Excerpt from TE Kalayci et al. (2018):
~ Jalili-Kharaajoo et al. apply intelligent controller to traffic control of ATM networks. First, the dynamics of the network is modeled by a locally linear neuro-fuzzy model. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. Simulation results show that the proposed fuzzy traffic controller can outperform the traditional usage parameter control mechanisms in terms of better selectivity and effectiveness.
~ Mehrabian et al. present a theoretical analysis of online autonomous intelligent adaptive tracking controller based on BELBIC for aerospace launch vehicle. The algorithm is very robust and fast in terms of adapting to dynamic change in the plant, due to its online learning ability. Development and application of this algorithm for an aerospace launch vehicle during atmospheric flight in an experimental setting is presented to illustrate the performance of the control algorithm.
~ Rouhani et al. propose a BELBIC based control to govern the dynamics of electrically heated micro-heat exchanger plant. First, the dynamics of the micro-heat exchanger, which acts as a nonlinear plant, is identified using a neuro-fuzzy network. To build the neuro-fuzzy model, a locally linear learning algorithm, called LoLiMoT, is used. Then, a BELBIC based controller is applied to the identified model. The impact of BELBIC in improving the control system performance is shown by comparing the results obtained from classic PID controller without BELBIC. The results demonstrate excellent improvements of control action without any considerable increase of control effort.
~ Khorramabadi et al. describe the design and evaluation of a reactor core power control based on emotional learning. The controller includes a neuro-fuzzy system with power error and its derivative as inputs. A fuzzy critic evaluates the current situation and provides the emotional signal (stress). The controller modifies its characteristics so that the critical stress is reduced. Simulation results show that the controller has good convergence and performance robustness characteristics over a wide range of operational parameters.
~ Jamali et al. utilise a model driven development approach for implementation of emotional learning as a bio-inspired algorithm. They implement the BELBIC model on FPGA. They then use the obtained embedded emotional controller, called E-BELBIC, for con- trolling cranes.
~ Dehkordi et al. develop a BELBIC based control mechanism for the switched reluctance motor (SRM) speed. Motor parameter changes, operating point changes, measurement noise, open circuit fault in one phase and asymmetric phases in SRM are also simulated to show the robustness and superior performance of BELBIC. To compare the BELBIC performance with other intelligent controllers, Fuzzy Logic Controller (FLC) is developed and system responses with BELBIC and FLC are compared.
SOURCE: How Wireless Sensor Networks Can Benefit from Brain Emotional Learning Based Intelligent Controller (BELBIC)