PD-type Fuzzy Controller Research Articles

PID-Fuzzy control of air handling units in the presence of uncertainty

Air-handling unit (AHU) is one of the most complex installations that its control is necessary for maintaining satisfactory comfort conditions in buildings with low energy utilization. In this paper, a multivariable nonlinear and minimum phase model of the AHU is considered in the presence of uncertainties. The controller design is divided into two types. The first controller type is for the linearized plant based on decoupled PID-Fuzzy and PD-type Fuzzy controllers. The second type is based on full matrix PID-Fuzzy and pole-placement controllers in which interaction effects are considered. The indoor temperature and relative humidity are controlled via manipulation of valve positions of air and cold water flow rates. Attainment of tracking goals is inspected through various desired commands of indoor temperature and relative humidity. According to the results, all of the controllers are efficient but the decoupled PD-type Fuzzy controller is the worst controller in tracking of desired commands. In comparison with other controllers, the decoupled PID-Fuzzy controller guarantees the robust performance against the model-based parametric uncertainties. In addition, using pole-placement controller leads to the less energy consumption in comparison with the other controllers.

Application of a self-tuning fuzzy PI–PD controller in an active anti-roll bar system for a passenger car

A fuzzy proportional-integral-derivative (PID) controller has not been widely investigated for active anti-roll bar (AARB) application due to its unspecific mathematical analysis and the derivative kick problem. This paper briefly explains how the derivative kick problem arises due to the nature of the PID controller as well as the conventional fuzzy PID controller in association with an AARB. There are two types of controllers proposed in this paper: self-tuning fuzzy proportional-integral–proportional-derivative (STF PI–PD) and PI–PD-type fuzzy controller. Literature reveals that the PI–PD configuration can avoid the derivative kick, unlike the standard PID configuration used in fuzzy PID controllers. STF PI–PD is a new controller proposed and presented in this paper, while the PI–PD-type fuzzy controller was developed by other researchers for robotics and automation applications. Some modifications were made on these controllers in order to make them work with an AARB system. The performances of these controllers were evaluated through a series of handling tests using a full car model simulated in MATLAB Simulink. The simulation results were compared with the performance of a passive anti-roll bar and the conventional fuzzy PID controller in order to show improvements and practicality of the proposed controllers. Roll angle signal was used as input for all the controllers. It is found that the STF PI–PD controller is able to suppress the derivative kick problem but could not reduce the roll motion as much as the conventional fuzzy PID would. However, the PI–PD-type fuzzy controller outperforms the rest by improving ride and handling of a simulated passenger car significantly.

Implementation of Fuzzy Logic Controller for Wheelchair Motion Control Based on EOG Data

The study of this paper is to implementation the fuzzy logic control designed for wheelchair motion based on the eye movement signals using electrooculograhphy (EOG) technique. This technique is to acquire the eye movement data from a person, for example, tetraplegia. The tetraplegia is paralysis caused by illness or injury to a human that result in the partial or total loss of use of all their limbs and torso. The eye movement data which was obtained can be used as a main communication tool between human and machine. The PD-type fuzzy controller was successfully designed and tested on the wheelchair model, for control the linear motion (focused for forward motion). The wheelchair model was developed using MSC.Visual Nastran 4D. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set by the EOG signal.

Implementation of Wheelchair Motion Control Based on Electrooculography Using Simulation and Experimental Performance Testing

The aim of this study is to perform the experimental verification on the fuzzy-based control designed for wheelchair motion. This motion control based on the eye movement signals using electrooculograhphy (EOG) technique. The EOG is a technique to acquire the eye movement data from a person, i.e tetraplegia, which the data obtained, can be used as a main communication tool. This study is about the implementation of the designed controller using PD-type fuzzy controller and tested on the hardware of the wheelchair system using the eye movement signal obtained through EOG technique as the motion input references. The results obtained show that the PD-type fuzzy logic controller designed has successfully managed to track the input reference for linear motion set (forward and backward direction) by the EOG signal.

자기동조에 의한 PD 형 퍼지제어시스템의 응답 개선

This study proposes a method for improvement of PD type fuzzy controller. The method includes self tuner using gradient algorithm that is one of the optimization algorithms. The proposed controller improves simple Takagi-Sugeno type FLC (Fuzzy Logic Control) system. The simple Takagi-Sugeno type FLC system changes nonlinear characteristic to linear parameters of consequent membership function. The simple FLC system could control the system by calibrating parameter of consequent membership function that changes the system response. While the determination on parameter of the simple FLC system works well only partially, the proposed method is needed to determine parameters that work for overall response. The simple FLC system doesn't predict the response characteristics. While the simple FLC system works just like proportional part of PID, our system includes derivative part to predict the next response. The proposed controller is constructed with P part and D part FLC system that characteristic parameter on system response is changed by self tuner for effective response. Since the proposed controller doesn't include integral part, it can't eliminate steady state error. So we include a gain to eliminate the steady state error.

