1.2SYMay 2, 2019
Adaptive Intelligent Secondary Control of Microgrids Using a Biologically-Inspired Reinforcement LearningMohammad Jafari, Vahid Sarfi, Amir Ghasemkhani et al.
In this paper, a biologically-inspired adaptive intelligent secondary controller is developed for microgrids to tackle system dynamics uncertainties, faults, and/or disturbances. The developed adaptive biologically-inspired controller adopts a novel computational model of emotional learning in mammalian limbic system. The learning capability of the proposed biologically-inspired intelligent controller makes it a promising approach to deal with the power system non-linear and volatile dynamics without increasing the controller complexity, and maintain the voltage and frequency stabilities by using an efficient reference tracking mechanism. The performance of the proposed intelligent secondary controller is validated in terms of the voltage and frequency absolute errors in the simulated microgrid. Simulation results highlight the efficiency and robustness of the proposed intelligent controller under the fault conditions and different system uncertainties compared to other benchmark controllers.
2.5ROOct 13, 2014
Applied Neural Cross-Correlation into the Curved Trajectory Detection Process for Braitenberg VehiclesMatin Macktoobian, Mohammad Jafari, Erfan Attarzadeh Gh
Curved Trajectory Detection (CTD) process could be considered among high-level planned capabilities for cognitive agents, has which been acquired under aegis of embedded artificial spiking neuronal circuits. In this paper, hard-wired implementation of the cross-correlation, as the most common comparison-driven scheme for both natural and artificial bionic constructions named Depth Detection Module(DDM), has been taken into account. It is manifestation of efficient handling upon epileptic seizures due to application of both excitatory and inhibitory connections within the circuit structure. Presented traditional analytic approach of the cross-correlation computation with regard to our neural mapping technique and the acquired traced precision have been turned into account for coherent accomplishments of the aforementioned design in perspective of the desired accuracy upon high-level cognitive reactions. Furthermore, the proposed circuit could be fitted into the scalable neuronal network of the CTD, properly. Simulated denouements have been captured based on the computational model of PIONEER mobile robot to verify characteristics of the module, in detail.