Brain Emotional Learning Based Intelligent Controller Research Articles

BELBIC for MRAS with highly non-linear process

Model Reference Adaptive Systems (MRASs) use mostly the traditional MIT rule based controllers to drive the difference (error) between the model reference signal and actual output one to zero value. MIT rule based controllers are slow and cause large error values in case of highly non-linear process. In this paper, we propose the Brain Emotional Learning Based Intelligent Controller (BELBIC) to replace the MIT rule based one. BELBIC benefits Brain Emotional Learning modeled algorithm in mammalians brain to seek the proper control signal that eliminates the error. In spite of some overshoots in MRAS with BELBIC, simulation of the proposed BELBIC for MRAS with its large number of adjustable gains achieves remarkable fast response.

Unmanned Ground Vehicle Navigation Using Brain Emotional Learning Based Intelligent Controller (BELBIC)

In this paper, we implement a novel control strategy for navigation of an Unmanned Ground Vehicle (UGV). This strategy consisted in the development and implementation of the Brain Emotional Learning Based Intelligent Controller (BELBIC) for heading, and path control of a UGV. BELBIC is an intelligent controller based on the model of the Amygdala-Orbitofrontal system of mammalians, which is a region in the brain known to be responsible for emotional learning process. Simulation of this controller for the cases of heading, and path control showed to be very robust and adaptable to dynamical changes in the plant. A comparison between BELBIC and a traditional PID control is presented to illustrate the performance of this control strategy.

A Sliding Mode Control Using Brain Limbic System Control Strategy for a Robotic Manipulator

This paper presents a robust bio-inspired sliding mode control approach, designed to achieve a favourable tracking performance in a class of robotic manipulators with uncertainties. To this end, brain emotional learning-based intelligent control (BELBIC) is applied, to adaptively adjust the control input law in the sliding mode control. The combined form provides an adjustment of the control input law that effectively alleviates the chattering effects of the sliding mode control. Specifically, the online parameters computed from the parameter uncertainties and external disturbances help to improve the system robustness. The simulation results demonstrate that the proposed bio-inspired control strategy is very successful at tracking the given trajectories with less chattering as compared to both the conventional and fuzzy sling mode control schemes.

Performance Analysis of Various DC-DC Converters with Optimum Controllers for PV Applications`

Alternative vehicles to Internal Combustion Engines (ICE), for instance the electric vehicle is becoming popular. Electric Vehicles (EV) are pollution free and cost effective because the fossil fuel cost increases day by day. These factors make people passion for electric vehicles. Electrical energy demand necessitates charging of electric vehicles using renewable energy. Among the different renewable energy resources, Photovoltaic (PV) cells are suitable for EV. The PV output power capacity is still low, so efforts continue to develop the PV converter and its controller, aiming for higher power-extracting efficiency. The PV system requires a proper DC-DC converter with optimum controller to deliver its maximum power. This study analyses the various DC-DC converters such as buck, boost, cuk and modified cuk converters to find the solution for maximum efficiency. In this study in addition to converters, various Maximum Power Point Tracking (MPPT) methods, such as Perturb and Observe, Incremental Conductance along with a proposed algorithm called Brain Emotional Learning Based Intelligent Controller (BELBIC) has been analyzed. The operation of the BELBIC is based on the emotion processing mechanism in the brain. This intelligent control is stimulated by the limbic system of the mammalian brain. The performance analysis of the converters and MPPT methods are simulated using MATLAB/SIMULINK. Furthermore, experimental results are presented in order to validate the modified cuk converter with proposed BELBIC algorithm.

Emotional Learning Based Position Control of Pneumatic Actuators

This paper presents a new scheme for position tracking of pneumatic actuators. The controller is built upon the Brain Emotional Learning Based Intelligent Control (BELBIC) concept proposed by Caro Lucas [Lucas, C., Shahmirzadi, D., & Sheikholeslami, N. (2004). Introducing BELBIC: Brain emotional learning based intelligent controller. International Journal of Intelligent Automation and Soft Computing, 10, 11–21]. First the structure of BELBIC is analyzed to further understand its features. Next, different types of emotional signals, required by BELBIC, are experimentally evaluated to meet the challenges in position tracking of pneumatic actuators. The best performing BELBIC structure is then experimentally compared with a previously developed robust proportional integral controller. It is also successfully applied to a force reflecting tele-operated application of pneumatic actuator.