Direct Torque Control System of Induction Motors with a Fuzzy Self-Adaptive Speed Controller

Conventional direct torque control has some disadvantages. It has the property of hysteresis, which only takes two value controls regardless of how much the error is. A simulation model of fuzzy direct torque control system for induction motors is built. And a fuzzy direct torque control system with an adaptive PD-type fuzzy logic speed controller is designed. The simulation result proves that this method is reasonable and offers a new thought way for designing and debugging actual control system of induction motors.

A systematic design methodology of PD fuzzy logic controller using cellular fuzzy logic concept

The present paper proposes a new approach for designing PD type fuzzy controllers, which employs the idea of fuzzy input space partitioning using the cellular fuzzy logic (CFL) framework, deploying the dynamical behaviour of the system to be controlled. The objective is to design PD type fuzzy controllers, determining both its structures and free parameters, such that the designed controller can provide satisfactory transient performance. A novel algorithm is presented to design the generalised proportional derivative fuzzy logic controller (PD-FLC). The proposed design methodology is detailed for the synthesis of both the Mamdani type and Takagi-Sugeno (T-S) type fuzzy controller equivalent to a PD control action. The validity of the design procedure is demonstrated by simulation case studies.

A PLC-based modified-fuzzy controller for PWM-driven induction motor drive with constant V/Hz ratio control

In this paper, a method to develop and design a fuzzy-hybrid control on an industrial controller to control speed of an induction motor and implementing a constant V/Hz ratio control is presented. Detailed discussions on the controller for a PWM-driven induction motor drive system, the system identification for the model transfer function, and the analysis on output responses and the associated manipulated variables are presented. The control objective is to provide an effective control action to sudden changes in reference speed and/or load torque. A switching type controller consisting of two control modes are devised: a PID-type fuzzy controller consisting of a PI-type and a PD-type fuzzy controller, and a conventional PID. At the early phase of the control action, the control task is handled by the PID-type fuzzy controller. At a later phase when the absolute of error is less than a threshold value, the input of integrator at the output side is no longer given by fuzzy action but fed by the incremental PID action. In term of control action, this is an enhanced proportional and derivative action when the actual value is closed to reference. Detailed evaluations of the controller's performance based on pre-defined performance indices under several conditions are presented. The findings demonstrate the ability of the control approach to provide a viable control solution in response to the different operating conditions and requirements.

Hybrid fuzzy controller for speed control of switched reluctance motor drives

A hybrid fuzzy controller for speed control of switched reluctance motor (SRM) drives is presented in this paper. The developed hybrid fuzzy control law consists of a proportional integral (PI) controller at steady state, a PI type fuzzy logic controller (FLC) at transient state and a simple logic controller between the steady and transient states to achieve the desired performance at various operating conditions under soft chopping operation. The importance of the hybrid fuzzy controller is highlighted by comparing the performance of various control approaches, including PI control, PI type fuzzy logic control and PD type fuzzy control for speed control of SRM motor drives. The complete control algorithm is demonstrated by intensive experimental results. It is shown that the presented hybrid controller for SRM drive has fast tracking capability, less steady state error and is robust to load disturbance. The complete speed control scheme of the SRM drive incorporating the hybrid control is experimentally implemented and validated using a high speed digital signal processor board TMS320F2812 for a prototype 1.2 kW SRM.

04/01536 Representative building design and internal load patterns for modelling energy use in residential buildings in Hong Kong: Wan, K. S. Y. and Yik, F. H. W. Applied Energy, 2004, 77, (1), 69–85

Air-handling unit (AHU) is one of the most complex installations that its control is necessary for maintaining satisfactory comfort conditions in buildings with low energy utilization. In this paper, a multivariable nonlinear and minimum phase model of the AHU is considered in the presence of uncertainties. The controller design is divided into two types. The first controller type is for the linearized plant based on decoupled PID-Fuzzy and PD-type Fuzzy controllers. The second type is based on full matrix PID-Fuzzy and pole-placement controllers in which interaction effects are considered. The indoor temperature and relative humidity are controlled via manipulation of valve positions of air and cold water flow rates. Attainment of tracking goals is inspected through various desired commands of indoor temperature and relative humidity. According to the results, all of the controllers are efficient but the decoupled PD-type Fuzzy controller is the worst controller in tracking of desired commands. In comparison with other controllers, the decoupled PID-Fuzzy controller guarantees the robust performance against the model-based parametric uncertainties. In addition, using pole-placement controller leads to the less energy consumption in comparison with the other controllers.

