Structure Of Fuzzy Controller Research Articles

A Novel Approach to a Recurrent Fuzzy Logic Controller Design Applied to Systems with Multiple Disturbances

In any process, control plays a vital role in the quality of the product. The influence of a controller's operation on a process is linked to its success or failure. However, using a controller that works properly presents several obstacles, such as non-linearity or the lack of a model that adequately represents the behavior of the process. Different approaches have been devised and studied to deal with these difficulties. One of the most promising and widely applied and studied in recent years is fuzzy control due to its ability to operate systems with no precise plant model. Herein, a set of rules and a fuzzy control structure are proposed, with fewer design parameters than a conventional fuzzy controller and better performance. The global stochastic optimization tool Differential Evolution is used to obtain the design parameters of the proposed fuzzy controller.

Improvement of predictive control algorithm based on fuzzy fractional order PID

Abstract The existing predictive control strategy has comprehensive prior knowledge of the controlled process, requires weak continuity of the search space for parameter optimization, and its application is limited to some extent. Therefore, improved research on the fuzzy fractional proportional integral differential (PID) predictive control algorithm is proposed. First, the control principle of PID predictive control equipment is proposed. According to this principle, the structure of the PID predictive control equipment adaptive fuzzy PID energy-saving controller is constructed. Through the PID energy-saving control parameter setting principle and fuzzy control rules, the adaptive fuzzy PID energy-saving control of PID predictive control equipment is realized. Finally, the fractional order PID predictive transfer function model is constructed to improve the predictive control algorithm based on PID optimization technology. The experimental results show that the accuracy and efficiency of the designed algorithm can get the best performance index, and its stability, overshoot, time, and control accuracy are basically unchanged. In the small area temperature control, the disturbance interference is small, the anti-disturbance ability is good, and it has strong robustness.

Experimental investigation of evolving cloud-based fuzzy control of a pilot thermal exchanger under a decentralized framework

Relying on the Robust Evolving Cloud-based Control (RECCo) protocol, a decentralized evolving fuzzy control scheme is presented in this paper for a strongly interacting thermal exchanger process and for the first time experimentally investigated on the real pilot plant. Loop pairing information is first derived based on experimental analysis of the process dynamics. Accordingly, independent RECCo-controlled loops of system temperature are arranged in functional blocks without any prior decoupling or process modelling. The evolvable fuzzy controller structure is learned concurrently relying on data streams only. Simulation and experiments are carried out to support the design procedure and verify the control performance. The results show reasonable tracking performance under different scenarios.

Tuning of fuzzy controller with arbitrary triangular input fuzzy sets based on proximal policy optimization for time-delays system

The analytical structure of fuzzy controller is important for better understanding and effective design of the fuzzy control system, especially for explaining why fuzzy control system work well. In this paper, we develop a three-dimensional fuzzy controller with arbitrary triangular input fuzzy sets. Then, the analytical structure of this fuzzy controller is derived rigorously, and some properties of this controller are given. To determine the parameters of the fuzzy controller, a novel proximal policy optimization algorithm with recall pool (RP-PPO) is proposed. A recall pool is designed in proximal policy optimization algorithm to store the best ten sets of parameters to enhance the optimization results. Finally, simulation studies are carried out on two time-delay systems and a nonlinear chemical process with time-varying time delay to validate the applicability and effectiveness of the proposed fuzzy controller.

Positivity and separation principle for observer-based output-feedback disturbance attenuation of uncertain discrete-time fuzzy models with immeasurable premise variables

We propose a robust observer-based output-feedback (OBOF) controller design method for the positive stabilization of discrete-time Takagi–Sugeno (T–S) fuzzy models subject to immeasurable premise variables, parametric uncertainties, and l∞ disturbances. The results of this paper are as follows. First, a new fuzzy OBOF controller structure is used to improve the possibility that the uncertain closed-loop system is positive. Second, the separation principle is demonstrated for the l∞–l∞ disturbance attenuation. Finally, the stability, positivity, and disturbance attenuation of T–S fuzzy closed-loop models are analyzed in the presence of the perturbation caused by the imperfect premise matching by the immeasurable premise variable.