A New Intelligent Approach to Patient-cooperative Control of Rehabilitation Robots

This paper presents a new intelligent method to control rehabilitation robots by mainly considering reactions of patient instead of doing a repetitive preprogrammed movement. It generates a general reference trajectory based on different reactions of patient during therapy. Three main reactions has been identified and included in reference trajectory: small variations, force shocks in a single moment and variable level of participation. This reference trajectory can facilitate every kind of patient-cooperative controller of rehabilitation with an evaluation factor. A simple planar 2-DOF exoskeleton is used as rehabilitation robot for leg therapy. The low level controller used here is Brain Emotional Learning Based Intelligent Controller (BELBIC) which is participating to develop an admittance control scheme. Performance of this controller has compared to PID with simulations and better results have observed specially in control signal and speed of reaction when controller encounters a force shock.

Real-Time Implementation of Brain Emotional Learning Developed for Digital Signal Processor-Based Interior Permanent Magnet Synchronous Motor Drive Systems

In this study, a brain emotional learning-based intelligent controller (BELBIC) is developed for the speed control of an interior permanent magnet synchronous motor (IPMSM). A novel and simple model of the IPMSM drive structure is established with the intelligent control system, which controls motor speed accurately without the use of any conventional PI controllers and is independent of motor parameters. This study is conducted in both real time and simulation with a new control plant for a laboratory 3 ph, 3.8 Nm IPMSM digital signal processor (DSP)-based drive system. This DSP-based drive system is then compared with conventional BELBIC and an optimized conventional PI controller. Results show that the proposed method performs better than the other controllers and exhibits excellent control characteristics, such as fast response, simple implementation, and robustness with respect to disturbances and manufacturing imperfections.

High performance emotional intelligent controller for induction motor speed control

This paper presents a real-time implementation of an improved emotional controller for induction motor (IM) drives. We have improved the design of Brain Emotional Learning Based Intelligent Controller (BELBIC) by using the fuzzy technique in calculating sensory input. The proposed controller is called Brain Emotional Learning and Fuzzy Based Intelligent Controller (BELFBIC) For the first time, Brain Emotional Learning and Fuzzy Based Intelligent Controller is used for Space Vector Pulse Width Modulation inverter fed induction motor V/f speed control. V/f control is simple and relatively easy to implement. It provides motor performance, which is adequate in most applications. A comparative analysis with PID controller and Fuzzy controller is also carried out. The simulation is carried out by Matlab / Simulink. The Proposed controller is successfully implemented in real time using a Digital Signal Controller (dsPIC30F2010) based three-phase 0.5 HP laboratory squirrel-cage IM.

BELBIC을 이용한 Rotary Inverted Pendulum 제어

This study performs erection of a pendulum hanging at a free end of an arm by rotating the arm to the upright position. A mathematical model of a rotary inverted pendulum system (RIPS) is derived. A brain emotional learning based intelligent controller (BELBIC) is designed and used as a controller for swinging up and balancing the pendulum of the RIPS. In simulations performed in the study, a pendulum is initially inclined at 45° with respect to the upright position. A simulation is also performed for evaluating the adaptiveness of the designed BELBIC in the case of system variation. In addition, a simulation is performed for evaluating the robustness of the designed BELBIC against a disturbance in the control input.

Designing an Emotional Intelligent Controller for IPFC to Improve the Transient Stability Based on Energy Function

The controllability and stability of power systems can be increased by Flexible AC Transmission Devices (FACTs). One of the FACTs devices is Interline Power-Flow Controller (IPFC) by which the voltage stability, dynamic stability and transient stability of power systems can be improved. In the present paper, the convenient operation and control of IPFC for transient stability improvement are considered. Considering that the system's Lyapunov energy function is a relevant tool to study the stability affair. IPFC energy function optimization has been used in order to access the maximum of transient stability margin. In order to control IPFC, a Brain Emotional Learning Based Intelligent Controller (BELBIC) and PI controller have been used. The utilization of the new controller is based on the emotion-processing mechanism in the brain and is essentially an action selection, which is based on sensory inputs and emotional cues. This intelligent control is based on the limbic system of the mammalian brain. Simulation confirms the ability of BELBIC controller compared with conventional PI controller. The designing results have been studied by the simulation of a single-machine system with infinite bus (SMIB) and another standard 9-buses system (Anderson and Fouad, 1977).