Real Time Hybrid Controller Implementation for Switched Reluctance Motor Drive

This study presents the real time hybrid controller implementation for a Switched Reluctance motor Drive. The developed hybrid control law consists of Proportional Integral (PI) controller at steady state, PI-type fuzzy logic controller (FLC) at transient state and a simple logic controller between steady and transient states to achieve desired performance at various operating conditions under soft chopping operation. The importance of hybrid controlling is highlighted by comparing the performance of various control approaches, including PI control, PI-type fuzzy logic control, PD type fuzzy control for speed control of SRM motor drives. The complete control algorithm is demonstrated by intensive experimental results. It is shown that the presented hybrid controller for SRM drive is with fast tracking capability, less steady state error and robust to load disturbance. The complete speed control scheme of the SRM drive incorporating the hybrid control is experimentally implemented and validated using a high speed digital signal processor board TMS320F2812 for a prototype 1.2- kW SRM.

A new fuzzy logic learning control scheme for repetitive trajectory tracking problems

In this paper, we consider repeatable tracking control tasks using a new control approach—PD type fuzzy logic learning control (FLLC). FLLC integrates two main control strategies: fuzzy logic control as the basic control part and learning control as the refinement part. The new FLLC is constructed by simply adding an iterative learning mechanism to a fuzzy PD controller. The incorporation of the learning function into fuzzy PD controllers ensures exact tracking because it completely nullifies the effects of reference signal and periodic disturbances on the tracking error. Through the rigorous proof based on energy function and functional analysis, we show that the proposed FLLC system achieves the following novel properties: (1) the tracking error sequence converges uniformly to zero; (2) learning control sequence converges to the desired control profile almost everywhere.

오차적분 적용계수를 이용한 PD+I 퍼지제어기

In this paper, we Propose a PD+I fuzzy controller using an error-accumulating applying factor. In fuzzy control, analytical study was done formerly, in which fuzzy control can be classified by PD type and PI type, and also the study for getting merits of both types was done, too. But the mixed type has a complex structure and many parameters. The proposed fuzzy controller is 2-input 2-out-put and PD type fuzzy control is used as a basic structure. And the proposed controller annihilates a steady-state error and improves transient responses because of using the error-accumulating applying factor which is determined in the real time along the current state of controlled process. Futhermore it is easy to tune the system because of decreasing the number of scaling factors and the I type controller with resetting resolves the integral wind-up problem. Finally we apply the proposed scheme to various plants and show the performance betterment.

A robust self-tuning scheme for PI- and PD-type fuzzy controllers

Proposes a simple but robust model independent self-tuning scheme for fuzzy logic controllers (FLCs). Here, the output scaling factor (SF) is adjusted online by fuzzy rules according to the current trend of the controlled process. The rule-base for tuning the output SF is defined on error (e) and change of error (/spl Delta/e) of the controlled variable using the most natural and unbiased membership functions (MFs). The proposed self-tuning technique is applied to both PI- and PD-type FLCs to conduct simulation analysis for a wide range of different linear and nonlinear second-order processes including a marginally stable system where even the well known Ziegler-Nichols tuned conventional PI or PID controllers fail to provide an acceptable performance due to excessively large overshoot. Performances of the proposed self-tuning FLCs are compared with those of their corresponding conventional FLCs in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error and integral-of-time-multiplied absolute error, in addition to the responses due to step set-point change and load disturbance and, in each case, the proposed scheme shows a remarkably improved performance over its conventional counterpart.

Fuzzy position control of hydraulic robots with valve deadbands

The development of a real-time fuzzy-logic controller for a class of industrial hydraulic robots is described. The main element of the controller is a PD-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. The controller, although effective, is shown to produce steady-state errors. The steady-state error in a step input response is mainly due to the hydraulic valve deadband in which the control action is not effective. The steady-state position error in a ramp input response is due to both valve deadband and the nature of the controller, since zero position and velocity errors would result in the controller closing the valve and stopping the motion. Conceptually simple actions are then identified to be applied in parallel with the above PD-type fuzzy controller in order to eliminate the steady-state errors. In particular, two methods are developed. In one method, a control term proportional to the integral of the error is calculated and added to the output from the main controller. In the second method, the control measure of the steady-state error obtained from the PD-type fuzzy controller is used as an offset. Both actions are activated only in the region of fuzzy zero velocity error. The performances of the techniques developed in this paper are evaluated experimentally. The experiments are performed on an instrumented Unimate MK-II industrial hydraulic robot which has been retrofitted as a research robot.

Fuzzy Control of a Nonlinear Servomotor Model

A nonlinear servomotor model with friction, saturation, backlash, and motor starting voltage is presented in this article. Fuzzy control and nonlinear PID control are compared using numerous computer simulations. A systematical method for tuning a PD-type fuzzy controller with training data is introduced.