Self-tuning Fuzzy Research Articles

An Optimized and Improved STF-PID Speed Control of Throttle Controlled HEV

An improved self-tuning fuzzy proportional–integral–derivative (ISTF-PID), fuzzy logic-based PID (FPID) and optimal PID controllers for speed control of nonlinear hybrid electric vehicle (HEV) are proposed in this paper. The performances of HEV with ISTF-PID, FPID and optimal PID controllers are compared with the performance of HEV with existing self-tuning fuzzy PID and conventional PID controllers. The gains of PID, FPID and ISTF-PID controllers are tuned using multiobjective genetic algorithm. The performance specifications such as integral of the absolute error, integral of the square of error, peak overshoot, rise time and settling time are considered as objective function of GA and for performance analysis of HEV with designed controllers. The proposed control techniques are designed to achieve the variable speed, fuel economy, reduced pollution and improved efficiency under uncertain environment.

자기동조 퍼지 제어기를 이용한 스위치드 릴럭턴스 모터의 전류제어

본 논문은 가격이 저렴하고 친환경적이면서도 고성능, 고내구성, 구조적 단순함의 장점을 갖고 있어 최근에 폭넓은 관심을 받고 있는 스위치드 릴럭턴스 모터(SRM: Switched Reluctance Motor)의 보다 정확하고 안정적인 전류제어 방법에 대해 설명한다. 대부분의 전동기의 전류제어 방법에는 알고리즘과 제어 이득의 선정이 다른 제어기에 비해 상대적으로 간편한 PI 제어기를 이용한 방법이 주로 사용되어 왔다. 그러나 일반적인 PI 제어기는 SRM과 같이 고정자 권선의 전류 및 회전자의 위치마다 비선형적으로 파라미터가 급변하는 시스템의 경우 변하는 동작 지점마다 제어 이득을 조정해 주어야 하는 어려움이 발생한다. 본 논문에서는 비선형적으로 특성이 변하는 SRM 드라이브 시스템에 제어 성능이 우수한 자기동조 퍼지 제어기를 이용한 제어기법을 적용하여 비선형적인 파라미터의 변화에도 보다 안정적인 전류제어가 가능한 것을 보였다. 또한 Matlab/Simulink 시뮬레이션을 이용하여, SRM 드라이브의 전류제어에 PI 전류 제어기(PICC: PI Current Controller)와 자기동조 퍼지 전류 제어기(STFCC: Self-tuning Fuzzy Current Controller)를 각각 적용한 후 그 결과를 비교하였으며 제안한 자기동조 퍼지 제어기의 제어성능이 우수함을 확인하였다. This paper describes an accurate and stable current control method of switched reluctance motors(SRMs), which have recently attracted considerable wide attention owing to their favorable features, such as high performance, high durability, structural simplicity, low cost, etc. In most cases, the PI controllers(PICC) have been used mostly for the current control of electric motors because their algorithm and selection of controller gain are relatively simpler compared to other controllers. On the other hand, the PI controller requires an adjustment of the controller gains for each operating point when nonlinear system parameters change rapidly. This paper presents a stable current control method of an SRM using self-tuning fuzzy current controller(STFCC) under nonlinear parameter variation. The performance of the considered method is validated via a dynamic simulation of the current controlled SRM drive using Matlab/Simulink program.

Heading Control for a Robotic Dolphin Based on a Self-Tuning Fuzzy Strategy

In this paper, a heading controller based on a self-tuning fuzzy strategy for a robotic dolphin is proposed to improve control accuracy and stability. The structure of the robotic dolphin is introduced and the turning motion is analysed. The analytic model indicates that the turning joint angle can be employed for the heading control. This non-linear model prevents the successful application of traditional model-based controllers. A fuzzy controller is proposed to realize the heading control in our work. It should be mentioned that the traditional fuzzy controller suffers from a distinguished steady-state error, due to the fact that the heading range is relatively large and the fuzzy controller's universe of discourse is fixed. To resolve this problem, a self-tuning mechanism is employed to adjust the input and output scaling factors according to the active working region in pursuit of favourable performance. Experimental results demonstrate the performance of the proposed controller in terms of steady-state error and robustness to interferences.

