Design Of Neuro-fuzzy Controller Research Articles

Multiobjective Neuro-Fuzzy Controller Design and Selection of Filter Parameters of UPQC Using Predator Prey Firefly and Enhanced Harmony Search Optimization

This research introduces a unified power quality conditioner (UPQC) that integrates solar photovoltaic (PV) system and battery energy systems (SBES) to address power quality (PQ) issues. The reference signals for voltage source converters of UPQC are produced by the Levenberg–Marquardt back propagation (LMBP) trained artificial neural network control (ANNC). This method removes the necessity for conventional dq0, abc complex shifting. Moreover, the optimal choice of parameters for the adaptive neuro-fuzzy inference system (ANFIS) was achieved through the integration of the enhanced harmony search algorithm (EHSA) and the predator-prey-based firefly algorithm (PPFA) in the form of the hybrid metaheuristic algorithm (PPF-EHSA). In addition, the algorithm is employed to optimize the selection of resistance and inductance values for the filters in UPQC. The primary objective of the ANNC with predator-prey-based firefly algorithm and enhanced harmony search algorithm (PPF-EHSA) is to enhance the stability of the DC-link capacitor voltage (DLCV) with reduced settling time amid changes in load, solar irradiation (G), and temperature (T). Moreover, the algorithm seeks to achieve a reduction in total harmonic distortion (THD) and enhance power factor (PF). The method also focuses on mitigating fluctuations such as swell, harmonics, and sag and also unbalances at the grid voltage. The proposed approach is examined through four distinct cases involving various permutations of loads and sun irradiation (G). However, in order to demonstrate the performance of the suggested approach, a comparison is conducted with the ant colony and genetic algorithms, i.e., (ACA) (GA), as well as the standard methods of synchronous reference frame (SRF) and instantaneous active and reactive power theory (p-q). The results clearly demonstrate that the proposed method exhibits a reduced mean square error (MSE) of 0.02107 and a lower total harmonic distortion (THD) of 2.06% compared to alternative methods.

INVERSE NEURO-FUZZY MODEL BASED CONTROLLER DESIGN FOR A PH NEUTRALIZATION PROCESS

Since pH neutralization processes have extremely nonlinear characteristics, controlling it might be difficult. Therefore, a special controller design is needed to handle the high nonlinearities of the process. In this study, an inverse neuro-fuzzy model-based controller (NFMBC) design is presented for control of a pH neutralization process (NP). Input-output (IO) data set of the process is collected by applying a proper excitation signal. Then, forward and inverse neuro-fuzzy models of the process are constructed by using this data set after a training process. In terms of design simplicity, a two-input-one-output model structure is chosen for both neuro-fuzzy models. These forward and inverse neuro-fuzzy models are used in a nonlinear internal model control (NIMC) structure in order to provide robustness against disturbances and model mismatches. To examine the proposed controller's performance, simulation studies are carried out under setpoint variation and disturbance conditions. Additionally, the performance of the inverse NFMBC is compared to that of a fuzzy proportional integral derivative (FPID) controller with a 7x7 rule base. The results demonstrate that the designed controller provides more effective control performance for setpoint variations and also exhibits higher robustness against disturbances in the acid flow rate than the FPID controller.

A Novel Design of Interval Type-2 Neuro-Fuzzy Controller for Flexible Structure

The aim of this research is to develop a novel design of interval type-2 neuro-fuzzy (IT2NF) controller for active vibration control of a flexible structure during an earthquake. For this purpose, two adaptive neural network based fuzzy logic controllers are designed and combined to create the novel design of an IT2NF controller to reduce the vibrations of two-storey flexible building model that occur during earthquake disturbance effects. Accordingly, dynamic modeling of a flexible structure is realized and simulated using the MATLAB / SimMechanics. Then, an experimental setup consisting of two-storey flexible structure, active mass damper (AMD) and shaker is established. Additionally, IT2NF controller is implemented in simulation and experimental models, and the effectiveness and performance of the IT2NF controller are tested under the scaled Northridge Earthquake acceleration. The obtained simulations and experimental responses are evaluated in terms of cart displacements, deflections, and accelerations of the flexible floors showing a good agreement between the simulations and the experimental results. The results show that the designed novel IT2NF controller reduced the total deflections of first and second floor by 72.3% and 68.7%, respectively, when compared with the uncontrolled system. Additionally, it is also found that the designed IT2NF controller is able to reduce the accelerations of the first and second floor by 64.8% and 54.6%, respectively. The proposed and designed control method reported in this study can be employed as an active vibration controller for multi-degree of freedom of flexible systems under the disturbances such as earthquake excitations.

