Jong Il Yoon

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.

Introduction to quantitative feedback theory for robust force control of load simulator

This paper presents a new kind of hydraulic load simulator (HLS) for conducting performance and stability test for control forces of hydraulic hybrid systems. For the purpose of improving force control performance of hydraulic hybrid systems, a robust force controller using quantitative feedback theory (QFT) technique applied to the HLS is also proposed in this paper. The controller is designed to satisfy the robust performance requirement, tracking performance specification, and disturbance attenuation despite uncertainties of HLS. 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.

A generation step to develop steel rolling machine performance using an electro-hydraulic actuator and an online tuning modified-grey fuzzy PID controller

This paper presents an application of an electro-hydraulic actuator (EHA) and a novel control method - online tuning modified-Grey fuzzy PID controller (OTMGFPID) to a steel rolling machine (SRM) in POSCO Corp. in order to improve its working performance. In this SRM, the center position of the roller, which affects directly the rolling quality, is controlled by the EHA system instead of using a conventional servo hydraulic actuator (SHA). To enhance the center position control (CPC) performance, the OTMGFPID which is a combination of a main control unit, online tuning fuzzy PID controller (OTFPID), and an online tuning modified-Grey predictor (OTMGP) is designed for the EHA. The OTMGP with self tuning ability of its predictor step size based on fuzzy technique (called FPSS) is used to estimate the actual system output and to create a compensating control signal corresponding to the system perturbations, consequently, improving the control quality. The ability of using the EHA and OTMGFPID controller for the SRM with CPC is verified by simulated force control target of a press machine using EHA. Simulation results prove that the EHA as well as the proposed controller has strong potential to be applied for CPC of the SRM.

Identification of a nonlinear black-box model for a self-sensing polymer metal composite actuator

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 the mobility of cations in the polymer network and vice versa. The aim of this paper is the identification of a novel accurate nonlinear black-box model (NBBM) for IPMC actuators with self-sensing behavior based on a recurrent multi-layer perceptron neural network (RMLPNN) and a self-adjustable learning mechanism (SALM). Firstly, an IPMC actuator is investigated. Driving voltage signals are applied to the IPMC in order to identify the IPMC characteristics. Secondly, the advanced NBBM for the IPMC is built with suitable inputs and output to estimate the IPMC tip displacement. Finally, the model parameters are optimized by the collected input/output training data. Modeling results show that the proposed self-sensing methodology based on the optimized NBBM model can well describe the bending behavior of the IPMC actuator corresponding to its applied power without using any measuring sensor.

Low-Firing Pb(Zr,Ti)O 3 -Based Multilayer Ceramic Actuators Using Ag Inner Electrode

We investigated the low firing of Li2CO3 added 0.2Pb(Mg1/3Nb2/3)O3 - 0.3Pb(Fe1/2Nb1/2) - 0.5Pb(Zr0.475Ti0.525)O3 (PMNPFN-PZT) ceramics and multilayer actuators (MLAs) using Ag inner electrodes. It was found that 0.1 wt% Li2CO3 was quite effective in lowering the sintering temperature of PMN-PFN-PZT ceramics from 1,100℃ down to 900℃ without deteriorating their piezoelectric ceramics (d33 = 425 pC/N and kp = 61.9%). However, excess Li2CO3 up to 0.3 wt% brings about unwanted problems such as the formation of a LiPbO2 secondary phase and subsequent degradation in the piezoelectric properties. Using 0.1 wt% Li2CO3 added PMN-PFN-PZT ceramics, MLAs with Ag inner electrodes were successfully fabricated, resulting in a normalized strain of 580 pm/V at an electric field of 1.5 kV/mm.

