PID-type Fuzzy Logic Controller Research Articles

Temperature-Controlled Laser Cutting of an Electrical Steel Sheet Using a Novel Fuzzy Logic Controller

A novel PID-type fuzzy logic controller (FLC) with an online fuzzy tuner was created to maintain stable in situ control of the cutting front temperature, aiming to enhance the laser process for thin non-oriented electrical steel sheets. In the developed controller, the output scaling factors and the universe of discourse were initially optimized using a hybrid of the particle swarm optimization and grey wolf optimization methods. The optimal parameters obtained were utilized in experiments involving the laser cutting of thin non-oriented electrical steel sheets, compared to an open-loop control system maintaining a constant cutting speed. The PID-type FLC with an online fuzzy tuner demonstrated a superior cutting quality, generating a smaller roundness and a reduced heat-affected zone (HAZ) through the in situ tuning of control parameters. Particularly, the HAZ width was significantly smaller than that reported in a previous study which used fuzzy gain scheduling for temperature control. Moreover, the cutting time was diminished by optimally adjusting the cutting speed using PID-type FLC with an online fuzzy tuner. Therefore, the accumulated heat in the steel sheet, particularly under high laser pulse frequencies, was effectively reduced, making it suitable for industrial applications.

A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications

Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.

A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm

The main aim of this work consists of proposing a new three-step adjusting approach for an improved version of PID-type fuzzy structure in order to determine its design parameters based on a novel hybrid PSO search technique called PSOSCALF, combining Sine Cosine Algorithm (SCA) and Levy Flight (LF) distribution. In addition, conventional and self-tuning controllers are designed to get a better understanding of the performance and robustness of the proposed PID-type FLC approach. At first, the proposed PID-type FLC structure is defined as an optimization problem and then the PSOSCALF algorithm is applied to resolve it systematically. Evaluation of the performance quality of the proposed fuzzy structure is accomplished based on the stabilization and tracking control of a nonlinear Inverted Pendulum (IP) system. To make a complete comparison, the performance of three other optimization techniques namely simple PSO, Differential Evolution (DE) and Cuckoo Search (CS) are examined against the hybrid PSOSCALF algorithm. The simulation results demonstrate that the proposed PSOSCALF-tuned PID-type FLC structure is able to decrease the overshoot and integral square error amounts by about 25% and 10%, respectively compared to the self-tuning controllers. Finally, for more validation, all the controllers are tested under four different disturbance scenarios. Obtained results show that the proposed PID-type FLC can better stabilize the pendulum angle under all the scenarios compared to the PID and self-tuning controllers.

Online Identification and Artificial Intelligence Control of a Servo Pneumatic System

The aim of this paper is to identify and control a pneumatic servo drive in real-time environment. Obtaining the system’s dynamic model accurately can be difficult once the pneumatic servo-system has been assembled since its highly nonlinear in nature, as a result, some difficulties in servo-pneumatic system modeling and control. In order to, overcome the complexity associated with the system nonlinearity, auto-regressive moving-average (ARMA) model is employed to identify the system’s dynamic model in real-time environment. The advantages of this approach include high accuracy in the estimated model, low cost, and time reduction in controller design. The results acquired from the online experimental measured data are used to predict a discrete transfer function of the pneumatic servo system. The fourth-order model with one-step prediction shows the best performance compared with different order estimated model with varying sizes of step. Due to the highly nonlinearity of the system under study, two sophisticated controllers, PID-type fuzzy logic controller and Fractional order PID controller were chosen and designedusingthree optimization algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), grey wolf optimization (GWO).

ON-LINE RULE WEIGHTING FOR PID-TYPE FUZZY LOGIC CONTROLLERS USING EXTENDED KALMAN FILTER

Abstract In this study, we propose a method for online tuning of fuzzy rule weights of PID-type fuzzy logic controller via Extended Kalman Filter. In the application phase of Extended Kalman Filter to this new online parameter optimization setting, suitable state and observation vectors is needed; in this framework, the rule weights of the rule base are defined as the states and the output of the fuzzy system is defined as the observation vector. We apply the weight adjustment not to the consequent of the rule but instead to the complete rule. The effectiveness of the proposed on-line weight adjustment method is then demonstrated on linear and non-linear systems by simulations. The performance of the proposed tuning method is evaluated according to four performance measures and performance amelioration is observed in all measures. Moreover, the proposed on-line tuned PID-type FLC can handle the noise more successfully than the conventional one.