Moving mass control system in conjunction with brain emotional learning-based intelligent control for rate regulation of suborbital reentry payloads

An adaptive intelligent control strategy based on a brain emotional learning model is investigated in the application of the rate regulation of suborbital reentry payloads. Because of nonlinear time-varying dynamics of these payloads, choosing an appropriate control mechanism and stability strategy can be an engineering challenge. Thus in a new approach, a moving mass control system in conjunction with brain emotional learning-based intelligent control is used to fulfill payload de-tumbling. The contribution of brain emotional learning-based intelligent control in handling the nonlinear time-varying dynamics is shown by comparison with results obtained from a linear proportional–integral controller. The results demonstrate excellent performance of brain emotional learning-based intelligent control in learning dynamic couplings and improvement of behavior without any considerable control system complexity.

Brain emotional learning based intelligent controller for stepper motor trajectory tracking

Excellent attributes of permanent magnet stepper motor (PMSM) make it prominent in robotic, aerospace, and numerical machine applications. However, the problem of nonlinearity and presence of mechanical configuration changes, particularly in precision reference trajectory tracking, must be put into perspective. In this paper, a novel cognitive strategy based on the emotional learning in limbic system of mammalian’s brain is employed to establish an intelligent controller in order to provide the necessary control actions as to achieve trajectory tracking of the rotor speed in different circumstances. Brain emotional learning based intelligent controller (BELBIC) is a model free controller, independent of model dynamic and variations that occurs in system, can be taken in to account as an outstanding option for the nonlinear applications. Fast response, high accuracy, and the ability of disturbance rejection introduce BELBIC as an eminent controller. To verify these attributes, different test beds have been simulated in Matlab Simulink environment and the performance of BELBIC is investigated. For further illumination, a classic controller called static proportional-integral-derivative (PID) is also applied on the model and then a comprehensive comparison, both in certain and uncertain condition, between the results of the proposed controllers is done. Uncertain situation is provided by applying load torque disturbance and variation in parameters of PMSM. The results of simulations clearly indicate the outstanding ability of BELBIC in speed tracking with high accuracy for the arbitrary reference signals and conspicuous robustness of this controller in presence of uncertainties. Key words: Permanent magnet stepper motor, limbic system, emotional learning, speed tracking, uncertainty, robustness.

Applying Brain Emotional Learning Based Intelligent Controller (Belbic) to Multiple‐Area Power Systems

AbstractEvolution of efficient power system control is very important. An effective power system simulation is useful for development as an evaluation of control performance. In this paper, a new, efficient simulation of multiple‐area power system control is proposed. We present the application of a Brain Emotional Learning Based Intelligent Controller (BELBIC) to regulate the frequency error for a two‐area interconnected power system. BELBIC is based on the emotional learning process in the Amygdala‐Orbitofrontal system of the mammalian brain. Simulation results of this controller and the PID controller for a two‐area power system in a matlab/simulink environment show that it develops the stability control performance and improves amplitude of oscillations and settling time up to 17% and 24%, respectively. Actually, the simulation shows that the proposed BELBIC model for the matlab/simulink environment works and gives acceptable results, without redesigning it for each separate system.

Designing BELBIC to Stabilize Gyro Chaotic System

One of the most significantphenomenon in some systems is chaos; so control chaotic system is troublesomeand shows the abilities of control methods. Brain emotional learning based intelligent controller (BELBIC) is an intelligent controller based on the model of emotional part of brain. In this article, BELBIC is applied to stabilize Gyro chaotic system. The contribution of BELBIC in improving the control system performance is shown by in comparison with results obtained from backstepping controller. BELBIC stabilizes Gyro Chaos with Shorter Setting Time and Lower Control Signal than backstepping controller.

Position Control of Hybrid Stepper Motor Using Brain Emotional Controller

In order to control the position of hybrid stepper motor and improve its performance, direct torque control strategy is adopted. The main idea of this paper is to present the implementation of an emotional controller for position control of hybrid stepper motor drive. The proposed controller is called Brain Emotional Learning Based Intelligent Controller (BELBIC). This controller is a computational model of emotional processing mechanism in the brain. The effectiveness of the proposed BELBIC controller-based hybrid stepper motor drive is verified by simulation results.

