Fuzzy Neural Control Research Articles

Fuzzy rule-based neural appointed-time control for uncertain nonlinear systems with aperiodic samplings

In this paper, a fuzzy rule-based neural appointed-time control scheme for uncertain nonlinear systems with aperiodic samplings is proposed. To guarantee tracking properties with preassigned convergence time and remove the high dependency on accurate initial states of system, we construct an improved prescribed performance control (IPPC) scheme on the basis of a hyperbolic cosecant performance function with a finite-time behavior. In light of the unavailability of partial states and uncertainties, by combining a fuzzy wavelet neural network (FWNN) with a state observer, a FWNN-based state observer is developed, which can simultaneously approximate unavailable system states and unknown lumped disturbances with a remarkable accuracy. In addition, aimed at eliminating the problem of parameter updating explosion caused by overlarge learning dimensions, a minimum-learning-parameter (MLP) technique is embedded in the FWNN-based state observer, where the norm of weight matrix is employed for online adaptive updating. Furthermore, an event-triggered control scheme with relative thresholds is synthesized within the framework of dynamic surface control (DSC), which can allow for aperiodic samplings to save communication and actuating resources. Meanwhile, a Nussbaum type function is introduced to solve the issue of unknown control coefficients. The significant features of our work are twofold: (1) Appointed-time tracking performances with much fewer sampling times are assured. (2) An enhanced robustness against uncertainties is achieved with a lower computational complexity and the requirement of full-state measurements is relaxed. Finally, two simulation examples are performed to validate the effectiveness and advantages of the proposed control scheme.

Coordinated optimization control of shield machine based on dynamic fuzzy neural network direct inverse control

During shield machine tunneling, the earth pressure in the sealed cabin must be kept balanced to ensure construction safety. As there is a strong nonlinear coupling relationship among the tunneling parameters, it is difficult to control the balance between the amount of soil entered and the amount discharged in the sealed cabin. So, the control effect of excavation face stability is poor. For this purpose, a coordinated optimization control method of shield machine based on dynamic fuzzy neural network (D-FNN) direct inverse control is proposed. The cutter head torque, advance speed, thrust, screw conveyor speed and earth pressure difference in the sealed cabin are selected as inputs, and the D-FNN control model of the control parameters is established, whose output are screw conveyor speed and advance speed at the next moment. The error reduction rate method is introduced to trim and identify the network structure to optimize the control model. On this basis, an optimal control system for earth pressure balance (EPB) of shield machine is established based on the direct inverse control method. The simulation results show that the method can optimize the control parameters coordinately according to the changes of the construction environment, effectively reduce the earth pressure fluctuations during shield tunneling, and can better control the stability of the excavation surface.

Development of Cooperative Controller for Dual-Motor Independent Drive Electric Tractor

This paper proposes a control strategy of dual-motor independent drive electric tractor with cooperative control power take-off (PTO) and driving systems by investigating its structural characteristics to meet the field operation requirements. In the following operation mode, the vehicle speed is taken as the input signal, and the PTO motor speed is followed by the vehicle speed at a proportional ratio. Four different principles of collaborative control strategy-based stability control, namely, general closed-loop, conventional PID, adaptive fuzzy PID, and fuzzy neural network adaptive PID controls, are proposed to meet the requirements during speed change. Hardware design is divided into modules in accordance with circuit function. Software design is divided into application layer and underlying software, where the application software realizes the control strategy of the entire machine. The underlying software is based on the hardware driver of MC9S12XEP100 that connects the application layer software and hardware. A hardware-in-the-loop test platform based on dSPACE/DS1007 is built to test the function of the collaborative controller. Results show that the stabilization time of fuzzy neural network adaptive PID control is 0.024 s. Compared with the three other control modes, the settling time decreases by 0.256, 0.034, and 0.028, respectively, and the overshoot decreases to 1.6%. Root locus analysis results show that the stability of the system is the best. Under the entire machine cooperative operation mode hardware in the loop test, the PTO motor maintains the fixed target speed under the fixed speed output mode, and the PTO speed in the following mode has a good follow-up to the vehicle speed. The controller realizes the control of the power output shaft to cut between the following and standard speeds. It also meets the requirement of cooperative control in the operation of dual-motor independent drive electric tractor.

