Order Fuzzy Controller Research Articles

Research step of PID control method of stepper motor based on improved fuzzy control algorithm

The significance of PID control within the management system of stepper motors is noteworthy; nonetheless, it is worth noting that stepper motors are susceptible to issues such as low power, step loss, and vibration. The conventional Proportional-Integral-Derivative (PID) control method is insufficient in addressing the control challenge specific to stepper motor management systems. Hence, this research work presents an enhanced fuzzy control method that integrates the principles of fuzzy control theory with traditional PID control theory. The integration of fuzzy control into the P ID control is undertaken to create a fuzzy controller that satisfies the demands of stepper motor control. Additionally, the division of indices is conducted in accordance with the specifications of the fuzzy controller in order to mitigate the disruptive elements of PID control. then, the use of fuzzy control rules is employed to achieve control over the stepper motor, resulting in the development of an enhanced scheme that is then subjected to rigorous validation. The present study employs a MATLAB simulation to compare the performance of the enhanced fuzzy control algorithm with that of the P-ID control method. The results demonstrate that the improved fuzzy control algorithm significantly enhances the stability and dynamic performance of the stepper motor. Superior to traditional Proportional-Integral-Derivative (PID) control.

Control and Robust Stabilization at Unstable Equilibrium by Fractional Controller for Magnetic Levitation Systems

The problem of control and stabilizing inherently non-linear and unstable magnetic levitation (Maglev) systems with uncertain equilibrium states has been studied. Accordingly, some significant works related to different control approaches have been highlighted to provide robust control and enhance the performance of the Maglev system. This work examines a method to control and stabilize the levitation system in the presence of disturbance and parameter variations to minimize the magnet gap deviation from the equilibrium position. To fulfill the stabilization and disturbance rejection for this non-linear dynamic system, the fractional order PID, fractional order sliding mode, and fractional order Fuzzy control approaches are conducted. In order to design the suitable control outlines based on fractional order controllers, a tuning hybrid method of GWO–PSO algorithms is applied by using the different performance criteria as Integrated Absolute Error (IAE), Integrated Time Weighted Absolute Error (ITAE), Integrated Squared Error (ISE), and Integrated Time Weighted Squared Error (ITSE). In general, these objectives are used by targeting the best tuning of specified control parameters. Finally, the simulation results are presented to determine which fractional controllers demonstrate better control performance, achieve fast and robust stability of the closed-loop system, and provide excellent disturbance suppression effect under nonlinear and uncertainty existing in the processing system.

Disturbance estimator based dynamic compensator design for fractional order fuzzy control systems

The robust stabilization problem for singular fractional order time delay T-S fuzzy systems with nonlinearities and unknown external disturbances is addressed in this paper. An equivalent-input-disturbance (EID) estimator is used to estimate the impact of external disturbances and nonlinearities on the system output. Based on this EID approach, a dynamic compensator is designed to solve the stabilization problem of the considered system. Moreover, by considering a relevant Lyapunov-Krasovskii functional candidate and by using Lyapunov technique, the stability conditions in terms of LMIs are acquired for the considered closed-loop system. At last, to validate the effectiveness of the proposed result, two numerical examples are provided.

Renewable generation based hybrid power system control using fractional order-fuzzy controller

This work primarily focuses on electrical characteristics of a hybrid power system (HPS) incorporating renewable energy generation (REG) (HPSREG). The major components of HPSREG are the resources coordinated with multi-unit of photovoltaic cells, multi-unit of wind turbine generators, a diesel engine generator (DEG), energy storage system (ESS) with diverse nature and an electric vehicle (EV). The performance characteristics of HPSREG are determined by constant generation of power from the various sources as well as varying load perturbations. As the variation in load demand will introduce fluctuation in frequency and power with constant generation. In few of overcome the frequency and power deviation under both the above-mentioned generation and load demand conditions, proper control technique is required. In order to control the deviation in frequency and power, an integration in the environment of fractional order (FO) calculus for proportional–integral–derivative (PID) controller and fuzzy controller, termed with FO-Fuzzy PID controller tuned with quasi-opposition based harmonic search (QOHS) algorithm has been proposed. The results acquired with the proposed FO-Fuzzy-PID controller are then analyzed along with FO-PID and PID controller route for quantify effectiveness for the same under the considered cases to determine the effectiveness of the algorithm undertaken. Sensitivity investigation is also conducted in order to show the strength of the technique under study of differences in HPSREG parameters of magnitude.

