Brain Emotional Learning Controller Research Articles

Control Schemes for Quadrotor UAV: Taxonomy and Survey

Quadrotor Unmanned Aerial Vehicle (UAV) is an unstable system, so it needs to be controlled efficiently and intelligently. Moreover, due to its non-linear, coupled, and under-actuated nature, the quadrotor has become an important research platform to study and validate various control theories. Different control approaches have been used to control the quadrotor UAV. In this context, a comprehensive study of different control schemes is presented in this research. First, an overview of the working and different applications of quadrotor UAVs is presented. Second, a mathematical model of the quadrotor is discussed. Later, the experimental results of various existing control techniques are discussed and compared. The various control schemes discussed and described for quadrotors are; Proportional Integral and Derivative (PID), Linear Quadratic Regulator (LQR), H-infinity ( H ∞ ), Sliding Mode Control (SMC), Feedback Linearization (FBL), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN), Iterative Learning Control (ILC), Reinforcement Learning Control (RLC), Brain Emotional Learning Control (BELC), Memory Based Control (MBC), Nested Saturation Control (NSC), and Hybrid Controllers (HC). Comparison is done among all the control techniques and it is concluded that the hybrid control method gives improved results. This survey presents a broad overview of the state-of-the-art in UAV design, control, and implementation for real-life applications.

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.

Fault-Tolerant Control for Robotic Systems Using a Wavelet Type-2 Fuzzy Brain Emotional Learning Controller and a TOPSIS-Based Self-organizing Algorithm

Fault-Tolerant Control for Robotic Systems Using a Wavelet Type-2 Fuzzy Brain Emotional Learning Controller and a TOPSIS-Based Self-organizing Algorithm

Wavelet Interval Type-2 Takagi-Kang-Sugeno Hybrid Controller for Time-Series Prediction and Chaotic Synchronization

This paper presents a new hybrid neural network for time series prediction and chaotic synchronization. The proposed controller consists of a wavelet interval type-2 Takagi Sugeno Kang fuzzy brain emotional learning controller (WIT2TFBELC), a wavelet interval type-2 Takagi Sugeno Kang fuzzy cerebellar model articulation controller (WIT2TFCMAC), and a robust compensator (RB). The main controller combines the WIT2TFBELC and the WIT2TFCMAC. A TSK fuzzy system is used to create a hybrid structure with the WIT2TFBELC and the WIT2TFCMAC. The TSK fuzzy system can effectively adjust the weighting for the main controller to achieve the time series prediction and chaotic prediction with better tracking response. Morever a robust compensator is used to achieve robust ability of the system. A Lyapunov function was used to establish the adaptive laws and effectively adjust the system parameters online. Finally, two examples of the application of the proposed algorithm are presented to point out the performance of proposed method.

A New Self-Organizing Double Function-Link Brain Emotional Learning Controller for MIMO Nonlinear Systems Using Sliding Surface

This paper aims to propose a new type of neural network which is the self-organizing double function-link brain emotional learning controller (SO-DFL-BELC) for multiple input multiple output (MIMO) nonlinear systems. The proposed controller is a newly designed neural network containing the key mechanism of a typical brain emotional learning controller (BELC), which is a mathematical model that approximates the judgmental and emotional activity of a brain, in which it is combined with some additional functions and methods. Firstly, a double function-link (DFL) network is applied to expand the function for a BELC to improve the accuracy of the system weights. Secondly, the self-organizing mechanism is utilized to increase or decrease the number of neurons that possibly supports the main controller to adapt to the sharp change of the input and to reduce the computation time significantly. Thirdly, the learning rules of the SO-DFL-BELC are developed based on the gradient descent algorithm and sliding surface. Finally, all parameters of the system can be optimized. The proposed SO-DFL-BELC is applied to control two different MIMO nonlinear systems that are a 4D chaotic system and a four-tank system. The simulation results show the favorable control performance of the proposed control algorithm.