Cooperative Fuzzy-Neural Control for Wastewater Treatment Process

Wastewater treatment process, including multiple biochemical reactions, is a complex industrial process with strong nonlinearity and time-varying dynamics. It is a challenge to design an effective controller for this kind of process. To solve this problem, a cooperative fuzzy-neural controller is proposed to improve the operation performance of wastewater treatment process in this article. The main advantages of cooperative fuzzy-neural controller contain the following three parts: first, a structure cooperative strategy is developed to adjust the number of fuzzy rules in the controller by coordinating the indexes of similarity and independent contributions. Then, the structure of cooperative fuzzy-neural controller with the balanced redundant degree and efficiency can be adapted to satisfy the different operation conditions of wastewater treatment process. Second, a parameter cooperative strategy is proposed to coordinate the global and local parameters of controller. Then, the parameters can be optimized together to meet the control requirements. Third, the stability of control strategy is given in details. Then, the corresponding stability conditions are shown to guide its application. Finally, the control performance is confirmed on the benchmark simulation model and real wastewater treatment process. The results demonstrate that the proposed cooperative fuzzy-neural controller can achieve superior control precision and low computational burden.

Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system

In this study, the control problem of complex robot system with uncertainties and disturbances is addressed. Fuzzy system-fuzzy neural network-backstepping control (FS-FNN-BSC) system is proposed, which can guarantee the accurate, stable and efficient control. First, the general dynamics model of robot is introduced briefly. Then, the design procedure of backstepping control (BSC) technique is presented, to make the best of the advantages of fuzzy system (FS) and fuzzy neural network (FNN) and compromise the accuracy and efficiency, the FS is adopted to approximate the modeling information, and the FNN is utilized to approximate and predict the non-modeling information, and the FS-FNN-BSC system is constructed. Moreover, based on the Lyapunov stability theorem, the stability of the FS-FNN-BSC is proved. To illustrate the correctness, practicality and generality of the proposed control method, the FS-FNN-BSC system is applied to the series robot (KUKA robot) and the parallel robot (Delta robot). And the superiority of the proposed FS-FNN-BSC strategy is highlighted by quantitative comparison with the existing intelligent control methods.

Neural-fuzzy controller configuration design for an electro-optical line of sight stabilization system

This paper presents a Neural-fuzzy model structure to improve the disturbance attenuation performance of a gimballed Line of Sight (LOS) stabilization system. Initially, a Fuzzy Logic Controller (FLC) based on prior qualitative information about system dynamics and linguistic performance criteria is developed. Next, proposed Neural-fuzzy model architecture is constructed, which overcome the difficulties and limitations of each isolated methodology. The Neural-fuzzy architecture is developed based on input-output data-sets available from FLC. Fuzzy curve approach is used to determine significant inputs, number of rules, initialization of connecting layers weights, and hence the model structure. Both the controller configurations are tested based on critical performance characteristics such as stability of the loop, responsiveness of the loop and insensitivity to disturbances. Finally, the comparative analysis suggests that the proposed Neural-fuzzy controller completely outperforms the FLC configuration and hence, can be very effective for more precise pointing applications.

Optimal DC machines performance based on intelligent controller

The disorganized neural controller links the calculation of the delicate rationale to the architecture of a five-layer pseudo-nervous system. The classical PID controller was replaced by an Adaptive Neuro Fuzzy Inference system (ANFIS), that will be adjusts the thin induction framework with the calculation of learning in half and a half; this makes the thin framework for learning. A fuzzy nervous-based vector display controls a frame under control. This paper displays an optimal performance with speed controller based on the drive of a power system. The proposed Neural Fuzzy Control Unit combines of a thin Boolean arithmetic calculation and five layers mock neural network system (ANN) structure, which fine-tunes the delicate deduction framework with a half and a half learning arithmetic. This makes a thin framework for learning. The proposed drive recruiting engine exhibition controlled by neurotransmitters is examined in different working conditions. The consequences of the proposed console contrast with those obtained by a regular PI controller and Fuzzy Logic controller. The study of reproduction shows the drive’s strength and reliability for superior driving applications. Structural reconstruction is accomplished through applying of Matlab Verson 2019b. DC machine is a contextual search. Palatable results were obtained; this demonstrates the capability of the ANFIS console in control using a high non-linear dynamic frame and great results can be obtain by tuning the intelligent controller.

Realization of the Neural Fuzzy Controller for the Sensorless PMSM Drive Control System

A neural fuzzy controller (NFC)-based speed controller for the sensorless permanent magnet synchronous motor (PMSM) drive control system is realized in this paper. The NFC is a fuzzy logic controller (FLC), which adjusts the RBFNN-based (radial basis function neural network) parameter by adapting the dynamic system characteristics. For sensorless PMSM drive, the integration of sliding mode observer (SMO) and phase-locked loop (PLL) is executed to estimate the rotor position and speed. To eliminate the initial rotor position estimation and overcome the conventional PLL-based position estimation error in the direction reversion transition, the I-f control strategy is applied to start up the motor and change the rotational direction effectively. The system performance was verified in various experimental conditions. The simulation and experimental results indicate that the proposed control algorithm is implemented efficiently. The motor starts up with diverse external loads, operates in a wide speed range for both positive and negative directions, and reverses the rotational direction stably. Furthermore, the system presents robustness against disturbance and tracks the command speed properly.