Adaptive Fuzzy Leader–Follower Synchronization of Constrained Heterogeneous Multiagent Systems

This article considers the distributed adaptive neuro-fuzzy output feedback control protocol design to solve the output synchronization problem for heterogeneous multiagent systems with nonlinear strict-feedback agent dynamics. The output constraints and actuator saturation are considered simultaneously. First, a distributed high-gain observer is employed to estimate the unmeasured agent state and relax the requirement of the Lipschitz continuity of nonlinear follower dynamics. Second, an asymmetric barrier Lyapunov function with time-varying constraint is presented to deal with both the transient and the steady-state constraints on the output synchronization error. To avoid the “explosion of complexity,” the dynamic surface control technique is employed to filter the virtual control signal for each follower. To deal with the actuator saturation, a distributed auxiliary dynamical system is designed for each follower. The fuzzy logic system is employed to compensate for the uncertain follower dynamics with guaranteed semiglobal uniformly ultimately boundedness of all closed-loop signals. Finally, a simulation example is conducted to verify the efficacy of the presented adaptive neuro-fuzzy controller design.

Design and performance analysis of adaptive neuro-fuzzy controller for speed control of permanent magnet synchronous motor drive

This article has been focused on the design of the artificial neural network with fuzzy inference system (ANFIS) for the speed control of permanent magnet synchronous motor (PMSM). PMSM is widely used in industrial applications such as robotic manipulators and machine tools due to the high efficiency, high torque to weight ratio and smaller size. One of the efficient control strategies of PMSM is based on ANFIS. ANFIS is very popular technique to deal with uncertainties. System dynamics in such cases can be compared with combining the proportional–integral–derivative (PID) with the Sliding Mode Controller (SMC). Simulations have been performed in MATLAB to validate the performance of the proposed model, and comparisons are made with ANFIS, SMC–PID and PID controllers compared to other controllers reported in the benchmark of the proposed controller’s efficiency. The proposed adaptive neuro-fuzzy-dependent results indicate good transient efficiency. Robustness against the robustness of adaptive neuro-fuzzy-based PID and SMC–PID controllers is satisfactory in terms of easy settling time, zero peaks overflow and zero steady state error. The simulation results have been implemented in MATLAB 2019b, and experimental results are implemented in BD63030.

Design and Implementation of Adaptive Neuro-fuzzy Exergame Controller

Introduction: Due to sedentary postures caused by video games, many health-related issues have occurred among players. One practical solution for dealing with the aforementioned problem is to come up with game controllers which promote physical exercises. In this study, an adaptive neuro-fuzzy exergame controller was introduced. Materials and Methods: During the training stage, the parameters of the fuzzy logic’s member functions were fine-tuned. By calculating a gradient vector and by applying backpropagation, the aforementioned parameters were updated using the measured error. The controller was made of four pads, each containing a resistor and a pushbutton, which were connected to a microcontroller. In order to improve the user experience, an adaptive neuro-fuzzy logic system was used to analyze the gathered data from the controller. Results: A pure fuzzy logic system (FLS) cannot provide an acceptable playing experience for players of different ages and physical characteristics. The received signal from the controller was sent to a fine-tuned FLS. The calculated output of the previously trained FLS was one of the defined classes of “ignore”, “press”, and “hold”, which was sent as a command to the main computer. Conclusion: In the proposed method, the FLS was fine-tuned by gathering data from the user, which improved the performance of the controller due to the fact that the controller was trained to best suit the needs of the user. The gathered data was then used to change the parameters of the FLS to provide an acceptable playing experience for the user.

Adaptive Command Filtered Neuro-Fuzzy Control Design for Fractional-Order Nonlinear Systems With Unknown Control Directions and Input Quantization

This article studies the adaptive backstepping control problem for a class of fractional-order (FO) nonlinear systems subject to input quantization and unknown control directions by combining with an indirect FO Lyapunov stability method, and a command filter-based FO dynamic surface control (FODSC) technique. First, a modified FODSC method is utilized to reduce the computational complexity existing in the conventional recursive procedure in which an FO command filter is designed to obtain the command signals and their FO derivatives. Furthermore, the Nussbaum function and neuro-fuzzy networks (NFNs) are adopted to deal with the problem of the unknown control directions and unknown nonlinear functions existing in the system. Moreover, by introducing the compensation and prediction mechanism to controller design, the adaptive controllers, and adaptive laws are constructed to ensure that all the signals of the controlled systems are bounded. Finally, two examples are given to show the validity of the developed control method.