Optimal PID-type fuzzy logic controller for a multi-input multi-output active magnetic bearing system

The performance of the fuzzy controllers depends highly on the proper selection of some design parameters which is usually tuned iteratively via a trial and error process based primarily on engineering intuition. With the recent developments in the area of global optimization, it has been made possible to obtain the optimal values of the design parameters systematically. Nevertheless, it is well known that unless a priori knowledge is available about the optimization search-domain, most of the available time-domain objective functions may result in undesirable solutions. It is consequently important to provide guidelines on how these parameters affect the closed-loop behavior. As a result, some alternative objective functions are presented for the time-domain optimization of the fuzzy controllers, and the design parameters of a PID-type fuzzy controller are tuned by using the proposed time-domain objective functions. Finally, the real-time application of the optimal PID-type fuzzy controller is investigated on the robust stabilization of a laboratory active magnetic bearing system. The experimental results show that the designed PID-type fuzzy controllers provide much superior performances than the linear on-board controllers while retaining lower profiles of control signals.

A new adaptive configuration of PID type fuzzy logic controller

In this paper, an adaptive configuration for PID type fuzzy logic controller (FLC) is proposed to improve the performances of both conventional PID (C-PID) controller and conventional PID type FLC (C-PID-FLC). The proposed configuration is called adaptive because its output scaling factors (SFs) are dynamically tuned while the controller is functioning. The initial values of SFs are calculated based on its well-tuned counterpart while the proceeding values are generated using a proposed stochastic hybrid bacterial foraging particle swarm optimization (h-BF-PSO) algorithm. The performance of the proposed configuration is evaluated through extensive simulations for different operating conditions (changes in reference, load disturbance and noise signals). The results reveal that the proposed scheme performs significantly better over the C-PID controller and the C-PID-FLC in terms of several performance indices (integral absolute error (IAE), integral-of-time-multiplied absolute error (ITAE) and integral-of-time-multiplied squared error (ITSE)), overshoot and settling time for plants with and without dead time.

MOPSO-based optimal fuzzy logic control strategy for standalone hybrid diesel-wave energy conversion system

The paper proposes a Multi-Objective Particle Swarm Optimization (MOPSO)-based Fuzzy Logic Control Proportional-Integral-Derivative (MOPSO-FLC-PID) controller for stand-alone hybrid Diesel- variable speedWave Energy Conversion System (WECS).The optimal control performance can be achieved by tuning scaling factors of the FLC and gain parameters with Multi-ObjectiveParticle Swarm Optimization (MOPSO) algorithm in order to improve system dynamic performance especially when nonlinearity,uncertainty and complexity of the hybrid Diesel-WECS are involved. Number of objective functions is defined to measurethe performance of the proposed controllers such as minimize the transient DC and AC voltage and current ripples, minimizethe DC and AC voltage and current Total Harmonic Distortion (THD), minimize the absolute total control steady state errordeviations, minimize the RMS absolute voltage deviations at the DC and AC load collection buses, ensure minimal losses aswell as wave energy optimal power utilization. Since some of these objective functions are conflicting, MOPSO is used to finda Pareto front from which a desired optimal operating state can be chosen. For the purpose of comparing the improvementobtained in the system dynamic performance with the application of the MOPSO-FLC-PID procedure to tune the scaling factorsof the FLC and the PID controller gains, these results are compared with those obtained using traditional Ziegler-Nichols PIDapproach, PID type fuzzy logic controller designed with Single-Objective Particle Swarm Optimization (SOPSO-FLC-PID),traditional PID type controller optimized with SOPSO-PID and traditional PID type controller optimized with MOPSO-PID.A complete simulation model is developed for such stand-alone hybrid Diesel-WECS under variable speed operation usingMATLAB Simulink environment. Simulation results show that the proposed design approaches are efficient to find the optimalscaling factors and PID gains of the controllers and therefore improves the transient performance of the hybrid Diesel-WECSover a wide range of operating conditions, sudden load change and three-phase short circuit fault.

Optimal single input PID-type fuzzy logic controller

Despite the demonstrated effectiveness of fuzzy logic controllers in linear and nonlinear systems, the major limitation is that there exists a steady-state error. This work presents a novel single input PID-type fuzzy logic controller that minimizes the steady-state error. It adopts the function of the error, derivative of the error, and the integral of the error to produce a single input variable of the single input fuzzy logic controller. Controller parameters are then optimized by multiobjective genetic algorithm. The proposed framework was tested on a system that has a third-order transfer function and on a permanent magnet linear synchronous motor system. The proposed framework has obtained good performance in minimizing the steady-state error. Additionally, there are only three rules required.