Pressure Regulation of Multi-level Gas Tank by Variable Universe Fuzzy Self-tuning Control

Pressure Regulation of Multi-level Gas Tank by Variable Universe Fuzzy Self-tuning Control

Optimal IPFC damping controller design based on simplex method and self-tuned fuzzy damping scheme in a two-area multimachine power system

This paper develops a novel self-tuned fuzzy damping control scheme for an interline power flow controller (IPFC) to suppress the interarea mode of oscillations in a multimachine power system. The nonlinear adaptive damping controller is based on coordinated operation of two fuzzy inference systems. The first one produces the required $q$-axis voltage reference of the quasi multipulse series converter in response to generator angle oscillations, while the second one is used to tune the output of the first one online for further reducing the error signal using a given set of fuzzy rules. The simplex method is employed to search for optimal gains of the damping controller by minimizing the objective function in which speed deviations between generators are formulated. The feasibility of the proposed technique is validated using time-domain simulation cases in the PSCAD simulation program. It is also shown that the proposed damping scheme for IPFC works better than the static synchronous series compensator, which utilizes the same damping scheme in reducing the interarea mode of oscillations.

Evaluation of Fuzzy Inference-based Self-tuning of Steering Control Gains for Heavy-duty Trucks

As the number of automobiles in worldwide increases, the number of associated serious environmental and safety problems also increases. A solution to these problems is an autonomous platooning system for trucks, which is expected to have various effects such as reduction in carbon dioxide emissions and increase in traffic capacity. Although various control laws have been proposed for automated driving, control gains are typically tuned manually. The optimal values change owing to the freight or over a period of several years. Therefore, when the control performance decreases, gain tuning is again required. In this study, as additional evaluations of the proposed self-tuning method, we newly examine if the method can adjust gain to the change of a freight weight by simulation and evaluate the convergence property of the method experimentally.

Design of Self-Tuning Fuzzy Control and Fault Tolerant Error Control Coding Based on Graphic User Interface for Smart Hybrid Powerpack

The Smart Hybrid Powerpack (SHP) is an electro-hydraulic system which combines the system of Electro Hydraulic Actuator (EHA) and advanced technologies such as network fault tolerance and intelligent control. EHA system has been famous in the industry because that the EHA acts as a power-shift which shifts the power from high-speed electric motor to the high-force of hydraulic cylinder by bi-directional piston pump. If errors in the plant and network occur in the SHP, the system will cause serious malfunctions. To reduce plant noises and network errors, this paper shows the intelligent control method comparing Self-tuning fuzzy with fuzzy control and network fault tolerant error control coding in the SHP. In the intelligent control part, the simulation result shows good performance to reduce plant noises by the self-tuning fuzzy than fuzzy control. In the network fault tolerant error control coding part, proposed scheme also shows good performance by CRC code and Reed-Solomon (R-S) code in two channel (CRT) method than one channel only. We developed LabVIEW Graphic User Interface (GUI) to show these simulation results. Using this GUI, we can save time to experiment and get benefit of guidance to make real program.

Design and Simulation Analysis on Self-Tuning Fuzzy Controller Based on Scaling Factor and Quantitative Factor

Along with the development of modern science and technology information, fuzzy control theory based on its unique advantage becomes more and more popular with scientists and engineers, therefore it is quickly applied to all walks of life, and is one of the advanced teaching theory introduced in educational industry. Firstly, on the basis of mapping between classical set and fuzzy set, the paper comprehensively analyzes the intelligent fuzzy control theory. Based on the improved fuzzy controller which is self-correcting through scale factor and quantification factor, we establishes the fuzzy control model of moral education. In order to verify the feasibility of the model, we design the fuzzy controller to carry out the applied simulation by using the MATLAB fuzzy logic toolbox, and the results show that the system has fast response, strong adaptability, and good steady-state performance.

PID Parameter Fuzzy Self-Tuning of the Motion Control System

In the motion control system, the fuzzy self-tuning of PID parameter is the combination of fuzzy control strategy and traditional PID control, and it is an effective method to improve the control precision. The paper discussed the principle of fuzzy self-adapted PID controller and the parameter fuzzy adjustment, and it realized self-tuning online of PID parameter for the motion control system by using a fuzzy inference method, and the controller was simulated in application of motion control system under the environment of Matlab/Simulink. The simulation result indicated the self-adaptive PID controller enable the motion control system to achieve the satisfied effect.