Experimental validation of fuzzy type‐2 against type‐1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

AbstractThis research presents and compares the outcomes of experimental implementations of different fuzzy logic control structures for a proton exchange membrane fuel cell (PEMFC). These devices are well known for their capability to transform chemical energy into electrical with low emissions. Commonly, a PEMFC has a linkage with a boost converter which allows a suitable end‐user voltage through a nonlinear control law. Hence, the contribution in this sense is the experimental comparison of two fuzzy logic strategies known as type‐1 and type‐2 that were implemented in a PEMFC system. The approaches were embedded in a control board dSPACE 1102 which also has the capability to acquire data. The contrast of results showed capabilities improvement against disturbances in terms of error reduction, control signal, and robustness.

Fuzzy based back stepping controller for glucose level regulation under meal disturbance

Diabetes is one of the most common and critical diseases around the world, which need insulin injections to control the body’s glucose rate. A robust back stepping (BS) controller design based on fuzzy system is introduced in this paper to control the glucose level with the presence of meal disturbance. The controller’s design is based on Bergman’s mathematical model. Different fuzzy controller structures are implemented (fuzzy PI, fuzzy PD, and fuzzy PID) controllers along with BS controller named as (BS-fuzzy PI, BS-fuzzy PD, BS-fuzzy PID) controllers. Simulation results using MATLAB/Simulink show efficiency and robustness of the proposed design in terms of controlling the insulin concentration level in blood under meal disturbance and retaining the glucose level to its Basal value.

Modified grasshopper optimization algorithm optimized adaptive fuzzy lead-lag controller for coordinated design of FACTS controller with PSS

This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopper optimization algorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopper optimization algorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopper optimization algorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority.

Human-Machine Shared Steering Control for Vehicle Lane Keeping Systems via a Fuzzy Observer-Based Event-Triggered Method

This paper is concerned with the human-machine shared control issue for vehicle lane keeping systems via a new fuzzy observer-based event-triggered method. In order to capture system nonlinear and uncertain characteristics such as nonlinear tire dynamics, varying velocity and driver behavioral uncertainties, Takagi-Sugeno fuzzy approach is employed to model the global driver-vehicle-road system. After system modeling, the fuzzy observer-based control structure is considered because a full states information is not available in practical driving environment. Then, most existing human-machine shared control methods are based on the periodic sampling communication mechanism. However, since the network bandwidth is limited, the above mechanism may cause oversampling and communication congestion. Thus, an adaptive event-triggered mechanism is introduced between the observer and the controller to mitigate the communication burden and improve the bandwidth utilization. Based on Lyapunov functional theory, a set of sufficient conditions are given to calculate desired human-machine shared controllers. Finally, simulation tests are implemented on Matlab/Simulink-CarSim platform and simulation results illustrate that the proposed method can achieve a favorable improvement in the lane keeping capability, the driver handling comfort and the network bandwidth utilization.

Optimal design of fuzzy-PID controller for automatic generation control of multi-source interconnected power system

This paper suggests a fuzzy logic controller (FLC) structure from seven membership functions (MFs) and its input–output relationship rules to design a secondary controller to reduce load frequency control (LFC) issues. The FLC is coupled to a proportional–integral–derivative (PID) controller as the proposed FPID controller, which is tuned by an optimized water cycle algorithm (WCA). The proposed WCA: FPID scheme was implemented with two models from the literature under the integral time absolute error cost function. Initially, a two-area non-reheat unit was implemented, and the gains of PID and FPID controllers were adjusted to verify the suitability of WCA in solving LFC issues. Then, in order to identify the robustness of the closed-loop system, sensitivity analysis is carried out. Additionally, a two-area non-reheat unit was tested under the governor dead band nonlinearity. To guarantee the suitability of the proposed FPID controller, a model with a mixture of power plants, such as reheat, hydro, and gas unit in each area was carried out with and without the HVDC link, which can increase practical issues with LFC. The proposed controller's robustness was studied for all models under numerous scenarios, step load perturbations (SLP), and different objective functions. Simulation results proved that the proposed FPID controller provided superior performance compared to recently reported techniques in terms of peaks and settling time.

