Dinh Quang Truong

Force control for press machines using an online smart tuning fuzzy PID based on a robust extended Kalman filter

Research highlights? Electro-hydraulic actuators (EHAs) are especially paid attention in heavy industry. ? An online smart tuning fuzzy PID (OSTFPID) controller is designed for press machines. ? Here, the fuzzy PID structure is optimized by a robust extended Kalman filter. ? An electro-hydraulic test machine (EHTM) is setup to investigate pressing forces. ? Experiments are carried out to evaluate the effectiveness control method. Electro-hydraulic actuators (EHAs) have a wide range of applications where force or position control with high accuracy is exceedingly necessary. Among them, press machines applied hybrid EHAs are more and more used in the heavy industry. This paper presents an online smart tuning fuzzy PID (OSTFPID) approach based on a robust extended Kalman filter (REKF) for the development of high force control precision in the press machines. Here, the main control unit employs the fuzzy PID structure of which membership function (MF) optimization is considered as a system identification problem. A smart selection procedure (SSP) is implemented to pick out only fuzzy input and output MFs activated at each running step, and then the REKF algorithm is used to tune the active MFs automatically during the operation process to minimize the control error. Consequently, the active MFs are trained about their shapes and positions to adapt to the working conditions. As the result, the control performance is significantly improved, while the optimizing time and number of the controller calculations are remarkably reduced. In order to verify the ability of the proposed controller applied to the press machines using EHAs, a test press bench system called electro-hydraulic test machine (EHTM) is also suggested and setup to use in this study. Real-time experiments on the EHTM are carried out to evaluate the control method in a large variation of working environments. Considerable improvement in the performance generated by the designed controller is compared with the traditional PID and fuzzy PID controllers.

Online self-tuning fuzzy proportional–integral–derivative control for hydraulic load simulator

Hydraulic systems play an important role in modern industry owing to the fact that hydraulic actuator systems have many advantages over other technologies with electric motors, high durability, and the ability to produce large force at high speeds. Therefore, the hydraulic actuator has a wide range of application fields such as hydraulic pressing machines, moulding technology, etc. where controlled forces or pressures with high accuracy and fast response are the most significant demands. Consequently, many hybrid actuator models have been developed for research on how to control forces or pressures with the best results. The current paper presents a new kind of hydraulic load simulator for conducting performance and stability tests for control forces of hydraulic hybrid systems. In the dynamic loading process, disturbance makes the control performance (such as stability, frequency response, loading sensitivity, etc.) decrease or turn bad. In order to improve the control performance of a loading system and to eliminate or reduce the disturbance, an online self-tuning fuzzy proportional—integral—derivative (PID) controller is designed. Experiments are carried out to evaluate the effectiveness of the proposed control method applied for hydraulic systems with varied external disturbance as in real working conditions.

Robust force control of a hybrid actuator using quantitative feedback theory

The use of hydraulic systems in industrial applications has become widespread due to their advantages in efficiency. In recent years, hybrid actuation systems, which combine electric and hydraulic technology into a compact unit, have been adapted to a wide variety of force, speed and torque requirements. A hybrid actuation system resolves energy consumption and noise problems characteristic of conventional hydraulic systems. A new, low-cost hybrid actuator using a DC motor is considered to be a novel linear actuator with various applications such as robotics, automation, plastic injection-molding, and metal forming technology. However, this efficiency gain is often accompanied by a degradation of system stability and control problems. In this paper, to satisfy robust performance requirements, tracking performance specifications, and disturbance attenuation requirements, the design of a robust force controller for a new hybrid actuator using Quantitative Feedback Theory (QFT) is presented. A family of plant models is obtained from measuring frequency responses of the system in the presence of significant uncertainty. Experimental results show that the hybrid actuator can achieve highly robust force tracking even when environmental stiffness set-point force varies. In addition, it is understood that the new system reduces energy use, even though its response is similar to that of a valve-controlled system.

Self tuning fuzzy PID control for hydraulic load simulator

Hydraulic systems play an important role in modern industry for the reason that hydraulic actuator systems take many advantages over other technologies. Therefore, hydraulic actuator has a wide range of application fields where controlled forces or pressures with high accuracy and fast response is the most significant demand. However, disturbances in the real working conditions make the control performance such as the stability, the frequency response and loading sensitivity decrease or go bad. This paper presents a new kind of hydraulic load simulator for conducting force control performance. A self tuning fuzzy PID controller is designed to eliminate or reduce the disturbance and improve control performance of loading system. Experimental results show that the proposed controller is feasible to apply for hydraulic systems with varied external disturbances.

Application of Fuzzy-PID Controller in Hydraulic Load Simulator

In modern industries, hydraulic actuator systems take an important part in recent years due to their advantages including high durability and the ability to produce large forces at high speeds. Therefore, hydraulic actuator has a wide range of application fields where controlled force or pressure with high accuracy and fast response is the most significant demand. Consequently, many hybrid actuator models have been developed to research about control force or pressure with best results. This paper presents a new kind of hydraulic load simulator using a fuzzy-PID controller for force control. Experiments are carried out to evaluate the effectiveness of the proposed control method applied for hydraulic systems with varied external disturbance as in real working conditions.

