Brain Emotional Learning Based Intelligent Controller Research Articles

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

Adaptive control of a class of uncertain nonlinear systems using brain emotional learning and Legendre polynomials

In this paper, an adaptive controller for a class of uncertain nonlinear systems is presented using a combination of Legendre polynomials and brain emotional learning-based intelligent controller (BELBIC). Recently, some versions of BELBIC have been presented with the aim of satisfying the universal approximation property using Gaussian basis function. However, the size of regressor vector is too large that imposes a heavy computational load to the processor. The novelty of this paper is presenting a new version of BELBIC with less computational burden using Legendre polynomials. Moreover, there are very few tuning parameters in Legendre polynomials. Another contribution of this paper is editing the stability analysis presented in recent related works. Due to the intrinsic non-differentiability of the adaptation rules of BELBIC, the second time derivative of Lyapunov function is undefined and thus, the Barbalat’s lemma cannot be applied to verify the asymptotic convergence of the error function. Therefore, bounded-input-bounded-output (BIBO) stability can only be claimed for this controller. Simulation results on different case studies show that Legendre polynomials can improve the universal approximation property of BELBIC with less tuning parameters. Moreover, in the absence of the robust control term in the control law, the performance Legendre polynomials will not deteriorate, while the performance degrade in Gaussian basis function is quite considerable.

Microvibration Control of Hybrid Platform for High-Tech Equipment in Buildings Using Brain Emotional Learning-Based Intelligent Controller

In the semiconductor industry, the vulnerability of high-tech facilities installed on platforms to ground excitations induced by nearby traffic is significantly pronounced, primarily due to their small-scale dimensions. Consequently, it is imperative to design a smart control technique by effectively utilizing model-free controllers. Recently, adaptive intelligent control algorithms have emerged as a viable alternative to conventional model-based control algorithms. To address this issue, this study meticulously designed a hybrid platform using the adaptive intelligent controller known as the brain emotional learning-based intelligent controller, along with a Sugano fuzzy inference system to effectively optimize the controller’s learning parameters. These learning and intelligent-based algorithms offer notable advantages, including the ability to handle nonlinearity, uncertainty, and training capabilities within the control systems. To assess the effectiveness of the proposed controller in mitigating vibrations induced by traffic on high-tech facilities, a three degree-of-freedom structure is employed along with the hybrid platform. Finally, the performance of the hybrid platform in terms of microvibration control levels is meticulously validated using the Bolt Beranek Newman vibration criteria. Simulation results unequivocally demonstrate that the proposed controller outperforms both an uncontrolled system and a traditional linear quadratic regulator controller in terms of reducing the traffic-induced response of the hybrid platform and second floor, respectively. Through the integration of learning and intelligent-based controllers, the velocity levels of both the hybrid platform and the second floor are reduced to approximately 49.02% and remain well within the acceptable standard criteria curves.

A novel optimization control of gas turbine based on a hybrid method using the BELBIC and adaptive multi input multi output feedback control

In recent years, the use of Gas Turbines (GTs) to generate electricity has grown exponentially. Therefore, for the optimal performance of gas power plants, a lot of research has been done on modeling different parts of GTs, estimating model parameters, and controlling them. But most of the available methods are not accurate enough, like most linear methods, or are model-based, which require an accurate model of the system (like most nonlinear methods), or there is a constant need to adjust the controller parameters. To address these shortcomings, this study uses a new hybrid method including the brain emotional learning-based intelligent controller, the nonlinear multivariate method in the form of feedback linearization, and an adaptive control method of mode predictive reference model used to quickly control the GT. The Rowen model is used to simulate the nonlinear model of the GT. Owing to the influence of exhaust temperature on the speed of GT and the multivariate system model, nonlinear multivariate controller design is considered. First, the adaptive control method of the state-predictive reference model for a multi-output multi-input system, in general, is presented, and then, the proposed method for a GT with real dynamic values is implemented. The simulation results show the ability of the proposed controller to control the GT. In order to prove the efficiency of the proposed method, the obtained results are compared with the PID industrial controller method and the classical reference model method.

