Adaptive Fuzzy Neural Controller Research Articles

Enhancing grid stability through predictive control and fuzzy neural networks in flywheel energy storage systems integration

Renewable energy systems, exemplified by solar and wind power, are increasingly integrated into modern power grids to mitigate environmental impact and reduce reliance on fossil fuels. However, the inherent intermittency and unpredictability of renewable energy sources pose challenges to grid stability and reliability. Energy Storage Systems (ESS) offer a promising solution to address these challenges by smoothing out power fluctuations and ensuring a consistent power supply. Among various ESS technologies, Flywheel Energy Storage Systems (FESS) have emerged as a noteworthy contender due to their rapid response times, low operating costs, and extended lifespan. This paper focuses on investigating the operation of a novel unit comprising a solar power system integrated with a Flywheel Energy Storage System (PV-FESS). The aim is to develop an effective control algorithm utilizing adaptive fuzzy neural networks and model predictive control (ANFIS-MPC) to manage power fluctuations stemming from renewable energy sources within the grid. The proposed control strategy aims to optimize the operation of the PV-FESS system by dynamically adjusting the energy absorption or release of the flywheel to maintain grid stability. Simulation studies conducted using Matlab-Simulink/Simcape software validate the efficacy of the ANFIS-MPC algorithm in mitigating abnormal fluctuations from renewable energy sources. The results demonstrate that the PV-FESS system effectively balances power fluctuations, ensuring a stable and reliable power output to the grid.

Event-Triggered Adaptive Fuzzy Neural Network Output Feedback Control for Constrained Stochastic Nonlinear Systems.

This article investigates the problem of command-filtered event-triggered adaptive fuzzy neural network (FNN) output feedback control for stochastic nonlinear systems (SNSs) with time-varying asymmetric constraints and input saturation. By constructing quartic asymmetric time-varying barrier Lyapunov functions (TVBLFs), all the state variables are not to transgress the prescribed dynamic constraints. The command-filtered backstepping method and the error compensation mechanism are combined to eliminate the issue of "computational explosion" and compensate the filtering errors. An FNN observer is developed to estimate the unmeasured states. The event-triggered mechanism is introduced to improve the efficiency in resource utilization. It is shown that the tracking error can converge to a small neighborhood of the origin, and all signals in the closed-loop systems are bounded. Finally, a physical example is used to verify the feasibility of the theoretical results.

A Novel Hyperchaotic Financial System with Sinusoidal Hyperbolic Nonlinearity: From Theoretical Analysis to Adaptive Neural Fuzzy Controller Method

Chaotic systems are known to be extremely sensitive to initial conditions, meaning small changes can have a significant impact on the outcomes. By analyzing the average profit margin in relation to chaotic dynamics, companies can conduct sensitivity analysis to assess the potential impact of various factors on their profitability. This analysis can help identify critical variables or scenarios that may significantly affect profit margins. In this article, we have proposed a hyperchaotic financial system with sinusoidal hyperbolic non-linear variables applied to the average profit margin. Furthermore, we have investigated the stability of the hyperchaotic financial dynamics model to provide information to companies to assess the consistency and reliability of their profitability. In addition, fundamental dynamic behavior like Lyapunov exponents, bifurcation analysis, coexisting attractors have been reported. Finally, a nonlinear feedback control approach is developed to train an adaptive neural fuzzy controller. The application of Lyapunov theory confirms that this nonlinear feedback controller can effectively minimize the synchronization error within a finite duration. The results from simulations establish the effectiveness of the proposed neural fuzzy controller architecture in controlling the synchronization of two hyperchaotic financial models. Additionally, the simulation includes a comparison between the performance of the nonlinear controller and the adaptive neural fuzzy controller.

Development of an Adaptive Fuzzy-Neural Controller for Temperature Control in a Brick Tunnel Kiln

This research focuses on developing an adaptive fuzzy-neural control system to manage and maintain temperature in brick tunnel kilns. The problem is how to optimize performance and energy consumption in the brick production process. A control algorithm using a combination of a fuzzy logic system and neural network is proposed to automatically adjust temperature parameters, optimize production efficiency, and reduce energy consumption. Simulation and experimental results demonstrate the outstanding performance of the presented system, with significant improvements in energy efficiency and product quality compared to traditional control methods. Moreover, the obtained results exhibit the potential for wide application of the adaptive neuro-fuzzy control method in academic study or industrial production processes.

Adaptive Fuzzy Neural Network Command Filtered Impedance Control of Constrained Robotic Manipulators With Disturbance Observer.

