A Saturation-Based Tuning Method for Fuzzy PID Controller

Duan, Xiaogang; Deng, Hua; Li, Han-Xiong

doi:10.1109/tie.2012.2222858

Cited by 69 publications

(34 citation statements)

References 32 publications

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“…In [15] authors have given a method for pole placements using PID controllers. In [16] authors proposed a method for tuning of fuzzy based PID controller. In [17] authors have presented an approach for genetically tuning of PID controller.…”

Section: *Author For Correspondencementioning

confidence: 99%

Tracking control of parallel manipulator with 3-DOF

Chaudhary¹,

Ohri²

2016

IJATEE

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A manipulator is fundamentally a reprogrammable, multifunctional mechanical arm intended to move parts, tools, and specialized device through preprogrammed motions for the performance of various tasks. With the advances in technology over last decades Parallel manipulator became more popular for industrial purpose. Parallel manipulators are used for variety of tasks such as flight simulator [1], medical tools [2-4], pick and place operations. The kinematics of parallel manipulator differs from serial manipulator.Parallel manipulator consists of two platforms one of which is fixed and another is moving platform. These two platforms are connected to each other with the help of limbs. These limbs may consist of revolute or translational joints. Each limb is driven individually. Such type of structure results in closed kinematic mechanism. The kinematic analysis of parallel manipulator is difficult and it has been focus of many researchers [5][6][7][8]. The trajectory control of parallel mechanism like this present unique challenges because the dynamics of parallel manipulator are highly coupled and nonlinear. The precision depends on the accuracy of each actuator as well as on the synchronization of the actuators. In last few decades researchers have focused on optimal control theory and formulated the well-known optimal state feedback controller known as linear quadratic regulator, this approach reduces the deviation in state trajectories of a system maintaining minimum control effort. A very detailed study of optimal control is presented in [18]. To get the optimal control signal algebraic Riccati equation Research Article AbstractA parallel three degrees of freedom (3-DOF) manipulator known as Maryland manipulator is considered in this paper. The manipulator model considered in this paper is more practical because the offset lengths are not taken zero. The trajectory tracking control of the Maryland manipulator is done using linear-quadratic regulator (LQR) based proportional-integral-derivative (PID) controller. Three sequential trajectories are used to provide complete dynamic analysis of the manipulator. The manipulator considered here is more practical because the offset lengths are not taken zero unlike previous research articles. The motivation behind using PID controller is its simplicity and effectiveness. The manipulator is a highly coupled and nonlinear system so the correct determination of control signal is very important to achieve good tracking performance. The process for selecting the PID parameters is discussed in detail and the simulations results are used to show the efficacy of the controller.

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Section: *Author For Correspondencementioning

confidence: 99%

Tracking control of parallel manipulator with 3-DOF

Chaudhary¹,

Ohri²

2016

IJATEE

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Section: Introductionmentioning

confidence: 99%

“…Thus, it is the most popular type of FPID controller used in various studies and applications [17]. Several approaches have been presented to adjust the scale factors in the different configurations of two-input FPID controllers, such as self-tuning [18], trial and error [19], saturation-based tuning methods [20], and using genetic algorithms [21].…”

Section: Introductionmentioning

confidence: 99%

“…The reason is that the overall gain of the system is increased and causes an oscillatory and even critically stable behavior in the controlled variable. Recent studies have shown that the gain of the FPID controller must be smaller than that of the conventional PID controller [28], even in controllers without an AW system, to prevent the controller's inherent saturation [20].With this adjustment in FPID controllers or their two-input variants, the output scaling factor is reduced and the tracking time constant is augmented. Consequently, the system presents more bounded oscillations in the steady state of the response, but the transient response is slower.…”

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confidence: 99%

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A novel method for fuzzy scale factors scheduling in fuzzy PD+I with anti-windup system controllers

Sartorius-Castellanos¹,

Moreno-Vázquez²,

Antonio-Ortiz³

et al. 2016

Rev. Fac. Ing. Univ. Antioquia

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ABSTRACT:The adjustment of scale factors in fuzzy controllers is a key factor in their correct functioning. In two-inputs fuzzy PID controllers, such as fuzzy PI+D (FPI+D) and fuzzy PD+I (FPD+I), the adjustment of scale factors is directly related to the adjustment of the gains of a PID controller using some of the traditional methods of adjustment. In systems that have control signal saturation, fuzzy PID controllers require anti-windup systems (AW) that limit the controller's integral action. In these situations, the adjustments of scale factors are not directly related to the adjustment of gains of a PID controller. Its use increases the overall gain system and creates an unbounded controller, which causes a faster response in the transient state but an oscillatory behavior and even critical stability in the steady state of the response. A solution to this problem is to reduce the output scale factor, to create a bounded controller, in which the tracking time constant is augmented. Consequently, the system presents more bounded oscillations in the steady state, but the transient response is slower. The main motivation of this research was to develop an approach for adjusting fuzzy PD+I controllers with an anti-windup system (FPD+I AW) with faster response in the transient state and without oscillatory behavior in the steady state. This approach uses a second fuzzy controller, which adjusts the output scale factor and the tracking time constant according to the actual system error. To verify the effectiveness of the proposed approach, a fuzzy PD+I controller with an AW system based on tracking back calculation and fuzzy scale factor scheduling (FPD+I AW-FSFS) was implemented and used to control the speed in a direct current motor with control signal saturation and was compared with the responses of FPD+I unbounded and FPD+I bounded controllers with AWs based on tracking back calculation, thereby proving the effectiveness of the proposed method. RESUMEN:El ajuste de los factores de escala en los controladores difusos es un factor clave para su correcto funcionamiento. En controladores difusos PID con dos entradas, como los controladores difusos PI+D (FPI+D) y PD+I (FPD+I), el ajuste de los factores de escala está directamente relacionado con el ajuste de las ganancias de un controlador PID, utilizando alguno de los métodos tradicionales de ajuste. En sistemas que presentan saturación en la señal de control, los controladores difusos PID requieren un sistema de anti-windup (AW) que limite la acción integral del controlador. En estos casos, el ajuste de los factores de escala no está relacionado directamente al ajuste de las ganancias de un controlador PID. Utilizar este ajuste de ganancias, incrementa la ganancia general del sistema, creando un controlador sin acotar, el cual presenta una rápida respuesta en el estado transitorio, pero un comportamiento oscilatorio, e incluso críticamente inestable en la respuesta en estado estable. Una solución a este problema es reducir el factor de escala de salida, creando u...

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“…Fuzzy logic [11] is becoming a very useful tool to deal with nonlinear systems by inferring from a rule base that contains the necessary knowledge extracted from an expert [12]. For example, a fuzzy controller was adopted to control a clamp rotation of a forging manipulator [13] and a fuzzy controller was designed for speed control of a nonlinear servosystem [14]. The design of fuzzy controllers depends mainly on the experience of operators rather than the analytical model of the controlled object, so making a stability analysis is hard work [15], [16].…”

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confidence: 99%

FUZZY P+ID Controller for a Constant Tension Winch in a Cable Laying System

Chen

2017

IEEE Trans. Ind. Electron.

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A Saturation-Based Tuning Method for Fuzzy PID Controller

Cited by 69 publications

References 32 publications

Tracking control of parallel manipulator with 3-DOF

Tracking control of parallel manipulator with 3-DOF

A novel method for fuzzy scale factors scheduling in fuzzy PD+I with anti-windup system controllers

FUZZY P+ID Controller for a Constant Tension Winch in a Cable Laying System

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