Intelligent Secondary Control of Islanded AC Microgrids: A Brain Emotional Learning-Based Approach

This paper proposes a distributed intelligent secondary control (SC) approach based on brain emotional learning-based intelligent controller (BELBIC) for power electronic-based ac microgrid (MG). The BELBIC controller is able to learn quick-auto and handle model complexity, non-linearity, and uncertainty of the MG. The proposed controller is fully model-free, indicating that the voltage amplitude and frequency deviations are regulated without previous knowledge of the system model and parameters. This approach ensures low steady-state variations with higher bandwidth and maintains accurate power-sharing of the droop mechanism. Furthermore, primary control is realized with a robust finite control set-model predictive control (FCS-MPC) in the inner level to increase the system frequency bandwidth and a droop control in the outer level to regulate the power-sharing among the distributed generations. Finally, experimental tests obtained from a hardware-in-the-loop testbed validate the proposed control strategy for different cases.

Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)

There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model.

A real-test and simulation combined platform for developing intelligent tracking control of multirotors

PurposeThis study aims to minimize the risk of costly failures of flight tests during the path tracking control design, and a noble approach has been proposed in this study to put the whole vehicle-in-the simulation loop. Working with the real system is essential for developing intelligent and data-driven controllers for multirotor drones which needs learning the drones' nonlinear complicated dynamics. The vehicle-in-the-loop (VIL) platform developed in this paper is a safe and effective solution to deal with this problem.Design/methodology/approachTo avoid risky flight test during controller design, the multirotor is hinged to a shaft that allows the multirotor's angular motion but restricts translational motion. The test-bed includes the real system attitude dynamics and the simulation of the position dynamics to model the complete flight based on real-time reactions of the vehicle. For the authors' case study, a hexacopter angular motion provides the real-time attitude data in translational motion simulation loop. To test the set-up, a proportional-integral-derivative (PID) and a brain emotional learning-based intelligent controller (BELBIC) is implemented for tracking of circle and 8-shape flight trajectories.FindingsThe results show that the platform helps the intelligent controller to learn the system dynamics without worrying about the failure in the early stages of the design and in the real-world flight test. Although the hexacopter translational dynamics is modeled in simulation, the authors still have highly accurate attitude dynamics matching the requirement of the control loop design. The comparison of the two controllers also shows that the performance of BELBIC is better than PID in this test.Originality/valueThe research background is reviewed in the introduction section. The other sections are originally developed in this paper.

Analysis of a Hybrid Wind/Photovoltaic Energy System Controlled by Brain Emotional Learning-Based Intelligent Controller

Recently, hybrid wind/PV microgrids have gained great attention all over the world. It has the merits of being environmentally friendly, reliable, sustainable, and efficient compared to its counterparts. Though there has been great development in this issue, the control and energy management of these systems still face challenges. The source of those challenges is the intermittent nature of both wind and PV energy. On the other hand, a new intelligent control technique called Brain Emotional Learning-Based Intelligent Controller (BELBIC) has garnered more interest. This paper proposes the control and energy management of hybrid wind/PV microgrids using a BELBIC controller. To design the system, simple power and energy analyses were proposed. The proposed microgrid was modeled and simulated using MATLAB. The responses of the energy system were tested under two different types of disturbances, namely step and ramp disturbances. These disturbances are applied to the wind speed, the irradiation level of the PV, and the load power. The results indicate that the AC load voltage and frequency are steady with negligible transients against the previous disturbance. In addition, the performance is better than that of the classical PI controller. Also, energy management acts perfectly to compensate for the intermittence and disturbances of the wind and PV energies. On the other hand, the system robustness against model parameters uncertainties in the microgrid parameters are studied.

Deep brain emotional learning-based intelligent controller applied to an inverted pendulum system

Deep brain emotional learning-based intelligent controller applied to an inverted pendulum system

Emotional Learning based adaptive control algorithm for Semi-Active seismic control of structures

A tuned mass damper (TMD) is a passive mass-spring system, widely accepted as an effective response control strategy for a structure under external excitation. The inherent passive nature, optimum tuning requirements of TMDs are known to lead to sensitivity problems and/or detuning of the TMDs for changing vibration conditions, limiting their performance to narrow band of input excitation. Due to these limitations, semi-active control concepts are used to introduce adaptability in passive TMDs. Semi-active response control using TMD is much researched in recent years for protection of civil engineering structures against earthquake. In this study, the efficacy of a semi-active mass driver (MD) system coupled with magnetorheological (MR) damper is investigated for seismic response control of a primary structure. The input current to the MR damper in the semi-active MR-MD is controlled using an adaptive Brain Emotional Learning Based Intelligent Controller (BELBIC) control algorithm in a closed loop architecture. The efficiency of the damping addition from the MR damper to the mass driver which is reducing the response of the primary structure is investigated using analytical and simulation studies, considering off-tuning, emulated using three different tuning cases of the MR-MD. From the studies, it is found that semi-active MR-MD is performing satisfactorily in reducing the seismic response of the primary structure and BELBIC algorithm performs efficiently even during off-tuning conditions and presence of physical constraints.