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

Adaptive Neural-Fuzzy controller design combined with LQR to control the position of gantry crane

As the world grows, the demand for transporting goods is increasing, the number of goods in factories and ports is increasing, to transport all these goods, cranes are indispensable. In fact, currently, crane rigs working in factories and ports operate with low stability, when working or the phenomenon of swaying of the load occurs, leading to inaccurate positioning, loss of safe transportation of goods. To overcome these shortcomings, the paper proposes the design of a neural-fuzzy adaptive controller combined with an LQR controller (ANFIS-LQR) to control the forklift's position in the shortest time to achieve the desired exact position. At the same time, we want to control the deflection angle of the load so that the vibration when working is minimal. To check and evaluate the quality and stability of the system; the proposed design controller is simulated on MATLAB/Simulink software in the case of changes in system parameters and noise affecting the gantry crane system. To evaluate the superiority of the paper compared with published works, the author compares ANFIS-LQR with other published control methods such as DE-PID, Fuzzy-PD, Fuzzy dual and Fuzzy sliding, the simulation results show that the neural-fuzzy adaptive controller combined with the proposed LQR controller works well t_xlvt=2.1s , t_xlgt=3.5s, 0max=0.3(rad).

Chaos synchronization for a class of uncertain chaotic supply chain and its control by ANFIS

In this paper, modelling of a three-level chaotic supply chain network. This model has the uncertainty of the retailer in the manufacturer. An adaptive neural fuzzy method has been proposed to synchronize the two chaotic supply chain networks. To train adaptive neural fuzzy controller, first, a nonlinear feedback control method is designed. Then, using Lyapanov theory, it is proved that the nonlinear feedback controller can reduce the synchronization error to zero in a finite time. The simulation results show that the proposed neural fuzzy controller architecture well controls the synchronization of the two chaotic supply chain networks. In the other part of the simulation, a comparison is made between the performance of the nonlinear controller and the adaptive neural fuzzy. Also, in the simulation results, the controller signal is depicted. This signal indicates that the cost of implementation in the real world is not high and is easily implemented.

An adaptive neural fuzzy virtual inertia control method for VSC-HVDC system

Compared to conventional DC transmission, Voltage source converter based high voltage direct current transmission (VSC-HVDC) has the advantages of independently controllable transmission power, no commutation failure, no reactive power compensation and low harmonic levels. However, VSC-HVDC transmission cannot provide effective frequency support and regulation for the system after grid connection. For this reason, an adaptive neural fuzzy virtual inertia control method is proposed to impose a control strategy on the converter at the receiving end of a VSC-HVDC transmission. The method uses the frequency change rate and the amount of change of frequency as constraints to dynamically adjust the virtual inertia and damping coefficients. It provides larger inertial support when the frequency fluctuation is large and smaller inertial support when the frequency fluctuation is small, thus adapting to different operating conditions. Finally, a hardware-in-the-loop simulation platform is built. The superiority and application prospect of the proposed strategy in frequency stability of the system are verified by comparing the proposed strategy with the traditional virtual control strategy.

Performance Enhancement of DVR Using Adaptive Neural Fuzzy and Extreme Learning Machine-Based Control Strategy

Performance Enhancement of DVR Using Adaptive Neural Fuzzy and Extreme Learning Machine-Based Control Strategy

Adaptive Fuzzy Neural Network Harmonic Control with a Super-Twisting Sliding Mode Approach

This paper designed an adaptive super-twisting sliding mode control (STSMC) scheme based on an output feedback fuzzy neural network (OFFNN) for an active power filter (APF), aiming at tracking compensation current quickly and precisely, and solving the harmonic current problem in the electrical grid. With the use of OFFNN approximator, the proposed controller has the characteristic of full regulation and high approximation accuracy, where the parameters of OFFNN can be adjusted to the optimal values adaptively, thereby increasing the versatility of the control method. Moreover, due to an added signal feedback loop, the controller can obtain more information to track the state variable faster and more correctly. Simulations studies are given to demonstrate the performance of the proposed controller in the harmonic suppression, and verify its better steady-state and dynamic performance.

Stability analysis of a phase-shifted full-bridge circuit for electric vehicles based on adaptive neural fuzzy PID control

In the paper, adaptive neural fuzzy (ANF) PID control is applied on the stability analysis of phase-shifted full-bridge (PSFB) zero-voltage switch (ZVS) circuit, which is used in battery chargers of electric vehicles. At first, the small-signal mathematical model of the circuit is constructed. Then, by fuzzing the parameters of PID, a closed-loop system of the small-signal mathematical model is established. Further, after training samples collected from the fuzzy PID system by adaptive neural algorithm, an ANF PID controller is utilized to build a closed-loop system. Finally, the characteristics of stability, overshoot and response speed of the mathematical model and circuit model systems are analyzed. According to the simulation results of PSFB ZVS circuit, the three control strategies have certain optimizations in overshoot and adjustment time. Among them, the optimization effect of PID control in closed-loop system is the weakest. From the results of small-signal model and circuit model, the ANF PID system has highest optimization. Experiments demonstrate that the ANF PID system gives satisfactory control performance and meets the expectation of optimization design.