Design of An Advanced Time Delay Measurement and A Smart Adaptive Unequal Interval Grey Predictor for Real-Time Nonlinear Control Systems

This paper is a generation step for developing a novel control methodology based on a variable sampling period (VSP) approach to deal with nonlinear systems containing random delays. The proposed VSP is constructed from an advanced time delay measurement (TDM) method and a novel time delay prediction (TDP) method. The TDM is built to measure real working time of the controlled system, consequently observing a set of actual system delays. Next, the TDP is based on a so-called Smart Adaptive Unequal Interval Grey Model with single-variable first-order - SAUIGM(1,1) to forecast the system delay in the next working step for adjusting the sampling period in order to eliminate bad effects of time delays on the control performance. The SAUIGM(1,1) model was developed from the GM(1,1) model with four significant improvements. It can be easily applied to any practical prediction problem and achieve high prediction accuracy even in case of sparse or largely noisy data. Real-time delay measurements and predictions have been carried out with several examples to verify the proposed TDM and TDP methods. The results indicate that the designed TDM and TDP have strong potential to be applied to the suggested VSP methodology for nonlinear control systems.

Hysteresis modeling of magneto-rheological (MR) fluid damper by self tuning fuzzy control

Magneto-rheological (MR) fluid damper is a semi-active control device that has recently received more attention by the vibration control community. But inherent nonlinear hysteresis character of magneto-rheological fluid dampers is one of the challenging aspects for utilizing this device to achieve high system performance. So the development of accurate model is necessary to take the advantage their unique characteristics. Research by others has shown that a system of nonlinear differential equations can successfully be used to describe the hysteresis behavior of the MR damper. The focus of this paper is to develop an alternative method for modeling a damper in the form of centre average fuzzy interference system, where back propagation learning rules are used to adjust the weight of network. The inputs for the model are used from the experimental data. The resulting fuzzy interference system is satisfactorily represents the behavior of the MR fluid damper with reduced computational requirements. Use of the neuro-fuzzy model increases the feasibility of real time simulation.

Robust Variable Sampling Period Control for Networked Control Systems

The aim of this paper is to develop a novel robust variable sampling period controller (RVSPC) for networked control systems (NCSs) in the presence of random time delays and packet losses. Different from the existing control techniques for NCSs, the RVSPC controller is constructed as a hybrid robust controller with the adaptive variable sampling period. To adapt to the delay variation, a so-called variable sampling period adjuster based on a time delay predictor (TDP) and a time delay and packet detector is designed to adjust effectively the sampling period. To efficiently compensate for the random delays and packet losses, the hybrid controller is designed as a combination of a quantitative feedback theory (QFT)-based robust controller and a robust state feedback controller (RSFC) with adaptive control gain. A smart switch based on the TDP is then used to select properly the main control unit as the QFT or RSFC corresponding to the current delay and packet loss status. Illustrative examples with NCSs including both computation and communication delays and packet losses are finally carried out to illustrate the effectiveness of the proposed method.

A study on force control of electric-hydraulic load simulator using an online tuning Quantitative Feedback Theory

Nowadays, hydraulic actuators play an important role in a modern industry where controlled force or position with high accuracy is the most significant demand. This paper presents a new kind of electric-hydraulic load simulator (HLS) for conducting performance and stability test in the bench system where force control is important. The system model consists of a hybrid hydro-electric actuator and another hydraulic circuit generating disturbances. For the purpose of improving force control performance of hydraulic hybrid systems, an online tuning force controller based quantitative feedback theory (QFT) technique is applied to the load simulator and also proposed in this paper. The controller is firstly designed to satisfy the robust performance requirement, tracking performance specification, and disturbance attenuation despite uncertainties of the load simulator system. Secondly, by using gradient descent method it becomes an online tuning QFT controller during the system operation process to adapt with a wide range of working conditions including perturbations. Experiments are carried out to evaluate the effectiveness of the proposed control method applied for the electric-hydraulic load simulator systems.

Design and Verification of a Non-linear Black-Box Model for Ionic Polymer Metal Composite Actuators:

An ion polymer metal composite (IPMC) is an electro-active polymer that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network and vice versa. This article presents a novel accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The NBBM is a combination of two advanced designs which are a general multi-layer perceptron neural network (GMLPNN) and a self-adjustable learning mechanism (SALM). Here, the GMLPNN is constructed with an ability to auto-adjust its structure based on its characteristic vector, while the SALM is built to take part in training the GMLPNN decisive parameters. For the model verification, an IPMC actuator is set up to investigate the IPMC characteristics as well as to generate training data. Next, the advanced NBBM model for the IPMC system is performed with suitable inputs to estimate the IPMC tip displacement. Finally, the model parameters are optimized by using the SALM mechanism with ...An ion polymer metal composite (IPMC) is an electro-active polymer that bends in response to a small applied electrical field as a result of mobility of cations in the polymer network and vice versa. This article presents a novel accurate nonlinear black-box model (NBBM) for estimating the bending behavior of IPMC actuators. The NBBM is a combination of two advanced designs which are a general multi-layer perceptron neural network (GMLPNN) and a self-adjustable learning mechanism (SALM). Here, the GMLPNN is constructed with an ability to auto-adjust its structure based on its characteristic vector, while the SALM is built to take part in training the GMLPNN decisive parameters. For the model verification, an IPMC actuator is set up to investigate the IPMC characteristics as well as to generate training data. Next, the advanced NBBM model for the IPMC system is performed with suitable inputs to estimate the IPMC tip displacement. Finally, the model parameters are optimized by using the SALM mechanism with training data. The NBBM model ability is evaluated by a comparison of the estimated and real IPMC bending characteristics.