Synchronization of Chaotic System Using a Brain-Imitated Neural Network Controller and Its Applications for Secure Communications

This paper proposes a new hybrid algorithm for secure communication applications. The proposed algorithm includes a fuzzy brain emotional learning controller (FBELC), a recurrent cerebellar model articulation controller (RCMAC), and a robust compensator (RC). The main brain-imitated neural network controller is a combination of the RCMAC and the FBELC, which is a mathematical model that approximates the decision and emotional activity of a human brain. A fuzzy inference system is also merged into the FBELC to produce an efficient hybrid structure, then it is used for secure communication applications. The 3-dimensional (3D) Genesio chaotic system is used for audio and image secure communication systems to show the potency and performance of the proposed algorithm. In the first application, a new image encryption algorithm is proposed to enhance security for information transmission, then several standard images are applied for the chaotic synchronization of image secure communication. In the second application, the audio signal is embedded in a 3D chaotic trajectory, which is used as an encryption carrier signal, after using the proposed method for the decryption, the source signal can be retrieved. The comparisons of simulation results using security analyses and root mean square error for recent algorithms are performed to validate the performance and efficiency of the proposed hybrid algorithm. The simulation results point out that our algorithm can attain better synchronization performance, and achieve more efficient audio and image secure communications.

Интеллектуальный робастный регулятор на технологиях когнитивных вычислений. Ч. 1: Модели когнитивного управления с эмоциональным обучением мозга

n on-linecontrol and decision-making systems, emotional brain training is a preferred methodology (compared to stochastic gradient-based and evolutionary algorithms) due to its low computational complexity and fast robust learning. To describe the emotional learning of the brain, a mathematical model was created —the brain emotional learning controller (BELC). The design of intelligent systems based on emotional signals basedoncontrol methods assoft computing technologies: artificial neural networks, fuzzy control and genetic algorithms. Based on the simulated mathematical model of mammals BEL, a controller architecture has been developed. Applied approachcalled “Brain Emotional Learning Based Intelligent Controller” (BELBIC) —a neurobiologically motivated intelligent controller based on a computational model of emotional learning in the mammalian limbic system. The article describes applied models of intelligent regulators based on emotional learning of the brain. BELBIC's learning capabilities;versatility and low computational complexity make it a very promising toolkitfor on-lineapplications.

H∞ tracking control for nonlinear multivariable systems using wavelet-type TSK fuzzy brain emotional learning with particle swarm optimization

This paper studies the H∞ tracking control for uncertain nonlinear multivariable systems. We propose a control strategy, which combines the adaptive wavelet-type Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning controller (WTFBELC) and the H∞ robust tracking compensator. As for the adaptive WTFBELC, it is a main controller designed to mimic the ideal controller. The proposed WTFBELC is to obtain much better ability of handling nonlinearities and uncertainties, but the proposed H∞ robust tracking compensator is to compensate the residual error between the adaptive WTFBELC and the ideal controller. Furthermore, the optimal learning rates of the adaptive WTFBELC are searched quickly by using the particle swarm optimization (PSO) algorithm, and the parameter updated laws are derived based on the steepest descent gradient method. The robust tracking performance of this novel control scheme is guaranteed based on Lyapunov stability theory. The mass-spring-damper mechanical system and the three-link robot manipulator, are used to verify the effectiveness of the proposed adaptive PSO-WTFBELC H∞ control scheme.

Self-Organizing Adaptive Fuzzy Brain Emotional Learning Control for Nonlinear Systems

This paper aims to develop a more efficient adaptive control system for uncertain nonlinear systems. A novel self-organizing fuzzy brain emotional learning controller (FBELC) is proposed. The FBELC neural network is the mathematical replica of human brain emotions incorporated with fuzzy inference rules, which mimics the judgments and emotions of the brain. The FBELC contains two sub-neural networks, namely a sensory neural network and an emotional neural network. These duet networks influence each other, thus improving the learning ability of the system. The proposed control system also merges sliding mode control to simplify the input space dimension of FBELC. In this study, the self-organization algorithm for the structure of FBELC is developed; thus, the growing and pruning of input layers of FBELC can be automatically proceeded to give the most efficient network structure. Moreover, the parameter adaptive law is derived using gradient descent method to achieve effective learning ability of the network. The proposed control system comprises the self-organizing adaptive FBELC (SOAFC) used as the main controller and a fuzzy compensator designed to obtain robust stability of the system for handling uncertain nonlinear systems. The developed SOAFC is then applied to a double inverted pendulum system and a biped robot to illustrate its effectiveness.