Experimental Fuzzy Control for Tower Crane

Tower crane is a popular model in industry. It helps to move constructing material from lower place to higher place and vice versa. Due to its importance in real application, educational experimental models are necessary to be built in laboratory for researching and training students. In this paper, an experimental platform of tower crane system is presented for testing algorithms. For a real model with un-known system parameters and MIMO under-actuated structure, intelligent control is a suitable selection. Fuzzy control is a primary algorithm in training intelligent control for students. Thence, an 81-rule fuzzy controller is designed and used to control position and anti-sway for this model. This idea is inspired from fuzzy control for SIMO system, such as inverted pendulum. Our controller gives good results under kinds of external effects. The quality of controlling is proved to be better under fuzzy controller than under no-control. Therefore, survey in this paper can be used to train students in fuzzy control structure for MIMO under-actuated system.

Different Fuzzy Control Configurations Tuned by the Bees Algorithm for LFC of Two-Area Power System

This study develops and implements a design of the Fuzzy Proportional Integral Derivative with filtered derivative mode (Fuzzy PIDF) for Load Frequency Control (LFC) of a two-area interconnected power system. To attain the optimal values of the proposed structure’s parameters which guarantee the best possible performance, the Bees Algorithm (BA) and other optimisation tools are used to accomplish this task. A Step Load Perturbation (SLP) of 0.2 pu is applied in area one to examine the dynamic performance of the system with the proposed controller employed as the LFC system. The supremacy of Fuzzy PIDF is proven by comparing the results with those of previous studies for the same power system. As the designed controller is required to provide reliable performance, this study is further extended to propose three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA for the LFC for the same dual-area power system. Moreover, an extensive examination of the robustness of these structures towards the parametric uncertainties of the investigated power system, considering thirteen cases, is carried out. The simulation results indicate that the contribution of the BA tuned the proposed fuzzy control structures in alleviating the overshoot, undershoot, and the settling time of the frequency in both areas and the tie-line power oscillations. Based on the obtained results, it is revealed that the lowest drop of the frequency in area one is −0.0414 Hz, which is achieved by the proposed Fuzzy PIDF tuned by the BA. It is also divulged that the proposed techniques, as was evidenced by their performance, offer a good transient response, a considerable capability for disturbance rejection, and an insensitivity towards the parametric uncertainty of the controlled system.

Design of porous media burner control system based on arm embedded processor

Because the traditional porous media burner control system has the problems of low control accuracy and long control time, a porous media burner control system based on Advanced RISC Machines (ARM) embedded processor is designed. The hardware of the system is designed through ARM embedded processor, FPGA data acquisition card, power supply circuit, reset circuit and audible and visual alarm module, and the system software is designed based on it. According to the structure of fuzzy controller, fuzzy control rules, assignment table of fuzzy variables, fuzzy control table, and defuzzification, the fuzzy controller is used to fuzzy control the porous media burner. The simulation results show that the designed system has the highest precision and the shortest control time.

On-Line FPGA Hardware in the Loop Validation of Based Fuzzy-STPWM Induction Motor Control

This work is part of the study and implementation of a fuzzy PWM control structure (fuzzy pulse width modulation) of an induction machine (MAS) on a circuit-based platform reconfigurable FPGA type. We first presented a strategy for the hardware implementation of a fuzzy inference system on a programmable logic circuit of FPGA type, through the hardware description language (VHDL) and Xilinx generator system (XSG). Secondly we proposed Fuzzy-PWM architecture for the improvement of response time of an asynchronous machine (MAS) and finally we validate the proposed hardware co-simulation architecture in real time on the ML402 development kit (based on FPGA Xilinx Virtex-4) and Simulink / Matlab.

Fuzzy Controller for Simplified Model of Mobile Vehicle Steering Booster

Abstract The article is aimed on the possibility of fuzzy control of an experimental device that represents the simplified model of automobile steering booster. The main part of model consists of a graphical unit illustrating the control of speed and supporting torque. Based on the structure of fuzzy controller, various counts and configurations of fuzzy sets were defined, whereby the relevant results were achieved by their application on controlled system.