Semi-active vibration control of building structure by Self Tuned Brain Emotional Learning Based Intelligent Controller

Control algorithms are the most crucial aspects in effective control of civil structures exposed to earthquake forces. Recently, adaptive intelligent control algorithms are evolving to be a viable substitute strategy for conventional model-based control algorithms. One of the most recent developments, known as the Brain Emotional Learning Based Intelligent Controller (BELBIC), has caught the attention of scientists as a model-free adaptive control system. It possesses appealing capabilities for dealing with nonlinearities and uncertainties in control frameworks. The modern semi-actively controlled civil structures have a highly uncertain and nonlinear nature following severe disturbances. As a result, these structures require real-time (online) robust control actions towards changing conditions, which the controllers with rigid settings cannot adapt. This study intends to overcome this issue in two ways: an online self-tuning brain emotional learning-based intelligent controller (ST-BELBIC) is formulated. Then its capabilities in improving the performance of cascaded controller in attenuating seismic vibrations of a three-story scaled building structure are validated. In this case, the central control unit BELBIC is based on sensory inputs (SI) and emotional cues (reward) signals. The main contribution of the proposed controller is a self-attuned version of the standard BELBIC that uses the benefits of a first-order Sugano fuzzy inference system (FIS) to adapt its parameters online. The proposed control methodology can be a promising model-free controller in terms of online tuning, simplicity of configuration, ease of applicability, less operational time, and neutralizing nonlinearities. The simulation affirms that the proposed controller compared with conventional LQR and intelligent Fuzzy tuned PID (FT-PID) controllers shows a superior performance regarding attenuating seismic responses of the building and can also improve the performance of cascaded FT-PID controller.

Research developments in adaptive intelligent vibration control of smart civil structures

Control algorithms are the most critical aspects in the successful control of civil structures subjected to earthquake and wind forces. In recent years, adaptive intelligent control algorithms are emerging as an acceptable substitute method to conventional model-based control algorithms. These algorithms mainly work on the principles of artificial intelligence (AI) and soft computing (SC) methods that make them highly efficient in controlling highly nonlinear, time-varying, and time-delayed complex civil structures. The current research probes to control algorithms, that this article set forth an inclusive state-of-the-art review of adaptive intelligent control (AIC) algorithms for vibration control of smart civil structures. First, a general introduction to adaptive intelligent control is presented along with its advantages over conventional control algorithms. Second, their classification concerning artificial intelligence and soft computing methods is provided that mainly consists of artificial neural network-based controller, brain emotional learning-based intelligent controller, replicator dynamics-based controller, multi-agent system-based controller, support vector machine-based controller, fuzzy logic control, adaptive neuro-fuzzy inference system-based controller, adaptive filters-base controller, and meta-heuristic algorithms-based hybrid controllers. Third, a brief review of these algorithms with their developments on the theory and applications is provided. Fourth, we demonstrate a summarized overview of the cited literature with a brief trend analysis is presented. Finally, this study presents an overview of these innovative AIC methods that can demonstrate future directions. The contribution of this article is the anticipation of detailed and in-depth discussion into the perspective of AI and SC-based AIC method advances that enabled practical applications in attenuating vibration response of smart civil structures. Moreover, the review demonstrates the computing advantages of AIC over conventional controllers that are important in creating the next generation of smart civil structures.

Brain Emotional Learning and Adaptive Model Predictive Controller for Induction Motor Drive: A New Cascaded Vector Control Topology.

With the development of high-speed microprocessors, it is now possible to implement mathematically complex vector control algorithms without compromising on the performance of motor drive. Among vector control techniques space vector proportional-integral (PI), direct-torque control (DTC), field-oriented control (FOC), model-predictive control (MPC) are being widely used in industries. But their limitations have urged researchers to develop more advance techniques. In this paper, a new technique learning and adaptive model - based predictive control (termed as LAMPC) is proposed for the vector control of three phase induction motor. In the proposed method, the dynamic model of induction motor is updated adaptively based on prediction (receding horizon principle) for the inner control loop (current control) while the brain emotional learning-based intelligent controller (BELIC) is used for the outer control loop (speed control). The proposed methodology offers desired dynamic response, precise tracking, good disturbance handling capability along with satisfactory steady-state performance. To show the effectiveness of the proposed approach, benchmark simulation results for various inputs are presented using MATLAB/Simulink. Finally, the detailed qualitative and quantitative comparison of the proposed LAMPC is made with the most relevant vector techniques to show its significance.