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems.

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control.

Wavelet Fuzzy Brain Emotional Learning Control System Design for MIMO Uncertain Nonlinear Systems.

This paper aims to present a novel efficient scheme in order to more effectively control the multiple input and multiple output (MIMO) uncertain nonlinear systems. A wavelet fuzzy brain emotional learning controller (WFBELC) model is proposed, which is comprises the benefit of wavelet function, fuzzy theory and brain emotional neural network. When it is used as the main tracking controller for a MIMO uncertain nonlinear systems, the performances of the system, such as the approximation ability, the learning performance and the convergence rate, will be effectively improved. Meanwhile, the gradient descent method is used to adjust the parameters online of WFBELC and the Lyapunov function is employed to guarantee the rapid convergence of the control systems. For the sake of the further illustrating the superiority of this model, two examples of uncertain nonlinear systems, a Duffing-Holmes chaotic system and a Chua's chaotic circuit, are studied. After compared with other models, the test results show that the proposed model can be applied to obtain more satisfactory control performance and be more suitable to deal with the influence of the uncertainty of the MIMO nonlinear systems.

Self-evolving type-2 fuzzy brain emotional learning control design for chaotic systems using PSO

This work presents a design of interval type-2 fuzzy brain emotional learning control (T2FBELC) combining with the self-evolving algorithm to help the network to automatically achieve the optimum construction from the empty initial rule. In the control system design, the T2FBELC is the main controller used to mimic an ideal controller, and a robust controller is a compensator for the compensation of the residual error. Implementing the steepest descent gradient approach, the parameter adaptive laws of the proposed system are derived. Besides, the particle swarm optimization (PSO) is applied to find the optimal learning rates for the parameter adaptive laws. The stability of the proposed algorithm is guaranteed using the Lyapunov function. Finally, the effectiveness of the proposed control system is verified by numerical simulations of the chaotic systems.

Self-Organizing Brain Emotional Learning Controller Network for Intelligent Control System of Mobile Robots

The trajectory tracking ability of mobile robots suffers from uncertain disturbances. This paper proposes an adaptive control system consisting of a new type of self-organizing neural network controller for mobile robot control. The newly designed neural network contains the key mechanisms of a typical brain emotional learning controller network and a self-organizing radial basis function network. In this system, the input values are delivered to a sensory channel and an emotional channel, and the two channels interact with each other to generate the final outputs of the proposed network. The proposed network possesses the ability of online generation and elimination of fuzzy rules to achieve an optimal neural structure. The parameters of the proposed network are online tunable by the brain emotional learning rules and gradient descent method; in addition, the stability analysis theory is used to guarantee the convergence of the proposed controller. In the experimentation, a simulated mobile robot was applied to verify the feasibility and effectiveness of the proposed control system. The comparative study using the cutting-edge neural network-based control systems confirms that the proposed network is capable of producing better control performances with high computational efficiency.

Fuzzy Brain Emotional Learning Control System Design for Nonlinear Systems

This paper aims to develop a new fuzzy neural network by incorporating a brain emotional learning controller (BELC) with the fuzzy inference rules. The brain has an orbitofrontal cortex and an amygdala. The BELC is a mathematical model, which imitates judgment and emotion of a brain. So that BELC contains two neural networks; the former is a sensory neural network and the latter is an emotional neural network. These two systems affect each other and this will effectively improve the approximation ability of BELC. By incorporating fuzzy inference system into the BELC, a novel fuzzy brain emotional learning controller (FBELC) is proposed which includes two fuzzy inference systems. The parameter updating rules have an interacting term between two systems, which will increase the learning performance of this system. Then, the developed FBELC is applied to control nonlinear systems. Two examples, a chaotic system and an inverted double pendulum system, are demonstrated to illustrate the effectiveness of the proposed control method. A comparison between the proposed FBELC with other controllers shows that the proposed controller can achieve better control performance than the other controllers.