Kyoung Kwan Ahn

Incipient piezoelectrics and electrostriction behavior in Sn-doped Bi1/2(Na0.82K0.18)1/2TiO3 lead-free ceramics

Dielectric, ferroelectric, piezoelectric, and strain properties of lead-free Sn-doped Bi1/2(Na0.82K0.18)1/2TiO3 (BNKT) were investigated. A crossover from a nonergodic relaxor to an ergodic relaxor state at room temperature, accompanied by a giant electric-field-induced strain, was observed at 5 at. % Sn doping. Switching dynamics monitored during a bipolar poling cycle manifested that the observed giant strain originates from incipient piezoelectricity. When Sn doping level reached 8 at. %, BNKT exhibited an electrostrictive behavior with a highly temperature-insensitive electrostrictive coefficient of Q11 = 0.023 m4 C−2.

Adaptive Backstepping Control of an Electrohydraulic Actuator

This paper presents an adaptive position control for a pump- controlled electrohydraulic actuator (EHA) based on an adaptive backstepping control scheme. The core feature of this paper is the combination of a modified backstepping algorithm with a special adaptation law to compensate all nonlinearities and uncertainties in EHA system. First of all, the mathematical model of the EHA is developed. The position control is then formulated using a modified backstepping technique and the uncertainties in hydraulic system are adapted by employing a special Lyapunov function. The control signal consists of an adaptive control signal to compensate the uncertainties and a simple robust structure to ensure the robustness corresponding to a bounded disturbance. Experimental results proved strongly the ability of the proposed control method.

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.

Speed Control of a Hydraulic Pressure Coupling Drive Using an Adaptive Fuzzy Sliding-Mode Control

In this paper, an adaptive fuzzy sliding-mode control (AFSMC) was proposed for speed control of a hydraulic pressure coupling drive. The AFSMC combined a direct adaptive fuzzy scheme and a fuzzy sliding scheme in a new structure to reduce the tracking error and the chattering of the control effort. The input nonlinearity of the secondary unit, the input dead zone, was taken into account during the speed controller synthesis and analysis of the stability of the closed-loop system. The stability of a system was proven from Lyapunovs sense. Experiments were performed with different controllers, the AFSMC, the traditional sliding-mode control, and the PID controllers, and under different operating conditions. Then, the experimental results were brought into comparison to evaluate the effectiveness of the AFSMC controller from the viewpoints of stability, performance, and robustness of the closed-loop system.

Nonlinear PID Control to Improve the Control Performance of the Pneumatic Artificial Muscle Manipulator Using Neural Network

A novel actuator system which has achieved increased popularity to provide these advantages such as high strength and power/weight ratio, low cost, compactness, ease of maintenance, cleanliness, readily available, cheap power source, inherent safety and mobility assistance to humans performing tasks has been the utilization of the pneumatic artificial muscle (PAM) manipulator, in recent times. However, the complex nonlinear dynamics of the PAM manipulator makes it a challenging and appealing system for modeling and control design. The problems with the time variance, compliance, high hysteresis and nonlinearity of pneumatic systems have made it difficult to realize precise position control with high speed. In order to realize satisfactory control performance, the effect of nonlinear factors contained in thePAM manipulator must be considered. The purpose of this study is to improve the control performance of thePAM manipulator using a nonlinearPID controller. Superb mixture of conventionalPID controller and the neural network, which has powerful capability of learning, adaptation and tackling nonlinearity, brings us a novel nonlinearPID controller using neural network. This proposed controller is appropriate for a kind of plants with nonlinearity uncertainties and disturbances. The experiments were carried out in practicalPAM manipulator and the effectiveness of the proposed control algorithm was demonstrated through the experiments, which suggests its superior performance and disturbance rejection.

Inverse Double NARX Fuzzy Modeling for System Identification

In this paper, a novel inverse double nonlinear autoregressive with exogenous input (NARX) fuzzy model is applied to simultaneously model and identify both joints of the prototype two-axis pneumatic artificial muscle (PAM) robot arms inverse dynamic model. Highly nonlinear features of both joints of the nonlinear manipulator system are identified by the proposed inverse double NARX fuzzy (IDNF) model based on experimental input-output training data. The modified genetic algorithm (GA) optimally generates the appropriate fuzzy if-then rules to perfectly characterize the dynamic features of the two-axis PAM manipulator system. The evaluation of different IDNF models with various ARX model structures will be discussed. For the first time, the nonlinear IDNF model of the two-axis PAM robot arm is investigated. The results show that the nonlinear IDNF model that is trained by GA performs better and has a higher accuracy than the conventional inverse fuzzy model.

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.

Feedforward Control of Shape Memory Alloy Actuators Using Fuzzy-Based Inverse Preisach Model

This brief investigates a possible application of the inverse Preisach model in combination with the feedforward and feedback control strategies to control shape memory alloy actuators. In the feedforward control design, a fuzzy-based inverse Preisach model is used to compensate for the hysteresis nonlinearity effect. An extrema input history and a fuzzy inference is utilized to replace the inverse classical Preisach model. This work allows for a reduction in the number of experimental parameters and computation time for the inversion of the classical Preisach model. A proportional-integral-derivative (PID) controller is used as a feedback controller to regulate the error between the desired output and the system output. To demonstrate the effectiveness of the proposed controller, real-time control experiment results are presented.

Position Control of Shape Memory Alloy Actuators by Using Self Tuning Fuzzy PID Controller

Shape memory alloy (SMA) actuators, which have ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Although the number of applications is increasing, there has been limited success in precise motion control since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis or the surrounding environment of the systems is changed. This paper presents a new development of SMA position control system by using self-tuning fuzzy PID controller. The use of this control algorithm is to tune the parameters of the PID controller by integrating fuzzy inference and producing a fuzzy adaptive PID controller that can be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self tuning fuzzy PID controller are both included in this paper

Improvement of the Control Performance of Pneumatic Artificial Muscle Manipulators Using an Intelligent Switching Control Method

Problems with the control, oscillatory motion and compliance of pneumatic systems have prevented their widespread use in advanced robotics. However, their compactness, power/weight ratio, ease of maintenance and inherent safety are factors that could be potentially exploited in sophisticated dexterous manipulator designs. These advantages have led to the development of novel actuators such as the McKibben Muscle, Rubber Actuator and Pneumatic Artificial Muscle Manipulators. However, some limitations still exist, such as a deterioration of the performance of transient response due to the changes in the external inertia load in the pneumatic artificial muscle manipulator.To overcome this problem, a switching algorithm of the control parameter using a learning vector quantization neural network (LVQNN) is newly proposed. This estimates the external inertia load of the pneumatic artificial muscle manipulator. The effectiveness of the proposed control algorithm is demonstrated through experiments with different external inertia loads.