PID-type fuzzy logic controller tuning based on particle swarm optimization

In this paper, a new PID-type fuzzy logic controller (FLC) tuning strategy is proposed using a particle swarm optimization (PSO) approach. In order to improve further the performance and robustness properties of the proposed PID-fuzzy approach, two self-tuning mechanisms are introduced. The scaling factors tuning problem of these PID-type FLC structures is formulated and systematically resolved, using a proposed constrained PSO algorithm. The case of an electrical DC drive benchmark is investigated, within a developed real-time framework, to illustrate the efficiency and superiority of the proposed PSO-based fuzzy control approaches. Simulation and experimental results show the advantages of the designed PSO-tuned PID-type FLC structures in terms of efficiency and robustness.

A PID type fuzzy logic controller: Design, performance evaluation and applications with PLCs on long dead-time systems

It has been widely verified that a fuzzy logic controller can achieve a better performance for industrial systems than conventional controllers. In addition, fuzzy logic controllers have a major interest of researchers in the area of industrial process control. For these reasons in this paper, a PID type fuzzy logic controller (FPID) is presented for programmable logic controllers (PLC), and the design steps are explained in detail (such as tuning FPID's parameters, using symmetric or non-symmetric membership functions) to express the presented FPID functional for industrial systems. The aim of this study is to put forward efficiency of PID type fuzzy logic controllers for PLCs on industrial long dead-time systems. Moreover in this paper, a PLC algorithm is constructed and the complete algorithm is tested in a first order long dead-time system, second order long dead-time systems, and results of this tests are offered and explained.

Self tuning fuzzy PID type load and frequency controller

In this paper, a self tuning fuzzy PID type controller is proposed for solving the load frequency control (LFC) problem. The fuzzy PID type controller is constructed as a set of control rules, and the control signal is directly deduced from the knowledge base and the fuzzy inference. Moreover, there exists a self tuning mechanism that adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller in an on-line manner. The self tuning mechanism depends on the peak observer idea, and this idea is modified and adapted to the LFC problem. A two area interconnected system is assumed for demonstrations. The proposed self tuning fuzzy PID type controller has been compared with the fuzzy PID type controller without a self tuning mechanism and the conventional integral controller through some performance indices.

A New Self-tuning Design for PID-type Fuzzy Logic Controllers

In this study, a new method is proposed for tuning the coefficients of PID type fuzzy logic controllers. The new method adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller using a fuzzy inference mechanism in an on-line manner. The fuzzy inference mechanism that adjusts the related coefficients has two inputs one of which is called “normalized acceleration” and the other one is the classical “error”. The “normalized acceleration” gives the “relative rate” information about the fastness or slowness of the system response. An appropriate rule-base is generated for the adaptation of the derivative coefficient of the PID type fuzzy logic controller using these two input variables. The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations. The simulations are done on a second order system with varying parameters and time delay.

Self-tuning of PID-type fuzzy logic controller coefficients via relative rate observer

In this study, a new method is proposed for tuning the coefficients of PID-type fuzzy logic controllers (FLCs). The new method adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID-type FLC using a fuzzy inference mechanism in an on-line manner. The fuzzy inference mechanism that adjusts the related coefficients has two inputs, one of which is called “normalized acceleration” and the other one is the classical “error”. The “normalized acceleration” gives the “relative rate” information about the fastness or slowness of the system response. An appropriate rule-base is generated for the adaptation of the derivative coefficient of the PID-type FLC using these two input variables. The integral coefficient is then updated as the reciprocal of the derivative coefficient. The robustness and effectiveness of the new self-tuning algorithm have been compared with the other related tuning methods proposed in the literature through simulations. The simulations are done on a second-order system with varying parameters and time delay.

AUTO-TUNING OF FUZZY PID CONTROLLERS

In this work the auto-tuning procedure proposed by Astrom and Hagglund is extended and developed for tuning the scaling factors of a modified hybrid PID type fuzzy logic controller (MHPID-FLC). This new procedure is based on two steps. First, mathematical expressions to link the scaling factors of the MHPID-FLC with the proportional, integral and derivative actions of its traditional counterpart are derived. Second, based on this relationship and using the Ziegler-Nichols tuning formulae, the scaling factors of the MHPID-FLC are obtained by means of a relay experiment. The effectiveness of this approach is shown in benchmark processes taken from the literature.

SELF-TUNING FUZZY CONTROL OF A ROTARY DRYER

Drying, especially rotary drying is without doubt one of the oldest and most common unit operations in industries. It is a very complex non-linear process including the movement of solids in addition to thermal drying. This means that both the modelling and control of a rotary dryer is difficult with conventional methods. The aim of this research was to improve dryer control by developing control systems based on self-tuning PID-type fuzzy logic controllers. The behaviour of the control systems has been tested with simulations based on the model of a pilot plant dryer located in the Control Engineering Laboratory at the University of Oulu. The control results have been compared achieved with a conventional PID controller.