Fuzzy wavelet neural control with improved prescribed performance for MEMS gyroscope subject to input quantization

In this paper, a fuzzy wavelet neural control scheme with improved prescribed performance is investigated for micro-electro-mechanical system (MEMS) gyroscope in the presence of uncertainties and input quantization. A hysteresis quantizer (HQ) is introduced in the controller design to generate input signal in a finite set, which can greatly reduce the actuator bandwidth without decreasing the control accuracy, and avoid the undesirable chattering occurring universally in other quantizers. To guarantee the output tracking with better prescribed transient behavior, a modified prescribed performance control (MPPC) consisting of asymmetric performance boundaries and an error transformation function is explored, such that arbitrarily small overshoot can be assured without retuning design parameters. Unlike the traditional neural network that suffers from explosion of learning, a fuzzy wavelet neural network (FWNN) based on minimal-learning-parameter (MLP) is designed to identify uncertainties with slight computational burden. A robust quantized control scheme is synthesized to compensate for quantization error and achieve prescribed ultimately uniformly bounded (UUB) tracking. Finally, extensive simulations are presented to verify the effectiveness of proposed control scheme.

Thermal comfort control via air conditioning system using fuzzy neural network feedback controller

<span>Despite their complexity and uncertainty, air conditioning systems should provide the optimal thermal conditions in a building. These controller systems should be adaptable to changes in environmental parameters. In most air conditioning systems, today, there are On/Off controllers or PID in more advanced types, which, due to different environmental conditions, are not optimal and cannot provide the optimal environmental conditions. Controlling thermal comfort of an air conditioning system requires estimation of thermal comfort index. In this study, fuzzy controller was used to provide thermal comfort in an air conditioning system, and neural network was used to estimate thermal comfort in the feedback path of the controller. Fuzzy controller has a good response given the non-linear features of air conditioning systems. In addition, the neural network makes it possible to use thermal comfort feedback in a real-time control.</span>

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.

Optimal Robot-Environment Interaction Under Broad Fuzzy Neural Adaptive Control.

This article proposes a novel control strategy based on a broad fuzzy neural network (BFNN) which is subjected to contact with the unknown environment. Compared with the conventional fuzzy neural network (NN), a prominent feature can be achieved by taking the advantage of the broad learning system (BLS) to explicitly tackle the problem of how to choose a sufficient number of NN units to approximate the unknown dynamic model. Aiming at providing a soft compliant contact scheme without the requirement of the environment model, an adaptive impedance learning is developed to establish the optimal interaction between the robot and the environment. Meanwhile, the problems related to the state constraints are addressed by incorporating a barrier Lyapunov function (BLF) into the design of a trajectory tracking controller. The proposed method can achieve desired tracking and interaction performance while guaranteeing the stability of the closed-loop system. In addition, simulation and experimental studies are performed to verify the effectiveness of BFNN under optimal impedance control with a two degree-of-freedom (DOF) manipulator and a Baxter robot, respectively.

Recurrent fuzzy wavelet neural network variable impedance control of robotic manipulators with fuzzy gain dynamic surface in an unknown varied environment

In this paper, an intelligent variable impedance control combined with a fuzzy gain dynamic surface is proposed to improve the interaction of the robot manipulator with an unknown varied environment. The parameters of the proposed variable impedance are adapted by optimization an introduced cost function using a recurrent fuzzy wavelet network. The stability conditions for the varying inertial, stiffness and damping are presented to guarantee the stability of the variable impedance. Additionally, a fuzzy dynamic surface method is developed to tune the gains of the dynamic surface as a robust controller. The proposed fuzzy gain dynamic surface is used to force the end-effector of the manipulator to track the desired impedance profile in the presence of large disturbances. Using Lyapunov's method, the stability of the mentioned closed-loop system is proved. Finally, by using a designed simulator for IRB120 (ABB) robot, several simulations are carried out to verify the performance of the proposed method for the execution of various tasks in an unknown varied environment in the presence of large disturbances.

Design and Verification of Fuzzy Neural Network Automatic Control Algorithm in Intelligent Agriculture

Through the analysis and in-depth study of the fuzzy control system model and neural network, the optimal control strategy is obtained by the organic combination of fuzzy reasoning and neural network analysis training, and the automatic control algorithm based on fuzzy neural network is designed. The modified algorithm is applied and verified in Modern Agriculture Demonstration Park of Chengdu Agricultural College. The results show that the designed algorithm can meet the demand of automatic control in the field of intelligent agricultural control, and the control system has good universality and scalability. The application of automatic control algorithm based on fuzzy neural network can accelerate the development of agricultural modernization in China, and lay a solid foundation for the further research on the application of automatic control in other fields.