Design of Novel BELBIC Controlled Semi-Active Suspension and Comparative Analysis with Passive and PID Controlled Suspension

Passenger comfort, quality of ride, and handling have broughta lot of attention and concern toautomotive design engineers. These 2 parameters must have optimum balance as they have an inverse effect on each other. Researchers have proposed several approaches and techniques like PID control, fuzzy approach, GA, techniques with inspiration from nature and hybrid techniques to attain the same. A new controller based on the learning behavior of the human brain has been used for the control of semi-active suspension in this study. The controller is known as the Brain Emotional Learning-Based Intelligent Controller (BELBIC). A one-fourth model of car along with the driver model having 6 degrees of freedom (DOF) wasmodeled and simulated. The objective of the studywasto analyze the performance of the proposed controller for improving the dynamic response of the vehicle model coupled with complex biodynamic models of the human body as a passenger, making the whole dynamic system very complex to control. The performance wasanalyzed based on percentage reduction in the overshoot of the vehicle’s sprung mass as well as different human body parts when subjected to road disturbances. The proposed controller performance wascompared with the PID controller, widely used in semi-active suspension. The simulation results obtained for BELBIC controlled system for circular road bump showed that the overshoot of passenger head and body wasreduced by 18.84 and 18.82 %, respectively and reduction for buttock and leg displacement was18.87 %. The vehicle’s seat and sprung mass displacement displayedan improvement of 18.90 and 18.51 %. The overshoot of passenger's head and body displacement wasimproved by 19.79and 19.62 %,respectively, whereas improvement for buttock & leg, vehicle’s seat, and sprung mass displacement were19.81, 20.00, and 20.49 % against trapezoidal speed bump. The PID controlled suspension disclosed an improvement of 8.74, 8.53, 8.75, 11.11, 14.75 % against circular bump and 10.72, 10.33, 10.73, 11.11 and 11.75 % against trapezoidal bump for overshoot reduction of passenger head, body, buttock & leg, vehicle’s seat and sprung mass displacement, respectively. The proposed BELBIC controlled semi-active suspension outperformed the widely used PID controlled semi-active suspension and indicated asignificant improvement in the ride quality of the vehicle.

Intelligent robust pitch control of wind turbine using brain emotional learning

his article proposes an implementation of the brain emotional learning‐based intelligent controller (BELBIC) for high‐precision and robust pitch control of a 5‐MW wind turbine. The proposed model‐free controller is a biologically inspired method emulating the learning in the mammalian's limbic system and it is independent of the model dynamics and variations that might occur in a system. The auto‐learning capability of the BELBIC allows accommodating the nonlinearities associated with the wind turbine model and provides a reasonable degree of disturbance enabling precise and robust tracking of the pitch angle, even under unforeseen wind conditions. To investigate the trajectory tracking performance and robustness of the BELBIC in various unpredictable wind conditions, multiple uncertain wind speed conditions including gust and random wind, are simulated in MATLAB/Simulink. The results of simulations are compared with two benchmark control methods, fuzzy‐proportional‐integral‐derivative and gain‐scheduling proportional‐integral. The simulation results clearly indicate that the BELBIC serves better performance and robustness while guaranteeing quick and precise pitch angle response as well as its ability in dealing with nonlinearity and unforeseen wind conditions in comparison to the other two benchmark control methods.

Secure communication based on chaos synchronization using brain emotional learning

This paper presents a secure communication system based on chaos synchronization using brain emotional learning-based intelligent controller (BELBIC). Uncertainties, including parametric uncertainties and external disturbances are approximated using emotional learning. The obtained estimation is used in the controller or observer to apply appropriate input to the receiver to synchronize the chaotic signals of transmitter and receiver. The proposed system can effectively reduce the synchronization error. In comparison with other intelligent systems such as neuro-fuzzy systems, brain emotional learning has a more straightforward structure and consequently its tuning is simpler. Stability analysis using Lyapunov direct method guarantees that internal signals are bounded and the synchronization error converges to zero. Moreover, for secure communication and cryptography, an observer using emotional learning is developed. Simulation results verify the satisfactory performance of the proposed synchronization controller and observer.