A study on face tracking in real-time for robot

This paper proposed the face detection and tracking system using a embedded computing system for humanoid robot environment which allows the user to observe of recognize an unknown person. For detection faces in image sequence, the system uses the skin color model and deformable ellipse matching. Faces in a view are detected by maximizing the image gradient magnitude around the perimeter of the ellipse (or face line) and the number of skin color pixels in the ellipse area. The information of detected face regions are stored on-line into a face state table and are evaluated for tracking in real-time. Results of this processing are applied to embedded humanoid robot The applied robot acts to detect humans fate and react to it. After this processing, the result is sent to embedded humanoid robot through zigbee wireless module. The robot that got this data acts to detect humans face and react to it.

Time delay control of a pump-controlled electro-hydraulic actuator

This paper presents an accurate position control for a pump-controlled electro-hydraulic actuator (EHA) using time-delay estimation (TDE). The TDE technique is used to estimate nonlinear terms of EHA, which is a third order nonlinear system. The measured signal of linear encoder, and pressure of the EHA are used to implement the TDE technique. The effectiveness of the proposed controller is verified through experiment using an EHA test bench. The proposed controller shows better tracking performance compared with a PID controller.

Design of an online tuning modified-grey fuzzy PID controller for nonlinear systems

This paper presents a design of a novel adaptive controller — online tuning modified-Grey fuzzy PID (OTMGFPID) — to deal with nonlinear systems. The OTMGFPID is a combination of a main control unit — online tuning fuzzy PID (OTFPID), and a predictor — online tuning modified-Grey predictor (OTMGP). The OTFPID controller, which is built from an adaptive proportional-integral-derivative (PID) controller based on an online tuning fuzzy-neural technique and robust checking conditions, is used to drive the system to desired targets. In addition, a smart learning mechanism (SLM) was implemented into the OTFPID in order to optimize smartly its parameters with respect to the control error minimization. The OTMGP with online tuning ability of the predictor step size based fuzzy (FPSS) is used to estimate the actual system output and to create a compensating signal corresponding to the system perturbations. The effectiveness of the proposed OTMGFPID controller has been evaluated by numerical simulations in a comparison with other typical controllers.

A Study on a New Energy Saving Load Simulator using Hybrid Actuator and QFT Technique

The use of hydraulic systems in industrial applications has become widespread due to their efficiency advantages. In recent years, hybrid actuation system, which combines electric and hydraulic technology in a compact unit, can be adapted to a wide variety of force, speed and torque requirements. Moreover, the hybrid actuation system has dealt with the energy consumption and noise problem existed in the conventional hydraulic system. The new low cost hybrid actuator using DC motor as a power supply unit is considered as a novel linear actuator with various applications. However, this efficiency gain is often accompanied by a degradation of system stability and control problems. In this paper, to maintain robust performance requirement, tracking performance specification, and disturbance attenuation requirement, the design of a robust force controller for the new hybrid actuator using quantitative feedback theory (QFT) is presented. A family of plants model is obtained from experimental frequency responses of the system in the presence of significant uncertainty. Experimental results show that highly robust force tracking by hybrid actuator could be achieved even if the stiffness of environment and set-point force change. In addition, it is understood that the new system has energy saving effect even though it has almost the same response as that of valve controlled system

Identification of the 2-Axes Pneumatic Artificial Muscle (PAM) Robot Arm Using Double NARX Fuzzy Model and Genetic Algorithm

In this paper, a novel Forward Double NARX fuzzy model is used for modeling and identifying simultaneously both joints of the prototype 2-axes PAM robot arm. The highly nonlinear coupling feature of both links of the 2-axes PAM robot arm is modeled thoroughly through a Forward Double NARX Fuzzy Model-based identification process using experimental input-output training data. The evaluation of different types of Double NARX Fuzzy Model of the 2-axes PAM robot arm with various ARX model structure will be discussed. For first time, the nonlinear Forward Double NARX Fuzzy Model scheme of the prototype 2-axes PAM robot arm has been investigated. The results show that the nonlinear forward Double NARX Fuzzy Model trained by genetic algorithm yields more performance and higher accuracy than the traditional Fuzzy model.