Simulation And Analysis of An Intelligent Power System Stabilizer Using Fuzzy Logic

The basic aim of the power system stabilizer is to damp the fluctuations that occur on the rotating axis of the synchronous generator that result from noise or disturbance on the power system. This is achieved by producing an appropriate damping torque for these fluctuations across the excitation circuit of the generator and for a wide range of operation conditions. The study describes the types of power system stabilizers and giving an mathematical model of the power system that consists of a synchronous machine connected to the infinite bus though transmission lines. This has been achieved by simulating the electric and mechanical equations of power systems and proposing a methodological approach to design a Fuzzy Logic Power System Stabilize (FPSS) relaying in the design on the (Matlab/Fuzzy logic toolbox).Speed deviation (Δω) and acceleration (∆ ) of the synchronous machine are chosen as the input signals to the fuzzy controller in order to achieve a good dynamic performance .The complete range for the variation of each of the two controller inputs is represented by a 7×7 decision table, i.e. 49 rules using proportional derivative like fuzzy logic. The power system (SMIB) was tested with the presence and absence of the excitation system, then (CPSS) was added, and then (FPSS).The simulation results of the proposed fuzzy logic on )SMIB( gave a better dynamic response, decreased the settling time and good performance of the stabilizer in damping the fluctuations that arise in the speed of rotation of the generator and its active power in various operating conditions when proposed (FPSS) is compared with conventional PSS. The simulation results proved the superior performance of the proposed (FPSS).

Frequency and tie-line power awareness in eco-AGC of multi-area power system with SSO-based fractional order controller

The article focuses on how a conventional automatic generation control (AGC) will be economically efficient. AGC aims to keep system frequency and net scheduled interchanges between different control areas (tie-line power) within predetermined values. To generate power economically it is required to combine conventional AGC with economic load dispatch (ELD) which combined known as economic AGC (eco-AGC). In eco-AGC concern the deviation in frequency is brought back to zero but scheduled interchanges never comes to zero rather settled to a new value as power always flows from lower incremental fuel cost generating station to higher incremental cost generating station in response to load demand. To perform eco-AGC, this research article proposes a novel salp swarm optimization (SSO)-based fractional order fuzzy controller for reducing errors and to maintain system stable with economic efficient. To justify robustness of proposed controller different sensitive analysis has been done with wide variation of system parameters.

Frequency and tie-line power awareness in eco-AGC of multi-area power system with SSO-based fractional order controller

The article focuses on how a conventional automatic generation control (AGC) will be economically efficient. AGC aims to keep system frequency and net scheduled interchanges between different control areas (tie-line power) within predetermined values. To generate power economically it is required to combine conventional AGC with economic load dispatch (ELD) which combined known as economic AGC (eco-AGC). In eco-AGC concern the deviation in frequency is brought back to zero but scheduled interchanges never comes to zero rather settled to a new value as power always flows from lower incremental fuel cost generating station to higher incremental cost generating station in response to load demand. To perform eco-AGC, this research article proposes a novel salp swarm optimization (SSO)-based fractional order fuzzy controller for reducing errors and to maintain system stable with economic efficient. To justify robustness of proposed controller different sensitive analysis has been done with wide variation of system parameters.

