Fuzzy Set-point Research Articles

Event-Triggered Adaptive Fuzzy Setpoint Regulation of Surface Vessels With Unmeasured Velocities Under Thruster Saturation Constraints

This article investigates the event-triggered adaptive fuzzy output feedback setpoint regulation control for the surface vessels. The vessel velocities are noisy and small in the setpoint regulation operation and the thrusters have saturation constraints. A high-gain filter is constructed to obtain the vessel velocity estimations from noisy position and heading. An auxiliary dynamic filter with control deviation as the input is adopted to reduce thruster saturation effects. The adaptive fuzzy logic systems approximate vessel’s uncertain dynamics. The adaptive dynamic surface control is employed to derive the event-triggered adaptive fuzzy setpoint regulation control depending only on noisy position and heading measurements. By the virtue of the event-triggering, the vessel’s thruster acting frequencies are reduced such that the thruster excessive wear is avoided. The computational burden is reduced due to the differentiation avoidance for virtual stabilizing functions required in the traditional backstepping. It is analyzed that the event-triggered adaptive fuzzy setpoint regulation control maintains position and heading at desired points and ensures the closed-loop semi-global stability. Both theoretical analyses and simulations with comparisons validate the effectiveness and the superiority of the control scheme.

Fuzzy Set-Point Weight for PID Controller Based on Antlion Optimizer to Congestion Avoidance in TCP/AQM Routers

Abstract: The delay in delivering packets and packets loss in computer networks is due to the problem of congestion in TCP/AQM routers, and to solve this crisis the TCP and AQM worked together. Where TCP provides secure data transfer and designed to handle congestion after its occurrence. While AQM predicts congestion and tries to resolve the problem before it occurs. In this work, the Fuzzy-PID (FPID) controller presented to manage the avoidance of congestion problems associated with TCP networks. The approach is based on hybridization between the Fuzzy Logic Controller (FLC) and the Proportional-Integral-Derivative(PID) controller, with the structure of Fuzzy-Set-Point-Wight(FSW) for PID controller that optimized by Antlion Optimizer(ALO) Using a linearized TCP congestion model. our target is to control the queue length of the router for the queue level in demand. The FPID controller shows good robustness for a Different scenario of the TCP network and the queue length response showed fast tracking capability with good robustness to network parameters changing in comparison to PID controller, all simulations are carried out using MATLAB 2017b.

Synthesis of Fuzzy Setpoint for Control System of Switched Reluctance Generator

Synthesis of Fuzzy Setpoint for Control System of Switched Reluctance Generator

Firefly algorithm tuned fuzzy set-point weighted PID controller for antilock braking systems

Antilock Braking Systems (ABSs) are brake controllers designed to maintain the wheel slip in desired level during braking and acceleration. Since the factors causing the wheel slip to change such as nature of road surface, vehicle mass etc are highly uncertain, the task of controlling it has been a challenging one. In this paper, a modified PID controller called Set-Point Weighted PID (SPWPID) Controller has been designed to control the wheel slip. First, a Genetic Algorithm (GA) based fuzzy inference system (GAFSPWPID) is developed to determine the value of the weight that multiplies the set-point for the proportional action, based on the current output error and its derivative. It makes the controller more adaptive to external disturbances. Then, the GA is replaced by Firefly Algorithm (FA). Minimization of Integral Square Error (ISE) has been taken as objective for both cases. The performance of proposed Firefly Algorithm tuned Fuzzy Set-Point Weighted PID (FAFSPWPID) Controller is compared with SPWPID and GAFSPWPID controllers. Also, the performance of proposed controller is assessed for different initial conditions. A comparison has also been made with the controllers presented earlier in literature. Simulation results show that the proposed FAFSPWPID Controller performs better in both set-point tracking and adaptive to external disturbances than the other controllers.

Beer Fermentation Temperature Control Curve Optimization Based on the Fuzzy - Neural Network PID Control Algorithm

An auto-control model is presented to the process of beer fermentation, which has the characteristics such as time-varying, inertia, time-delay and nonlinear. The traditional PID control is difficult to accurately control. This paper according to the beer fermentation of problems puts forward a new control algorithm: fuzzy-neural network PID control algorithm. The fuzzy logistic differential control and intelligent integral control were supplemented into the fuzzy set-point weight tuning, so that the insufficiency of original PID control method was effectively improved. The advantages of this control algorithm are not only constitute a simple, small static error, dynamic response speed but also the ability to learn, etc. Therefore it not only can strengthen robust and intelligence of the system, but also make design simple and easily be required.