Fuzzy Neural Technique Based Dynamic Voltage Restorer for Power Quality Enhancement Under Different Voltage Variations

Abstract— Recently a large interest has been focused on power quality field due to: disturbances caused by non-linear loads, Increase in the number of electronic devices and renewable energy sources. Power quality measures the fitness of the electric power transmitted from generation to the industrial, domestic and commercial consumers. In a power system voltage distortion introduced by harmonics and voltage sags are the most severe affecting power quality, because of both consumers and utilities are affected by these disturbances. Different methods to enhance the power quality but the custom power device is the most effective and efficient method. One of which is the use of Dynamic Voltage Restorer (DVR). The performance of the DVR based on Fuzzy Neural controller to restore the load voltage to its nominal value under different fault conditions and power disturbances is presented in this paper. Simulation results are carried out using MATLAB/SIMULINK program. The faults and disturbances are initiated at 0.8s and kept till 0.95s.

Elements realization of software-algorithmic system optimization of technological processes based on adaptive methods

The paper examines a possibility to optimize complex technological processes of energy resources transfer and consumption. Notes the importance of unidentified and weakly formalized factors consideration, which influence quality of the described object classes functioning. Suggests use of fuzzy neural networks and controllers on their basis for implementation of technological processes optimization of energy resources transfer and consumption at the expense of its dynamic accuracy forecast improvement. Presents algorithmic realization, that later on configured in the MATLAB simulated environment. Quantitative and qualitative parameters, which characterize fuzzy neural controller’s usage quality, were received. Later, software environment elements are being described, which constructed on the basis of the suggested adaptive approach for complex technological processes optimization. Points out a possibility to conduct comparative study, and also simulation modeling implementation by way of presented solutions.

Increase in accuracy forecast for electrical energy consumption of the WEM subjects using fuzzy neural networks

This paper covers in depth functioning essence of the balancing electricity market in the Russian Federation in accordance with the current economic stimuli. Significant role of the prediction process for energy resources consumption has been noted, which has a direct influence on a pricing policy within the marked trade area. Analysis of different factors, that impact execution accuracy of the one day in advance model, was conducted. Problem of the weakly defined and unknown parameters, that influence the performance of the balancing electricity market, is suggested to resolve by means of fuzzy neural controller. The paper presents its structure and settings, and possibility to scale it to use not only as an analyzer but as an executive element in the subsystems of electric power systems. High accuracy performance of the fuzzy neural controller was shown - around (0,5-3) %, that is much higher than methods already in use (for instance, based on a coefficient of decline/growth of power consumption).

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.

Neural Fuzzy Control of the Indoor Air Quality Onboard a RO–RO Ship Garage

Air quality control is necessary to improve the environmental condition in particular Indoor Air Quality (IAQ), which has a direct impact on a living organism. In marine application, the International Maritime Organization (IMO) made standards to determine the gas emission limits and specify the air circulation in IAQ in addition to employing in energy-efficient and energy management system which leads to reduce emission and enhance the environmental condition by efficient and economical way. Applying advanced energy management policies and control could be achieved by energy consumption. In this paper, an intelligent neuro-fuzzy controller has been designed to model and control carbon monoxide (CO) concentration for a real case study of a high-speed craft passenger ship with a vehicle garage onboard for a liner between Egypt and Saudi Arabia ports. The relation between emitted CO and the number of cars and their positions has been modelled using artificial neural network (ANN). The ANN model has been built and validated based on real measurements of CO at different ventilation conditions of the case study. Different fuzzy controllers, fixed and adaptive, are designed to control CO during loading and unloading states. Scaling factors of fuzzy controller are adapted using two different ways namely Supervisor Fuzzy Controller (SFC) and Particle Swamp Optimization (PSO). Simulation results have analysed the proposed control system at different conditions. The obtained outcomes manifest the fact that the controller tends to work robustly and efficiently to maintain CO at the permissible allowed range.

Adaptive Finite-Time Saturated Tracking Control for a Class of Partially Known Robots

Only partial system knowledge for the generalized robotic dynamics with input saturation, i.e., the regression matrix for the specific robot, is required to design the proposed adaptive finite-time saturated tracking control (AFTSTC). It contains a nonlinear auxiliary tracking error, which can shape the system frequency response. As the operating point is in the neighborhood of the zero auxiliary tracking error, nonlinear filtering gains can be increased to accelerate its tracking ability. Moreover, the traditional skew-symmetric matrix's condition for the time derivative of inertia matrix and the Coriolis and centrifugal force matrix is not necessarily required such that the uncertainties and the finite-time convergence are reduced. The computational complexity as compared with the fuzzy neural network adaptive control is also addressed. The stability of the closed-loop system is verified by the Lyapunov stability theory. Finally, two practical robotic examples are given to validate the effectiveness and robustness of the proposed control.