Hybrid adaptive negative imaginary- neural-fuzzy control with model identification for a quadrotor

Quadrotor system is subject to multiple disturbances, including both internal and external effects (e.g. wind gusts, coupling effects, and unmodeled dynamics). For example, severe wind disturbances may significantly degrade trajectory tracking during the flight of autonomous aerial vehicles, or even cause loss of control or failure of a tracking mission. This paper introduces a robust hybrid control system, including a linear Strictly Negative Imaginary (SNI) controller and an adaptive nonlinear Neural-Fuzzy control law, to enable high-precision trajectory tracking tasks for a quadcopter drone. Based on a parallel form, both proposed controllers are able to enhance the transient performance, the system response, and the robustness of the quadcopter controllers. Also, a linear time-invariant SNI UAV dynamic model, in combination with an online adaptive residual nonlinear model using the neural network identification, is proposed to model the natural behavior of a quadcopter system. Through a series of numerical simulations, this paper highlights the effectiveness of our hybrid controller in the face of some parameter variations, such as nonlinear aerodynamic models and exogenous disturbances (e.g., wind gusts). Moreover, it compares its tracking performance with that of a fixed-gain SNI controller and the adaptive Neural-Fuzzy controller separately. Finally, the stability of the closed-loop control system is also discussed using the SNI theorem.

Review of Vehicle Active Safety Systems and Their Coordinated Control

Background:It is obvious that the safety concern associated with a vehicle is greatly valued by all, whether it is now or in the future, the automobile safety issue is the hotspot and the focus of the research by experts and scholars. The continuous increase in car ownership brings convenience to people's life and also poses a threat to people's life and property. Vehicles’ active safety system is the hotspot of current research and development, which plays an important role in automobile safety. Firstly, the vehicle’s active safety technology and its development are introduced. Then, a review is carried out examining the Anti-Lock Brake System (ABS), the Electronic Brake force Distribution (EBD/CBC), the Brake Assist System (BAS/EBA/BA), the Traction Control System (TCS/ASR), the Vehicle Stability Control (VSC/ESP/DSC), etc. At present, there are many patents on the control of each subsystem, but few patents on the integrated control for the active safety of vehicles.Objective:The main contents of this paper are as follows: the control strategies and methods of different active safety systems, the strategies to improve the stability of a vehicle control system and ensure the effectiveness of active safety system control. It provides a reference for the development of active safety control technology and patent.Methods:Through the analysis of different control algorithms and control strategies of Anti-lock and braking force distribution systems, it is pointed out that the switching of EBD/ABS coordinated control strategy according to slip rate can make full use of slip rate and road adhesion coefficient to improve the safety of the system. For the BAS, the slip problem is solved through the combination of the Mechanical Assistant Braking System (MABS) and Electronic Braking Assistant (EBA) system by measuring the distance and the speed of the vehicle ahead. The optimal slip rate control is realized by different control algorithms and control strategies of the traction control system. It is pointed out that the adaptive fuzzy neural controller should be used to control the yaw angular velocity and centroid side angle of the Electronic Stability Program (ESP), which has a good effect on maintaining vehicle stability. A sliding mode variable structure controller combined with constant speed control and law control is used to control the yaw moment.Results:Through the coordinated control strategy of EBD/ABS, the slip rate and road adhesion coefficient were fully utilized by switching according to the slip rate. The problem of the sliding slope was solved by MABS with EBA system. The ESP should use an adaptive fuzzy neural controller to control the yaw angular velocity and centroid side angle, and adopt the joint sliding mode variable structure controller which combines the ABS control and the yaw moment control. Through the optimal control theory, the coordinated control of each subsystem can significantly improve driving stability, riding comfort, fuel economy and so on.Conclusion:This study adopts different control strategies and control algorithms for different active safety control systems and makes full use of the tire-road friction coefficient and slip ratio optimal slip ratio. These focus on accurate control of control variables such as yawing angular velocity, centroid side-slip angle, yawing moment and finally ensure the vehicle braking stability, robustness of the controller and the lateral stability of the vehicle.

Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law; which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem.

An experimental study on inverse adaptive neural fuzzy control for nonlinear systems

In this paper, a type-2 fuzzy neural network is used to adaptive inverse control of a class of nonlinear systems. The proposed method has proper performance for real-time control of nonlinear and time-invariant systems and has a quick response to following changes. In this method, the high approxim ation ability of type 2 fuzzy systems is used to reverse system modeling and this inverse model is used immediately to control the system. In order to measure the efficiency of the proposed method, the control of dynamic, highly nonlinear and time varying water both temperature system with certain and uncertain water pressure is experimentally considered. The results show the accuracy of the proposed method.