Design of an interval type-2 fractional order fuzzy controller for a tractor active suspension system

Suspension systems are considered as critical parts of modern vehicles and are required in various types of tractors. To enhance drive comfort in case of road irregularities and isolate tractor user cabin from vibration performance, active suspension (AS) systems are utilized to handle its hydraulic actuator. In the present study, an interval type-2 fractional order fuzzy PID (IT2FOFPID) strategy was designed in tractor AS system to minimize the transmitted uneven surface fluctuations to the driver’s cabin. Therefore, biogeography-based optimization (BBO) algorithm was employed to regulate the parameters of controllers. To demonstrate the advantages of the IT2FOFPID controller, three different controllers involving type-1 fuzzy PID (T1FPID), fractional order fuzzy PID (FOFPID), and interval type-2 fuzzy PID (IT2FPID) control methods were considered. The validity of the proposed IT2FOFPID controller is demonstrated in comparative simulation results in the presence of various field disturbances. The findings revealed that the proposed controller makes outstanding yield in comparison to other controllers in the presence of road disturbances.

FOSSMC for AGC of Multi-Area Power System in Open Market Environment

This paper presents a Fractional Order based Second Order Sliding Mode Controller (FOSSMC) for Load Frequency Control (LFC) of Deregulated Multi Area Power System (DMAPS). In this paper a new control strategy have been proposed to improve dynamic response of multi area power system under one possible contract scenario by considering bilateral contracts. This proposed new FOSSMC strategy capable of dealing with frequency deviations for sudden change of load on DISCOs. The advantage of FOSSMC in terms of exhibiting dynamic response compared to group of factional order controllers such as Fractional Order PI(FOPI), Fractional Order Fuzzy Controller(FOFLC) and Fractional Order Sliding Mode Controller(FOSMC) and conventional PI controller.

Look-up table based fuzzy controller in order to control steaming room process

Introduction: In this paper, using look-up table control strategy,a fuzzy logic controller is designed for controlling the temperatureof steaming room in terrazzo tile plant corresponding to dedicatedprocess diagrams. Methods: In the proposed method, the temperature and error of temperature are considered as inputs to controlthe duration of valve open time to decrease the activation times ofvalves in order to increase their longevity. The strategy considersan off-line trained look-up table for setting the time of openingvalve in the specific temperature. A fuzzy controller with fifteen extracted rules is designed for controlling the duration of valve opentime. Results: Results show that the number of switching of valvereduces compare to intuitionistic or expert rule extraction. Conclusions: Simulations provide more compatible steaming process routcompare to PID controllers.

Fuzzy Control of Active Suspension System Using Full Car Model

Fuzzy logic technique has been proposed to control full car model based active suspension system. The full car model has been used to simulate the mathematical model of active suspension system. The performance of proposed controller mainly depends on body displacement, acceleration, roll and pitch angle of full car model. But the roll and pitch angle cannot be measured in quarter car model of active suspension system. The dynamic nature of suspension system and complex nonlinear characteristics of actuating system has increased the difficulty of creating mathematical model for active suspension system. In real time, the controller designed based on analytical method will not give better result due to its complex mathematical model. The fuzzy logic technique has able to give better performance for active suspension system irrespective of the complex nature of mathematical model of suspension system. This paper describes mathematical model of suspension system with fuzzy controller in order to obtain vehicle response for range of road input. The result of simulation will confirm the performance of fuzzy logic controller for active suspension system.DOI: http://dx.doi.org/10.5755/j01.mech.24.2.17457

Movement Stabilizing Using Afferent Control of Spinal Locomotor CPG Using Chaotic Takagi-Sugeno Fuzzy Logic Systems: A Simulation Study

