Recurrent Fuzzy Cerebellar Model Articulation Research Articles

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

Using a Hybrid of Interval Type-2 RFCMAC and Bilateral Filter for Satellite Image Dehazing

With increasing advancement of science and technology, remote sensing satellite imaging does not only monitor the Earth’s surface environment instantly and accurately but also helps to prevent destruction from inevitable disasters. The changing weather, e.g., cloudiness or haze formed from atmospheric suspended particles, results in low contrast satellite images, and partial information about Earth’s surface is lost. Therefore, this study proposes an effective dehazing method for one single satellite image, aiming to enhance the image contrast and filter out the region covered with haze, so as to reveal the lost information. First, the initial transmission map of the image is estimated using an Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller (IT2RFCMAC) model. For the halo and color oversaturation resulted from the processing procedure, a bilateral filter and quadratic function nonlinear conversion are used in turn to refine the initial transmission map. For the estimation of atmospheric light, the first 1% brightest region is used as the color vector of atmospheric light. Finally, the refined transmission map and atmospheric light are used as the parameters for reconstructing the image. The experimental results show that the proposed satellite image dehazing method has good performance in the visibility detail and color contrast of the reconstructed image. In order to further validate the effectiveness of the proposed method, visual assessment and quantitative evaluation were implemented, respectively, and compared with the methods proposed by relevant scholars. The visual assessment and quantitative evaluation analysis demonstrated good results of the proposed approach.

Using Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller Based on Improved Differential Evolution for Cooperative Carrying Control of Mobile Robots

Using Interval Type-2 Recurrent Fuzzy Cerebellar Model Articulation Controller Based on Improved Differential Evolution for Cooperative Carrying Control of Mobile Robots

An efficient recurrent fuzzy CMAC model based on a dynamic-group–based hybrid evolutionary algorithm for identification and prediction applications

This article presents an efficient TSK-type recurrent fuzzy cerebellar model articulation controller (T-RFCMAC) model based on a dynamic-group--based hybrid evolutionary algorithm (DGHEA) for solving identification and prediction problems. The proposed T-RFCMAC model is based on the traditional CMAC model and the Takagi--Sugeno--Kang (TSK) parametric fuzzy inference system. Otherwise, the recurrent network, which imports feedback links with a receptive field cell, is embedded in the T-RFCMAC model, and the feedback units are used as memory elements. The DGHEA, which is a hybrid of the dynamic-group quantum particle swarm optimization (QPSO) and the Nelder--Mead method, is proposed for adjusting the parameters of the T-RFCMAC model. In DGHEA, an entropy-based grouping technique is adopted to improve the searching capability and the convergent speed of quantum particles swarm optimization. Experimental results show that the proposed DGHEA-based T-RFCMAC model is more effective at identification and prediction than other models.

Recurrent Fuzzy Cerebellar Model Articulation Neural Network Based Power Control of a Single-Stage Three-Phase Grid-Connected Photovoltaic System During Grid Faults

A recurrent fuzzy cerebellar model articulation neural network (RFCMANN)-based controller to regulate the active and reactive power of a single-stage three-phase grid-connected photovoltaic (PV) system during grid faults is proposed in this study. Since the rapid growth of the amount of the PV systems in recent year has greatly affected the stability of the power system, the grid codes demand the grid-connected PV systems to have the low-voltage ride through (LVRT) capability to help sustaining the stability of power system especially during the grid faults. To satisfy the LVRT requirements and ensure the injected current within the safety value, the active and reactive power commands are calculated by using the current profile of the LVRT grid requirements and the current limit of the inverter. Moreover, the proposed RFCMANN controller uses the signed distance and input space repartition mechanisms to convert the dual-input variables to sole-input variable and repartition the input space to an appropriate quantity. Therefore, the structure and computation complexities of the proposed RFCMANN controller are effectively reduced and make it more practical. Furthermore, varied learning-rate coefficients are designed to guarantee the convergence of the tracking error for the proposed RFCMANN controller.

Mobile robot navigation control using recurrent fuzzy cerebellar model articulation controller based on improved dynamic artificial bee colony

In this study, an efficient navigation control method of mobile robot is proposed. The proposed navigation control method consists of behavior manager, toward goal behavior, and wall-following behavior. According to the relative position between the mobile robot and the environment, the behavior manager switches to determine toward goal behavior or wall-following behavior of mobile robot. A novel recurrent fuzzy cerebellar model articulation controller based on an improved dynamic artificial bee colony is proposed for performing wall-following control of mobile robot. The proposed improved dynamic artificial bee colony algorithm uses the sharing mechanism and the dynamic identity update to improve the performance of optimization. A reinforcement learning method is adopted to train the wall-following control of mobile robot. Experimental results show that the proposed method obtains a better navigation control than other methods in unknown environment.

Transmission map estimation of weather-degraded images using a hybrid of recurrent fuzzy cerebellar model articulation controller and weighted strategy

This study proposes a hybrid of a recurrent fuzzy cerebellar model articulation controller (RFCMAC) and a weighted strategy for solving single-image visibility in a degraded image. The proposed RFCMAC model is used to estimate the transmission map. The average value of the brightest 1% in a hazy image is calculated for atmospheric light estimation. A new adaptive weighted estimation is then used to refine the transmission map and remove the halo artifact from the sharp edges. Experimental results show that the proposed method has better dehazing capability compared to state-of-the-art techniques and is suitable for real-world applications.

Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) fault-tolerant control of a six-phase permanent magnet synchronous motor (PMSM) position servo drive is proposed in this study. First, the fault detection and operating decision method of the six-phase PMSM position servo drive is developed. Then, an ideal computed torque controller is designed for the tracking of the rotor position reference command. In general, it is impossible to design an ideal computed control law owing to the uncertainties of the six-phase PMSM position servo drive, which are difficult to know in advance for practical applications. Therefore, the RFNCMAN, which combined the merits of a recurrent fuzzy cerebellar model articulation network and a recurrent fuzzy neural network, is proposed to estimate a nonlinear equation included in the ideal computed control law with a robust compensator designed to compensate the minimum reconstructed error. Furthermore, the adaptive learning algorithm for the online training of the RFNCMAN is derived using the Lyapunov stability to guarantee the closed-loop stability. Finally, the proposed RFNCMAN fault-tolerant control system is implemented in a 32-bit floating-point DSP. The effectiveness of the six-phase PMSM position servo drive using the proposed intelligent fault-tolerant control system is verified by some experimental results.

A Novel Interactively Recurrent Self-Evolving Fuzzy CMAC and Its Classification Applications

In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

Intelligent position control of permanent magnet synchronous motor using recurrent fuzzy neural cerebellar model articulation network

A recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) control is proposed in this paper for position servo drive systems to track various periodical position references with robustness. The adopted position servo drive system is designed using a six-phase PMSM and equipped with a fault-tolerant control scheme. First, an ideal computed torque controller is designed for the tracking of the rotor position reference command. Since the uncertainties of the PMSM position servo drive system are difficult to know in advance, it is impossible to design an ideal computed control law for practical applications. Therefore, the RFNCMAN is proposed to mimic the ideal computed torque controller with a compensated controller to compensate the approximation error. In the RFNCMAN, a recurrent fuzzy cerebellar model articulation network (RFCMAN) is adopted in the first dimension to enhance the online learning rate and localisation learning capability. Moreover, a general recurrent fuzzy neural network (RFNN) is adopted in the second dimension to enhance the generalisation performance and to reduce the required memory and rule numbers. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP. The effectiveness of the proposed RFNCMAN control system is verified by some experimental results.

Reactive Power Control of Single-Stage Three-Phase Photovoltaic System during Grid Faults Using Recurrent Fuzzy Cerebellar Model Articulation Neural Network

This study presents a new active and reactive power control scheme for a single-stage three-phase grid-connected photovoltaic (PV) system during grid faults. The presented PV system utilizes a single-stage three-phase current-controlled voltage-source inverter to achieve the maximum power point tracking (MPPT) control of the PV panel with the function of low voltage ride through (LVRT). Moreover, a formula based on positive sequence voltage for evaluating the percentage of voltage sag is derived to determine the ratio of the injected reactive current to satisfy the LVRT regulations. To reduce the risk of overcurrent during LVRT operation, a current limit is predefined for the injection of reactive current. Furthermore, the control of active and reactive power is designed using a two-dimensional recurrent fuzzy cerebellar model articulation neural network (2D-RFCMANN). In addition, the online learning laws of 2D-RFCMANN are derived according to gradient descent method with varied learning-rate coefficients for network parameters to assure the convergence of the tracking error. Finally, some experimental tests are realized to validate the effectiveness of the proposed control scheme.

Development of a Recurrent Fuzzy CMAC With Adjustable Input Space Quantization and Self-Tuning Learning Rate for Control of a Dual-Axis Piezoelectric Actuated Micromotion Stage

This paper proposes a recurrent fuzzy cerebellar model articulation controller (RFCMAC) for a dual-axis micromotion stage powered by piezoelectric actuators. The proposed RFCMAC employs an adjustable input space quantization method with a learning rate capable of self-tuning. The proposed RFCMAC is aimed at dealing with the adverse effects due to unknown hysteresis nonlinearity, modeling uncertainty, and external disturbance that are commonly found in the piezoelectric actuator systems. The proposed RFCMAC mainly focuses on overcoming the drawbacks of the conventional CMAC control scheme when used to control a piezoelectric actuator system. In particular, the adjustable input space quantization method uses a simple repartitioning decision function to determine an appropriate input space quantization. In addition, the self-tuning law for the learning rate of the proposed RFCMAC is derived based on the discrete-time Lyapunov function so that convergence and faster learning can be guaranteed. Finally, in order to validate the effectiveness of the proposed control methodology, several contour-following experiments were conducted on a dual-axis piezoelectric actuated micromotion stage. Experimental results show that the proposed control scheme is feasible, and its performance is superior to that of the conventional CMAC control scheme.

A new pseudo-Gaussian-based recurrent fuzzy CMAC model for dynamic systems processing

This article presents a new pseudo-Gaussian-based recurrent fuzzy cerebellar model articulation controller (PG-RFCMAC) model for identifying various nonlinear dynamic systems. A pseudo-Gaussian basis function can provide the self-organising PG-RFCMAC model, which own a higher flexibility and can approach the optimise result more accurately. The pseudo-Gaussian basis function is used to model the hypercube cells and the fuzzy weights. The recurrent network is embedded in the PG-RFCMAC model by adding feedback connections with a receptive field cell, where the feedback units act as memory elements. An on-line learning algorithm is proposed for the automatic construction of the proposed model during the learning procedure. Computer simulations were conducted to illustrate the performance and applicability of the proposed model.