A novel bilateral impedance controls for underwater tele-operation systems

Owing to characteristics of the flow and the variability, it is extremely difficult to achieve the stability and the transparency of the underwater tele-operation system. In practice, the accurate force may not easily be acquired due to model uncertainties, the time delay and external disturbances. In order to enhance the stability and the transparency of the underwater tele-operation, an adaptive neural fuzzy inference system disturbance observer-based impedance control is proposed to both the master side and slave side. The learning algorithm of the adaptive neural fuzzy inference system network and the disturbance observer may simultaneously suppress model uncertainties of the nonlinear system and disturbances of external underwater environment. Concerning the time delay, the stability is analyzed by Lyapunov theorem. Numerical simulations are performed and results demonstrate the effective performance of the proposed method.

Adaptive neural-fuzzy and backstepping controller for port-Hamiltonian systems

In this paper, a novel control strategy is shown for the stabilisation of dynamic systems in the form of port-Hamiltonian systems. This hybrid approach composed by a neural fuzzy and backstepping controller is implemented to stabilise the port-Hamiltonian system by dividing it into two blocks in order to separate the variables and yield an efficient control strategy. The proposed control strategy consists of a hybrid approach formed by a neural fuzzy and backstepping controller. The neural-fuzzy controller consists of two steps: an offline training implementing a gradient descent algorithm and an online training by a Lyapunov stability approach. The backstepping controller is designed by a recursive method considering the port-Hamiltonian system properties and implementing a Lyapunov stability approach. Along with the proposed control strategy, a neural-fuzzy observer is implemented to estimate the port-Hamiltonian system states considering the properties of the system representation. Finally, a cart pendulum example is shown to verify the effectiveness of the proposed observer and controller along with a comparative analysis.

Uncertainty observation-based adaptive succinct fuzzy-neuro dynamic surface control for trajectory tracking of fully actuated underwater vehicle system with input saturation

In this paper, an uncertainty observation-based adaptive fuzzy neural dynamic surface control (UOB-AFNDSC) is proposed to investigate the problem of high-accuracy trajectory tracking of fully actuated underwater vehicles with respect to unknown uncertainties and input saturation. The control framework of UOB-AFNDSC is constructed using the dynamic surface technique, under which an online-succinct fuzzy-neuro uncertainty observer with both projection-based parameter learning and succinct network structure learning is constructed to online identify the lumped uncertainty term including system uncertainties and external disturbances. To further suppress the effect of uncertainty reconstruction error and input saturation error, two adaptive robust terms are introduced, respectively. To theoretically analyze the stability of the overall closed-loop control system, novel error variables are introduced to ensure the uniform ultimate boundedness of all signals, and the tracking accuracy can be easily adjusted by the width parameter of novel error variables. Finally, some simulations are carried out to demonstrate the effectiveness of the proposed control scheme.

A novel wireless topology control algorithm with energy balance and low-overhead based on adaptive fuzzy neural network

In order to achieve the trade-off between network cost and network energy balance, a novel low-overhead wireless topology control algorithm based on adaptive fuzzy neural control system is proposed in this paper, which can effectively control the energy consumption of wireless sensor network nodes. The artificial neural network firstly is trained to obtain the parameters of fuzzy controller in wireless network, and then the communication range of the nodes is constantly adjusted through the feedback and circulation so as to control the transmission power of the node. Finally, the actual energy consumption of the node is close to the expected value, which can achieve the low-overhead and prolong the network life-time. Experimental results show that our proposed algorithm can achieve the purpose of controlling energy consumption of nodes, and the nodes have lower and more stable average energy consumption.

Adaptive Neural Fuzzy Inference System Disturbance Observer-Based Control for Reaching Movement of Musculoskeletal Arm Model

Traditional disturbance observer techniques have limitations for multi-input multi-output-coupled systems, time-varying systems with large parameters, and complex systems which are difficult to obtain accurate model, such as the human musculoskeletal arm system. The neural network has advantages of generalized approximation ability, learning ability, and self-adaptive ability. Therefore, this paper develops an adaptive neural fuzzy inference system disturbance observer-based control to achieve the point-to-point control and the path tracking control of the end point of the musculoskeletal arm model. The adaptivity is improved by adjusting the learning algorithm of neural network parameters and the network structure in real time. The uncertainty of the inverse model of the system is solved by constructing a pseudo-system. The adaptive neural fuzzy inference system is used to identify the inverse model of the pseudo-system and to design a compound controller based on feedback control method. The stability is analyzed by Lyapunov function in detail. Furthermore, internal disturbances are suppressed by the learning algorithm of the adaptive neural fuzzy inference system network while external disturbances may be estimated by the multi-input multi-output disturbance observer at the same time. Simulations are performed to verify the point-to-point control and the path tracking control. Results demonstrate that both of the adaptivity and the accuracy are enhanced so that the system can achieve accurate tracking control of any arbitrary trajectory.