Background: External control of the function of the central pattern generators (CPGs), exist in the spinal cord, is possible by electrical or chemical stimulation of some of the spinal Afferents. After restarting the activity of spinal cord CPG, the dynamics of movements such as gait can be changed through the time by controlling the rhythm of the CPG. Methods: The purpose of this study was provision of closed loop control algorithm based on the Takagi-Sugeno fuzzy controller in order to adjust the weight of the spinal cord afferents in a neuro-mechanical model with the aim of controlling the rhythm of the CPGs. Rhythm control of CPGs has been done with the aim of implementing the process of resetting the phase during gait in order to stabilize the movement against external disturbances. In this paper, the efficiency of a continuous Takagi-Sugeno fuzzy controller with the efficiency of 2 fuzzy Takagi-Sugeno chaotic controllers has been compared. Results: It was shown by the results obtained from the simulation that the process of resetting the motion phase of the skeletal angle in face of applying disturbance in method of chaotic Takagi-Sugeno fuzzy controller is done with good features in the presence of delayed feedback in decreasing overshoot and undershoot to the amount of 1.982 and 0.17 radians, respectively so that the best amount of afferent to reset the phase and return to the desired angle is provided at the shortest possible time. Conclusion: In this paper, the efficiency of a continuous Takagi-Sugeno fuzzy controller with the efficiency of 2 fuzzy Takagi-Sugeno chaotic controllers has been compared for movement stabilization using spinal cord afferent control. According to the results, the best performance was observed when the chaotic fuzzy Takagi-Sugenocontroller in the presence of delayed feedback was used.

SPIKING NEURAL NETWORKS BASED PID LIKE FLC DESIGN FOR AN IDLE SPEED CONTROL OF AN AUTOMOTIVE ENGINE

Automatic control of automotive engines provides benefits in the engines performance like emission reduction and fuel economy. The drop in idle speed problem can be seen as the disturbance rejection problem in the main engine speed. In this paper, a PID-like Fuzzy Logic Control (PIDFC) with minimum structure for the four strokes, four cylinders, gasoline engine is designed and simulated to maintain the engine speed at nominal value in idle speed mode. The speed performance must satisfy minimize fuel consumption, and as a result reduces the fuel emissions. A spiking Neural Network (SNN) trained by Particle Swarm Optimization (PSO) algorithm is proposed to online-adapt the inputs and output gains of the PID fuzzy controller in order to achieve the required speed performance. A Mean Value Engine Model (MVEM) is used to simulate nonlinear model of engine. .Results of simulation for this controller showed good improvements over the PIDFC in the idle speed response. .The peak overshoot is reduced about (70 %), the undershoot is reduced about (50 %), the settling time is. .reduced about (83%) and the fuel consumed is reduced about (53%).

Adaptive control of bilateral teleoperation system with compensatory neural-fuzzy controllers

This paper presents two adaptive neural-fuzzy controllers equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. To the first controller is applied compensatory neural-fuzzy inference (CNFI) and to the second compensatory adaptive neural fuzzy inference system (CANFIS). Each controller is incorporated into a two channel bilateral teleoperation architecture involving force-position scheme, which combines the position control of the slave system with force reflection on the master. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a one degree of freedom teleoperation system actuated by DC motors. The experimental results obtained show a fairly high accuracy in terms of position and force tracking, under free space motion and hard contact motion, what highlights the effectiveness of the proposed controllers.

Simulation of Multidimensional Non-Linear Processes Based on the Second Order Fuzzy Controller

Fuzzy controller is a multidimensional nonlinear static link. For the fuzzy controller synthesis it is sufficient to determine its desired static characteristic and adjust it so that it coincides with the desired one. The paper describes the synthesis of a multidimensional nonlinear model using the second order fuzzy controller with the required precision.