A biologically-inspired reinforcement learning based intelligent distributed flocking control for Multi-Agent Systems in presence of uncertain system and dynamic environment

In this paper, we investigate the real-time flocking control of Multi-Agent Systems (MAS) in the presence of system uncertainties and dynamic environment. To handle the impacts from system uncertainties and dynamic environment, a novel reinforcement learning technique, which is appropriate for real-time implementation, has been integrated with multi-agent flocking control in this paper. The Brain Emotional Learning Based Intelligent Controller (BELBIC) is a biologically-inspired reinforcement learning-based controller relying on a computational model of emotional learning in the mammalian limbic system. The learning capabilities, multi-objective properties, and low computational complexity of BELBIC make it a very promising learning technique for implementation in real-time applications. Firstly, a novel brain emotional learning-based flocking control structure is proposed. Then, the real-time update laws are developed to tune the emotional signals based on real-time operational data. It is important to note that this data-driven reinforcement learning approach relaxes the requirement for system dynamics and effectively handle the uncertain impacts of the environment. Using the tuned emotional signals, the optimal flocking control can be obtained. The Lyapunov analysis has been used to prove the convergence of the proposed design. The effectiveness of the proposed design is also demonstrated through numerical and experimental results based on the coordination of multiple Unmanned Aerial Vehicles (UAVs).

Stability analysis of brain emotional intelligent controller with application to electrically driven robot manipulators

This study discusses stability analysis of the brain emotional learning-based intelligent controller (BELBIC) for model-free tracking control of a class of uncertain non-linear systems. Due to the unique adaptation laws for the controller gains, stability analysis of these controllers is a challenging task. This issue constructs the motivation of this study to undertake this task. Similar to direct adaptive fuzzy control, the BELBIC plays the role of a direct intelligent controller. Thus, the stability of the BELBIC can be guaranteed based on the stability analysis of direct adaptive fuzzy control. The main objective of this study is that the stability of the BELBIC in tracking control of a class of uncertain non-linear systems is guaranteed. Based on the proposed stability analysis and Barbalat's lemma, BELBIC can yield in the asymptotic convergence of the tracking error to zero. In comparison with the existing works in the literature, the proposed approach has fewer tuning parameters. Therefore, the developed approach has a simple structure. The simulation results on an articulated manipulator actuated by permanent magnet DC motors and comparison with other model-free approaches are presented to show the superiority of the proposed approach.

Design and analysis of novel bio inspired BELBIC and PSOBELBIC controlled semi active suspension

Passenger comfort, quality of ride and handling brings a lot of attention and concern of the automotive design engineers. Researchers are working and have introduced approaches like artificial neural network, genetic algorithm, fuzzy and hybrid control techniques have been proposed, modelled and analysed to check their impact on the above mentioned parameters. A new controller based on learning mode of human brain known as brain emotional learning based intelligent controller (BELBIC) and particle swarm optimised brain emotional learning based intelligent controller (PSOBELBIC) optimised by particle swarm optimisation (PSO) is introduced here. Six degrees of freedom (DOF) mathematical model of quarter car along with passenger and four degree of freedom half car is modelled and simulated. The PSOBELBIC gives the best performance with overall improvement of about 26% and 32.54% for speed bumps and 52.79%, 34.58% against random road.

Robust precise trajectory tracking of hybrid stepper motor using adaptive critic-based neuro-fuzzy controller

In this paper, an adaptive critic-based neuro-fuzzy controller (ACNFC) is developed for robust and high-precision speed trajectory tracking of a hybrid stepper motor (HSM). The proposed model-free controller uses the critic-based learning and backpropagation of errors for adaptive tuning of the consequence part of the fuzzy inference rule. This makes the ACNFC reconfigurable and robust in high-precision tracking applications, such as robot-assisted surgery, involving with parametric uncertainties and environmental disturbances. To investigate the performance and robustness of the ACNFC, HSM system is simulated under various conditions in MATLAB/Simulink. These operating conditions consider mechanical parameter variations, load disturbance, noise impact, and sudden fault occurrence. To verify the effectiveness of the proposed controller, test results are compared with the results obtained by optimized-PI and brain emotional learning-based intelligent controllers. Simulation results confirm the effective performance of the ACNFC for adaptive and precise speed response as well as dealing with nonlinearity and uncertainty in realistic applications.