fractional order Chua systems with simulation results is presented. The Results show that using the suggested control mechanism, the synchronization is fast and robust against parametric uncertainty in Chua slave system.

Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm

Abstract: An optimized design of a fuzzy logic system can be regarded as setting of different parameters of the system automatically. For a single parameter, there may exist multiple feasible values. Consequently, with the increase in number of parameters, the complexity of a system increases. Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. Extreme learning machine (ELM) is utilized to tune the consequent parameters of the interval type 2 fuzzy logic system (IT2FLS). A disadvantage of ELM is the random generation of its hidden neuron that causes additional uncertainty, in both approximation and learning. In order to overcome this limitation in an ELM-based IT2FLS, artificial bee colony optimization algorithm is utilized to obtain its antecedent parts parameters. The simulation results verified better performance of the proposed IT2FLS over other models with the benchmark data sets.

Improving the quality of service in network-based control systems:

Abstract: The use of data networks in control loops has received much attention recently due to its flexibility and economical advantages. In addition, mutual network usage has raised new challenges such as delay and data loss. This paper aims to reduce undesired effects of network by reducing the required traffic of the network. An estimation framework for network control system is introduced, in which estimations of local Kalman filter is sent to remote estimator based on the logic decided by a novel fuzzy communication logic. In order to do so, there exist two estimators, a remote estimator which estimates the states of the plant and its local copy that gives the same output. The output of the local estimator is compared with the real states of the system, if the states of the system are estimated with small error, there is no need to send data, hence, the probability of sending data is decreased using a fuzzy decision system. In order to optimize this fuzzy system, a particle swarm optimization (PSO) algorithm ...