Self-Tuning Closed-Loop Fuzzy Logic Control Algorithm for Adaptive Prestressed Structures

Constantly increasing traffic loads, unpredictable influences on and unplanned conditions of bridges represent new challenges in the field of engineering. Innovative strategies and construction techniques are required to meet these and future challenges. Adaptive prestressing offers great potential to increase a structure’s load-carrying capacity and to optimize its load-induced stresses. In this manner, cost efficiency and sustainability of constructions can be enhanced. Therefore, a control algorithm with high reliability and redundancy is required to meet the new challenges. In this paper, an adaptive fuzzy logic-based closed-loop control system for realization of adaptive prestressed structures is presented. Based on the new concept, two different specialized control algorithms are developed. In order to decrease deflections, the prestressing force of an aluminium truss is controlled. The algorithm includes a fuzzy system with model-based knowledge base. The learning process is established on direct synchronization of the calculated deflections of the model with the measured reactions of the structure. Another fuzzy system for adaptive prestressing of a reinforced concrete T-beam uses a model-free knowledge base. Optimization of load-induced stresses is accomplished utilizing expert knowledge. An incremental learning process is performed in accordance with the algorithm’s basic rules. Experiments on the two prototypes are conducted to investigate the functionality of the developed algorithms. In addition to efficiency, high reliability and redundancy are shown by the control system. In this article, the potential of fuzzy-controlled adaptive prestressing is illustrated.

A Self-Tuning Fuzzy Rule-Based Classifier for Data Streams

In recent years, tremendous amounts of data streams are generated in different application areas. The new challenges in these data need fast and online data processing, especially in classification problems. One of the most challenging problems in field of data streams that reduces the performance of traditional methods is concept change. To handle this problem, it is necessary to update the classifier system after every alteration of the concept of data. However, updating a classifier can often be a time consuming and expensive process. In this paper, an efficient method is proposed for quickly and easily updating of a fuzzy rule-based classifier by setting a weight for each rule. Then, two online procedures for online adjustment of the rule weights are proposed. The experimental results show the high performance of these methods against a non-weighted approach.

Frequency regulation for large load variations on micro-hydro power plants with real-time implementation

Micro-hydro power plants (MHPP) are usually built on mountains to provide electricity for rural communities. However, most of them are isolated from the national grid; thus, they require good control system to ensure the stability of both MHPP frequency and voltage outputs in spite of varying users load. This paper treats a new approach for frequency regulation of a MHPP against large load variations. For our simulation studies, a MHPP model is constructed from the mains physical equations that describe the nonlinear behavior of the plant. A self-tuning fuzzy proportional–integral (PI) controller, in which the controller gains are adjusted in real-time using a fuzzy logic inference system, is proposed. To demonstrate the effectiveness of the proposed controller, practical results under different loading conditions are presented. Those results show overall a good correlation between the computer simulation and the field-testing data.

Feedback linearization-based self-tuning fuzzy proportional integral derivative control for atmospheric pressure simulator

A new robust nonlinear controller is proposed to improve the performance of the atmospheric pressure simulator that has some special characteristics such as asymmetry and nonlinearity. The three major components in such systems, the chamber, the servo valve and the vacuum pump, are studied to develop a full nonlinear model which encompasses all the major nonlinearities. Based on the model expressed in the controllability canonical form, a feedback linearization controller is developed to handle the strong asymmetry and nonlinearity of the atmospheric pressure simulator. Considering the parametric uncertainties and un-modeled dynamics existing in the atmospheric pressure simulator, a self-tuning fuzzy proportional integral derivative controller integrating with feedback linearization is introduced to improve the performance of the atmospheric pressure simulator at high-altitude simulations. Simulations and experiments indicate that the proposed controller can effectively raise the dynamic response performance, and in the meantime stability can be ensured.

A New Self-Tuning Fuzzy PD Controller of a BDFIG for Wind Energy Conversion

A New Self-Tuning Fuzzy PD Controller of a BDFIG for Wind Energy Conversion

Semiactive Self-Tuning Fuzzy Logic Control of Full Vehicle Model with MR Damper

Intelligent controllers are studied for vibration reduction of a vehicle consisting in a semiactive suspension system with a magnetorheological(MR) damper. The vehicle is modeled with seven degrees of freedom as a full vehicle model. The semiactive suspension system consists of a linear spring and an MR damper. MR damper is modeled using Bouc-Wen hysteresis phenomenon and applied to a full vehicle model. Fuzzy Logic based controllers are designed to determine the MR damper voltage. Fuzzy Logic and Self-Tuning Fuzzy Logic controllers are applied to the semiactive suspension system. Results of the system are investigated by simulation studies in MATLAB-Simulink environment. The performance of the semiactive suspension system is analyzed with and without control. Simulation results showed that both Fuzzy Logic and Self-Tuning Fuzzy Logic controllers perform better compared to uncontrolled case. Furthermore, Self-Tuning Fuzzy Logic controller displayed a greater improvement in vibration reduction performance compared to Fuzzy Logic controller.