Design of Neuro-Fuzzy Controller for Stabilization of Single Phase Stand-Alone PV Power System

For the three phase power electronic and drive applications, vector control or the synchronous reference frame (SRF) based control concept is well accepted and settled amongst the research communities. Although the SRF concept has gained popularity and appreciation in developing the three phase controllers, still the concept has not reached the same level in case of a single phase system. The work presented in this paper is mainly concerned to the design of a hybrid Artificial Neural Network and Fuzzy Logic based controller for a single phase stand-alone photo-voltaic (PV) power system. The adaptive neuro fuzzy inference system (ANFIS) controller proposed in this paper is chiefly meant for improving the transient and steady state responses; for minimizing the distorting effect of the low order load current harmonics encountered particularly in case of switching the drive based inductive loads and to help maintain the inverter output voltage constant under different loading circumstances. The result obtained through simulation work, shows the effectiveness of the proposed controller as compared with the previously established research works.

Adaptive critic-based quaternion neuro-fuzzy controller design with application to chaos control

Neuro-fuzzy control structures despite all of the advantages from both neural networks features and fuzzy inference engines always get in trouble due to a large number of fuzzy rules which is because of the high order of the system or the large number of divisions considered for each input. In this paper, a new adaptive neuro-fuzzy controller is proposed based on the quaternion numbers, and thus the mentioned problem of large rule numbers is solved by using the quaternion back propagation concept. Furthermore, utilizing reinforcement learning which assesses output value produced by a critic is another strength of the proposed method. Finally, in order to show the superiority and effectiveness of the proposed controller in comparison with conventional neuro-fuzzy ones, a complex and challenging chaos control problem which is a chaotic spinning disk control is provided.

NeuroFuzzy Controller Design and FPGA-Based Embedded System for ACROBOT Model

NeuroFuzzy Controller Design and FPGA-Based Embedded System for ACROBOT Model

Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System

Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System

Design and Implementation of Neuro-Fuzzy Controller Using FPGA for Sun Tracking System

Nowadays, renewable energy is being used increasingly because of the global warming and destruction of the environment. Therefore, the studies are concentrating on gain of maximum power from this energy such as the solar energy. A sun tracker is device which rotates a photovoltaic (PV) panel to the sun to get the maximum power. Disturbances which are originated by passing the clouds are one of great challenges in design of the controller in addition to the losses power due to energy consumption in the motors and lifetime limitation of the sun tracker. In this paper, the neuro-fuzzy controller has been designed and implemented using Field Programmable Gate Array (FPGA) board for dual axis sun tracker based on optical sensors to orient the PV panel by two linear actuators. The experimental results reveal that proposed controller is more robust than fuzzy logic controller and proportional-integral (PI) controller since it has been trained offline using Matlab tool box to overcome those disturbances. The proposed controller can track the sun trajectory effectively, where the experimental results reveal that dual axis sun tracker power can collect 50.6% more daily power than fixed angle panel. Whilst one axis sun tracker power can collect 39.4 % more daily power than fixed angle panel. Hence, dual axis sun tracker can collect 8 % more daily power than one axis sun tracker.

Neuro-Fuzzy Controller Design to Navigate Unmanned Vehicle with Construction of Traffic Rules to Avoid Obstacles

To navigate an autonomous unmanned vehicle (UV) in an obstacle based trajectory, the fuzzy-neural network is very essential. In this paper, we designed a fuzzy inference system to guide an unmanned vehicle for maintaining traffic rules to reach its goal with avoiding obstacles. The fuzzy inference system which we designed, generates fuzzy if then rules and defines a nonlinear mapping of input data. The input data are initial position, velocity of vehicle and whether there is any obstacle. Input data are simulated and trained by hybrid algorithm. The result controls the navigation of UV and also minimizes error performance to reach the goal.

Design of Neuro-Fuzzy Controller for Control of Water Distribution in an Irrigation Main Canal

This paper propose a neuro-fuzzy Smith predictor controller (NFSP) for effective control of water distribution in an irrigation main canal, which allows to improve the efficiency and to reduce the water losses. The dynamic model of this process is obtained from an identification procedure based in real-time data. A neuro-fuzzy controller combined with a Smith predictor is therefore designed which behaves robustly against the effect of external disturbance and plant dynamical parameters variations. Simulated results compare the performance of the proposed controller with a standard PI controller, also combined with a conventional Smith predictor for several operation conditions. These results show that the proposed controller outperforms the standard controller in terms of performance and robustness.