Identification and real-time position control of a servo-hydraulic rotary actuator by means of a neurobiologically motivated algorithm

This paper presents a new intelligent approach for adaptive control of a nonlinear dynamic system. A modified version of the brain emotional learning based intelligent controller (BELBIC), a bio-inspired algorithm based upon a computational model of emotional learning which occurs in the amygdala, is utilized for position controlling a real laboratorial rotary electro-hydraulic servo (EHS) system. EHS systems are known to be nonlinear and non-smooth due to many factors such as leakage, friction, hysteresis, null shift, saturation, dead zone, and especially fluid flow expression through the servo valve. The large value of these factors can easily influence the control performance in the presence of a poor design. In this paper, a mathematical model of the EHS system is derived, and then the parameters of the model are identified using the recursive least squares method. In the next step, a BELBIC is designed based on this dynamic model and utilized to control the real laboratorial EHS system. To prove the effectiveness of the modified BELBIC’s online learning ability in reducing the overall tracking error, results have been compared to those obtained from an optimal PID controller, an auto-tuned fuzzy PI controller (ATFPIC), and a neural network predictive controller (NNPC) under similar circumstances. The results demonstrate not only excellent improvement in control action, but also less energy consumption.

Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm

Bounded rationally idea, rather that optimization idea, have result and better performance in decision making theory. Bounded rationality is the idea in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. The emotional theory is an important topic presented in this field. The new methods in the direction of purposeful forecasting issues, which are based on cognitive limitations, are presented in this study. The presented algorithms in this study are emphasizes to rectify the learning the peak points, to increase the forecasting accuracy, to decrease the computational time and comply the multi-object forecasting in the algorithms. The structure of the proposed algorithms is based on approximation of its current estimate according to previously learned estimates. The short term traffic flow forecasting is a real benchmark that has been studied in this area. Traffic flow is a good measure of traffic activity. The time-series data used for fitting the proposed models are obtained from a two lane street I-494 in Minnesota City, USA. The research discuss the strong points of new method based on neurofuzzy and limbic system structure such as Locally Linear Neurofuzzy network (LLNF) and Brain Emotional Learning Based Intelligent Controller (BELBIC) models against classical and other intelligent methods such as Radial Basis Function (RBF), Takagi–Sugeno (T–S) neurofuzzy, and Multi-Layer Perceptron (MLP), and the effect of noise on the performance of the models is also considered. Finally, findings confirmed the significance of structural brain modeling beyond the classical artificial neural networks.

Progress in Artificial Intelligence Techniques: from Brain to Emotion

Artificial Intelligence (AI) techniques, e.g. expert system (ES), fuzzy logic (FL), artificial neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO) and biologically inspired (BI) have recently been applied widely in power electronics and motor drives.Each AI method has its own uniqueness and characteristics. Recently, researchers have developed a computational model of emotional learning in mammalian brain, namely brain emotional learning based intelligent controller (BELBIC). The results indicate the ability of BELBIC to control unknown non-linear dynamic systems. Therefore, the BELBIC can be easily adopted for niche mechatronics and industrial applications.

A comparative study of various intelligent based controllers for speed control of IPMSM drives in the field-weakening region

Precise speed control of an Interior Permanent Magnet Synchronous Motor (IPMSM) drive becomes a complex issue due to the nonlinear nature of its developed torque. The system nonlinearity becomes severe when the IPMSM drive operates in the field weakening region. In order to achieve perfect control characteristics, the main purpose of this paper is to present a detailed comparison of various intelligent based controllers for flux weakening speed control of an IPMSM drive. In this paper, the Brain Emotional Learning Based Intelligent Controller (BELBIC), Genetic-Fuzzy Logic Based Controller (GFLBC), as well as genetic-PI based controller, are considered. BELBIC is a computational model of emotional processing mechanism in the brain. The effectiveness of the proposed BELBIC controller-based IPMSM drive is verified by simulation results at different operating conditions. Moreover, control regimes such as Maximum Torque Per Ampere (MTPA) control and flux weakening (FW) control as well as voltage and current constraints have been successfully applied. The results prove BELBIC’s perfect control characteristics, such as fast and smooth speed response, low maximum starting current, adaptability to speed and load changes and robustness to parameter variations, disturbance and sudden one-phase interruption.

Verification of intelligent control of a launch vehicle with HILS

The reliability of an intelligent self tuning controller called the brain emotional learning based intelligent controller (BELBIC) to attitude control of a nonlinear launch vehicle (LV) simulation with hardware-in-the loop simulation (HILS) is studied. To set up the HIL system of the LV a six-degree of freedom simulation of the LV and a hydraulic actuator, which was used for the pitch channel thrust vector control (TVC) actuator of the LV, is performed. The results of the BELBIC controller with a fuzzy controller (FC) and a PID controller in this HILS of the LV to control the pitch channel of the LV have been compared.