Design of a dual‐layered tilt fuzzy control structure for interconnected power system integrated with DFIG

Design of a dual‐layered tilt fuzzy control structure for interconnected power system integrated with <scp>DFIG</scp>

Optimization of a Fuzzy Automatic Voltage Controller Using Real-Time Recurrent Learning

The automatic voltage regulator is an important component in energy generation systems; therefore, the tuning of this system is a fundamental aspect for the suitable energy conversion. This article shows the optimization of a fuzzy automatic voltage controller for a generation system using real-time recurrent learning, which is a technique conventionally used for the training of recurrent neural networks. The controller used consists of a compact fuzzy system based on Boolean relations, designed having equivalences with PI, PD, PID, and second order controllers. For algorithm implementation, the training equations are deduced considering the structure of the second order compact fuzzy controller. The results show that a closed-loop fuzzy control strategy was successfully implemented using real-time recurrent learning. In order to implement the controllers optimization, different weighting values for error and control action are used. The results show the behavior of the configurations used and its performance considering the steady state error, overshoot, and settling time.

The performance analysis of type-2 fuzzy fractional-order tilt integral derivative controller with enhanced Harris hawks optimization

Modern Alternating Current (AC) microgrids (ACMGs) are highly complex because of the indeterminate nature of distributed energy generation and load causes frequency fluctuations. It is essential to maintain the constant power amongst load and generations by the proper design of an efficient controller to stabilize frequency fluctuations. In this paper, the type-2 fuzzy fractional-order tilt integral derivative controller structure is designed and analyzed for frequency control of an ACMG by using an enhanced Harris hawks optimization (EHHO). The modified optimization technique is analyzed employing test functions and compared with similar methods. It is noticed that the modified optimization technique is outperforming the other methods in most of the test functions. The frequency control performance of an ACMG is demonstrated and compared with the original Harris hawks optimization (HHO) and other optimization methods. The proposed type-2 fuzzy fractional-order tilt integral derivative controller tuned with EHHO technique is demonstrated to be effective and superior to any of the alternatives by application to a typical two-area test system.

Enhancement of Transient Stability in a Grid-Connected Wind Power Plant Utilizing PI-FL Controller

This paper proposed a blend of corresponding PIC and FLC for controlling the pitch angle of wind turbine connected to grid. Initially, conventional control methodology is employed to design the PI controller and later on, the concept of fuzzy logic methodology is adapted to analyze the gains of proportional integral controller. Due to versatility of fuzzy logic controller and rugged structure, sturdy nature of proportional integral controller are clubbed together, it provides a virtuous stagnant and dynamic outcomes. The proposed novel technique can be assessed by considering various disturbances such as short circuit network fault. The reenactment after effects of proposed controller is contrasted with proportional integral controller and FLC. From the findings it is shown that the proposed methodology could improve stability even the wind farm is subjected to different operating conditions. Moreover, a performance index in terms of absolute maximum deviations is defined in order to evaluate the adequacy of the proposed controller.

LMI-Fuzzy Control Design for Non-Minimum-Phase DC-DC Converters: An Application for Output Regulation

Robust control techniques for power converters are becoming more attractive because they can meet with most demanding control goals like uncertainties. In this sense, the Takagi-Sugeno (T-S) fuzzy controller based on linear matrix inequalities (LMI) is a linear control by intervals that has been relatively unexplored for the output-voltage regulation problem in switching converters. Through this technique it is possible to minimize the disturbance rejection level, satisfying constraints over the decay rate of state variables as well as the control effort. Therefore, it is possible to guarantee, a priori, the stability of the large-signal converters in a broad operation domain. This work presents the design of a fuzzy control synthesis based on a T-S fuzzy model for non-minimum phase dc-dc converters, such as boost and buck-boost. First, starting from the canonical bilinear converters expression, a Takagi-Sugeno (T-S) fuzzy model is obtained, allowing to define the fuzzy controller structure through the parallel distributed compensation technique (PDC). Finally, the fuzzy controller design based on LMIs is solved for the defined specification in close loop through MATLAB toolbox LMI. Simulations and experimental results of a 60 W prototype are presented to verify theoretical predictions.