The Design of Neuro-Fuzzy Controller for Active Suspension System

A neuro-fuzzy control (NFC) system is developed to control the suspension system of vehicle due to its nonlinearity and parameter variations. A neural network (NN) is used to adjust the premise parameters and the consequent parameters in fuzzy logic control (FLC). Simulation results by using NFC are compared with those of the conventional PID controller and passive suspension system. Based on the simulation, it can be concluded that the neuro-fuzzy controller shows a good performance in both passenger comfort and vehicle handing in comparison to the conventional PID controller and passive suspension system.

ANFIS based Neuro-Fuzzy Controller in LFC of Wind-Micro Hydro-Diesel Hybrid Power System

paper presents the design and analysis of Neuro-Fuzzy controller based on Adaptive Neuro-Fuzzy Inference System (ANFIS) architecture for Load frequency control of an isolated wind-micro hydro-diesel hybrid power system, to regulate the frequency deviation and power deviations. Due to the sudden load changes and intermittent wind power, large frequency fluctuation problem can occur. This newly developed control strategy combines the advantage of neural networks and fuzzy inference system and has simple structure that is easy to implement. So, in order to keep system performance near its optimum, it is desirable to track the operating conditions and use updated parameters to control the system. Simulations of the proposed ANFIS based Neuro- Fuzzy controller in an isolated wind-micro hydro-diesel hybrid power system with different load disturbances are performed. Also, a conventional proportional Integral (PI) controller and a fuzzy logic (FL) controller were designed separately to control the same hybrid power system for the performance comparison. The performance of the proposed controller is verified from simulations and comparisons. Simulation results show that the performance of the proposed ANFIS based Neuro-Fuzzy Controller damps out the frequency deviation and attains the steady state value with less settling time. The proposed ANFIS based Neuro-Fuzzy controller provides best control performance over a wide range of operating conditions.

Design of Neuro-Fuzzy Controller for a Rotary Dryer

Drying is a very complex non-linear process including the movement of solids in addition to the thermal drying. This means that both the modeling and control of a rotary dryer is difficult with conventional methods, and most of previous researches goes to directly linearize the non-linear model, estimating many parameters that affect the model or work on specific linear model so applying modern controllers even fuzzy or Neuro-Fuzzy always gives approximate results and valid only in certain operating points, in this paper we try to make a combination between two dynamic models of dryer plant to solve the drying rate equation which mainly causes the non-linearity of the model. This article will validate the proposed model then applying modern adaptive control techniques Fuzzy and Neuro-Fuzzy controllers, comparing the behavior of the plant dryer when connected to each of this controllers versus the conventional PID , the aim is to improve dryer control, this will lead to energy, cost savings and increasing efficiency of the drying, the behavior of the control systems has been tested with simulations based on the model of a plant dryer using The Matlab®, and Simulink and Fuzzy Logic Toolbox, ANFIS Toolbox.

Neuro-fuzzy control design of processes in chemical technologies

Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and laboratory mixing process.

Double chains quantum genetic algorithm with application to neuro-fuzzy controller design

This paper proposes a double chains quantum genetic algorithm (DCQGA), and shows its application in designing neuro-fuzzy controller. In this algorithm, the chromosomes are composed of qubits whose probability amplitudes comprise gene chains. The quantum chromosomes are evolved by quantum rotation gates, and mutated by quantum non-gates. For the direction of rotation angle of quantum rotation gates, a simple determining method is proposed. The magnitude of rotation angle is computed by integrating the gradient of the fitness function. Furthermore, a normalized neuro-fuzzy controller (NNFC) is constructed and designed automatically by the proposed algorithm. Application of the DCQGA-designed NNFC to real-time control of an inverted pendulum system is discussed. Experimental results demonstrate that the designed NNFC has very satisfactory performance.

Neuro-Fuzzy Controller for Rudder Roll Stabilization of Ships

The uncertainty of ship models and interferences can lead to reduce the effectiveness of conventional roll stabilization controller depending upon accurate mathematical model.This paper proposes the design of neuro-fuzzy controller for rudder roll stabilization. Learning ability of neural network is utilized to optimize the fuzzy controller. Hybrid learning rule which is a combination of backpropagation algorithm and least square estimator is utilized to realized parameter adjustment of fuzzy control rule and membership function in order to improve the adaptive capacity of controller. The simulation tests under various situations such as different navigational speed, sea conditions, wave-to-course angle and ship parameter perturbation are to check the performance of roll stabilizatin controller. Simulation results show that the neuro-fuzzy controller has better robustness and effectiveness for rudder roll stabilization in beam seas.