Fuzzy Logic Based Set-Point Weighting for Fractional Order PID Control

Differentiation and integration of non-integer order have drawn increasing attention in research community. Fractional order dynamic systems have been recognized as effective tool for characterizing the real world phenomena. This may be implemented by using different control structures in which a fuzzy mechanism is adopted to tune the parameters by using Ziegler-Nichols method. Fractional-order PID control is the development of general integer-order PID controller. This paper proposes the basic framework of fractional order dynamic system with fuzzy weighted set-point. Comparisons are made with PID and FOPID controllers for first and second order systems. The response shows the superiority of the fuzzy set-point weighting methodology over the other methods.

N-dimensional (α, β)-fuzzy H-ideals in hemirings

In this paper, we are combining the notions of n-dimensional fuzzy set and n-dimensional fuzzy point to introduce a new notion called n-dimensional (a, b)-fuzzy ideals in hemirings. The defined notion is a generalization of fuzzy ideals, interval valued fuzzy ideals, (a, b)-fuzzy ideals and interval valued (a, b)-fuzzy ideals in hemirings. We describe the characterization of n-dimensional (a, b)- fuzzy ideals in hemirings by upper level set. More over we define n-dimensional prime (a, b)-fuzzy ideals in hemirings.

Special Issue on Modeling and Simulation

Special Issue on Modeling and Simulation

Robust fuzzy controller development for a PEM fuel cell system

A polymer electrolyte membrane (PEM) fuel cell system model that is suitable for control study is presented in this paper. The PEM mathematical model is then used for the controller development to improve system performance. Within the university research facilities, there is available a PEM fuel cell test station (PEM-FCT), so the PEM-FCT is used for the modelling and controller study. A fuzzy set-point weighted PID controller is designed to improve the performance of the fuel cell system. The underlying idea of our controller design is to use a fuzzy based system to support the operation of a PID controller. The new control strategy is implemented on a PC based computer model of the FCT system and simulated. The results indicate that the control strategy has improved the system performance significantly. Thus, the new control strategy is implemented on PEM-FCT. Comparison made between the simulation model response and experimental results from the real FCT system using the same controller which shows performance enhancements, compared with the existing closed loop controller based on stoichiometry value updates to the flow controllers.

Fuzzy PID controller for 2D differential geometric guidance and control problem

A fuzzy set-point weighting proportional–integral–derivative (PID) controller is applied in the development of the flight control system for a two-dimensional differential geometric (DG) guidance and control system, whose function is to guarantee the achieved angle of attack (AOA) and track the commanded AOA efficiently. In particular, a Lyapunov stability criterion is introduced to study the relation between the stability and the output of the fuzzy inference system, and a genetic algorithm is utilised to tune the PID gains in the simulations so as to show the full potentiality of the proposed control scheme. The results demonstrate that the designed controller yields a fast-responding and stable system that is robust to parameter variations. Moreover, the DG guidance law is viable and effective in a realistic missile defence engagement.

PI control based on fuzzy set-point weighting tracking for hydraulic crane boom system

A PI control strategy based on fuzzy set-point weighting following was proposed for the active damping control of a hydraulic crane boom system (HCBS). Two valve-controlled PI controllers, which include a proportional feedforward controller based on fuzzy set-point weighting following and a limited semi-integrator(LSI), are designed respectively. LSI is used to limit output signal and to prevent wind up at the low frequency of the spectrum. By using a range camera and an electronic feedback control, the tip damping on the HCBS can be adjusted artificially. A collaborative control simulation technique of HOPSAN and MATLAB/SIMULINK is applied to the controller design. Simulation results show that the proposed PI control system has less overshoot as well as faster response. The tip damping on the HCBS during operation is improved.

Tuning of PID controllers with fuzzy logic

The paper presents a comparison between different methods, based on fuzzy logic, for the tuning of PID controllers. Specifically considered are different control structures in which a fuzzy mechanism is adopted to improve the performances given by Ziegler-Nichols parameters. To verify the full capabilities of each controller, genetic algorithms are used to tune the parameters of the fuzzy inference systems (scaling coefficients, shape of the membership functions, etc.). Furthermore, a discussion about the practical implementation issue of the controllers is provided, and comparisons made with a typical PID-like fuzzy controller and a standard nonlinear PID controller. The results show the superiority of the fuzzy set-point weighting methodology over the other methods.

Adaptive Tuning of Fuzzy Set-Point Weighting for PID Controllers

Abstract In this paper a new adaptive procedure for the tuning of Proportional-Integral-Derivative (PID) controllers is proposed. It is based on the concept of fuzzy set-point weighting, i.e. the set-point weight value is continuously updated depending on the current system error and its time derivative, according to a fuzzy inference system. In this way, both the load disturbance rejection and the Bet-point following specifications can be properly addressed. The parameters of the PID gains are fixed according to the Ziegler-Nichols method, while the coefficients of the fuzzy mechanism are adaptively modified based on the step responses. Simulation results show how the overall methodology is very effective in improving the performances in the step response, without requiring the acquisition of specific knowledge from the operator and without requiring a significant extra-effort from a computational point of view.