MEASURING THE DEGREE OF STRESS-STRAIN STATE STEEL ON THE BASIS OF SECOND ORDER FUZZY CONTROLLER

During the operation of electrical and power equipment under the influence of excess pressure, mechanical, thermal loads and other factors, there is an accumulation of fatigue and other damage to buildings and metallic structural elements, leading to leakage of adverse processes leading to equipment failures with serious consequences that affect fire and industrial safety. In this paper considered the use of electromagnetic-acoustic method together with the method of dynamic identification of stress-strain state and the level of damage to metal equipment and the application of integrated criteria for a comprehensive assessment of current condition of the process equipment. The measurement applies second order fuzzy control. The paper discusses the features of synthesis of fuzzy controller based on experimental data: the formation of linguistic variables input and output signals, synthesis algorithm of production rules. The application of production rules with a double consequent possible to synthesize the fuzzy controller with the required accuracy. High accuracy, smooth nonlinear multidimensional characteristics, ease of synthesis allow using this controller not only in technological process control systems, but also in solving simulation problems and creating multidimensional nonlinear dependencies. The paper deals with the optimization of the process of calculating the results of the measurement by a serial connection of fuzzy controllers. Since the fuzzy controller is a static unit, the serial connection will also be non-linear static unit, which can be represented as a fuzzy controller. Application of the second order fuzzy controller takes into account the effects of temperature and thickness of metal of the control object. As a result, electromagnetic acoustic method allows not only to assess the class of stress-strain state, but also to calculate the exact value of the internal stress of the metal.

Bare-bones imperialist competitive algorithm for a compensatory neural fuzzy controller

This study proposes a bare-bones imperialist competitive algorithm (BBICA) for a compensatory neural fuzzy controller in order to solve prediction problems and nonlinear control system problems. In the proposed BBICA, the population is divided into two types: imperialists and colonies. The purpose is that the cultures of colonies and societies can be similar to their empires. Moving colonies toward their relevant imperialist refers the assimilation policy. This study focuses on improving and exploring the process of assimilation. In the assimilation policy, there are two main control parameters for adjusting the direction of motion of colonies, i.e., location and angle. This study proposes the BBICA in order to reduce the use of the control parameters. Therefore, the proposed BBICA adopts a Gaussian distribution to improve the assimilation policy and the exploration ability of the global solution. Finally, the experimental results show that the proposed BBICA better approximates the global optimal solution and that it has faster convergence compared with other methods.

Fractional order fuzzy control of hybrid power system with renewable generation using chaotic PSO

This paper investigates the operation of a hybrid power system through a novel fuzzy control scheme. The hybrid power system employs various autonomous generation systems like wind turbine, solar photovoltaic, diesel engine, fuel-cell, aqua electrolyzer etc. Other energy storage devices like the battery, flywheel and ultra-capacitor are also present in the network. A novel fractional order (FO) fuzzy control scheme is employed and its parameters are tuned with a particle swarm optimization (PSO) algorithm augmented with two chaotic maps for achieving an improved performance. This FO fuzzy controller shows better performance over the classical PID, and the integer order fuzzy PID controller in both linear and nonlinear operating regimes. The FO fuzzy controller also shows stronger robustness properties against system parameter variation and rate constraint nonlinearity, than that with the other controller structures. The robustness is a highly desirable property in such a scenario since many components of the hybrid power system may be switched on/off or may run at lower/higher power output, at different time instants.

Integrated Motion Planning and Control for Multi Objectives Optimization and Multi Robots Navigation

This study proposes model of the optimal motion planner and controller for multi mobile robots. The proposed controller is responsible for avoiding the collision among multi robots after generating the optimal trajectory in term of multi objective for each robot. This work includes two stages: the first is to generate multi objective optimal path and trajectory for each robot independently from the start to the goal position using the modified genetic algorithm (MGA) with A∗ search algorithm (A∗). The second one is to establish a movement strategy to let the robots navigate with avoiding collision risk with each other by introducing Sugeno first order fuzzy controller. Five objective functions have been proposed in this study for minimizing travelling length, time, smoothness, security and trajectory and reducing the energy consumption for each mobile robot by using Cubic Spline interpolation curve fitting for optimal planned path. The simulation results show that the multi mobile robots navigate successfully with avoiding static obstacles as well as avoiding the collision between each other.

Coordinated signal control for arterial intersections using fuzzy logic

Abstract Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people’s time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.