Hybrid Fuzzy-Sliding Control with Fuzzy Self-Tuning for Vector Controlled Drive Systems

The purpose of this study was to develop a hybrid fuzzy-sliding controller with fuzzy self-tuning (HFSC). This controller used a fuzzy supervisory system to allocate the output proportions of a sliding-mode controller and a fuzzy controller (FC). The sliding-mode controller primarily provides rapid control efforts in the transient state, and the FC mainly offers smooth control in the steady state and decreases the chatter phenomenon caused by the sliding-mode controller. Finally, the proposed HFSC was implemented in the vector controlled drive system of induction motor as the speed controller. The experimental results showed that the tracking performance and effects of the HFSC were superior to those of the FC.

Missile Autopilot Design Based on Fuzzy Self-Tuning Immune PID Control

A design of the fuzzy self-tuning immune PID controller is proposed. The controller, which combines immune feedback mechanism and fuzzy PID control, executes the self-tuning of PID parameters in the control system. A simulation experiment is presented in light of a tactical missile autopilot transfer function. Simulation result shows that the performance of fuzzy self-tuning immune PID controller is better than that of fuzzy PID controller, especially in its robust performance for variable parameter system.

A Self-Tuning Fuzzy Logic Controller for Aircraft Roll Control System

In this paper, an aircraft roll control system based on autopilot operating conditions is modeled and simu lated using Matlab/Simulink. The modeling phase begins with the derivation of required mathematical model to describe the lateral d irectional motion of an aircraft. Then, Linear Quadratic Regulator (LQR), Fuzzy Logic Controller (FLC) and Self-Tuning Fu zzy Logic Controller (STFLC) are applied for controlling the roll angle o f the modeled aircraft system. Simu lation results of ro ll controllers are presented in time do main and the results obtained with STFLC are co mpared with the results of FLC and LQR. Finally, the performances of roll control systems are analysed in order to decide which control method gives better performance with respect to the desired roll angle. According to simu lation results, it is shown that STFLC deliver better performance than FLC and LQR.

Control of a Quadrotor Using a Smart Self-Tuning Fuzzy PID Controller

This paper deals with the modelling, simulation-based controller design and path planning of a four rotor helicopter known as a quadrotor. All the drags, aerodynamic, coriolis and gyroscopic effect are neglected. A Newton-Euler formulation is used to derive the mathematical model. A smart self-tuning fuzzy PID controller based on an EKF algorithm is proposed for the attitude and position control of the quadrotor. The PID gains are tuned using a self-tuning fuzzy algorithm. The self-tuning of fuzzy parameters is achieved based on an EKF algorithm. A smart selection technique and exclusive tuning of active fuzzy parameters is proposed to reduce the computational time. Dijkstra's algorithm is used for path planning in a closed and known environment filled with obstacles and/or boundaries. The Dijkstra algorithm helps avoid obstacle and find the shortest route from a given initial position to the final position.

Self-Tuning Fuzzy PI-Type Controller in Z-Source Inverter for Hybrid Electric Vehicles

This paper presents new algorithms to control speed induction motor (SIM) and the peak dc-link voltage (PDV) across the inverter bridge in z-source inverters (ZSI) by applying self-tuning fuzzy PI controller (SFP) with robust structure and non-linear characteristic. In particular, this so-called SFP based control algorithm (SFPA) is applied to a closed loop speed control system of induction motor, which relies on direct torque control scheme combined with modified space vector modulation (DTCMSVM) control strategy with so many exceptional features (e.g. fast torque response, low steady state torque ripple, and high accurate). Additionally, SFPA is used to control SIM and PDV are more adaptive to the sudden change of parameters such as load torque, stator resistance and dc input voltage (DIV), respectively. The transient response of SIM and PDV are thus improved with less over shoot, short rise time, small steady-state error and fast settling time, with low disturbance for output voltage stabilization in the inverter bridge. As a result, we achieve higher accuracy and robustness of SIM control system. Our new SFPA is verified in both simulation and experimental implementation using MATLAB and dSPACE DS1103, respectively. DOI: http://dx.doi.org/10.11591/ijpeds.v2i4.571