• Journal of the Korean Institute of Intelligent Systems
• /
• v.7 no.3
• /
• pp.81-88
• /
• 1997

This paper dealt with the position control of a flexible miniature finger driven by piezoelectric bimorph cells, cemented on both side of the finger. Bending moments generated by cells drives the finger, and end-point of the finger is controlled, so as to move in synchrony with fluctation of target and maintain a constant distance between target surface and inger's tip. The voltage applied for the cell is controlled by tip displacement error and error rate. We proposed a PD-Fuzzy controller under conception of PD control strategy. It brought and advantage which reduce number of rules than that of same type conventional fuzzy system and more correct redponse than PID control results.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.7 no.2
• /
• pp.96-101
• /
• 2007

Neural networks are known as kinds of intelligent strategies since they have learning capability. There are various their applications from intelligent control fields; however, their applications have limits from the point that the stability of the intelligent control systems is not usually guaranteed. In this paper we propose an adaptive tracking control for robot manipulators using the radial basis function network (RBFN) that is e. kind of neural networks. Adaptation laws for parameters of the RBFN are developed based on the Lyapunov stability theory to guarantee the stability of the overall control scheme. Filtered tracking errors between actual outputs and desired outputs are discussed in the sense of the uniformly ultimately boundedness(UUB). Additionally, it is also shown that parameters of the RBFN are bounded. Experimental results for a SCARA-type robot manipulator show that the proposed adaptive tracking controller is adaptable to the environment changes and is more robust than the conventional PID controller and the neuro-controller based on the multilayer perceptron.

• Journal of Energy Engineering
• /
• v.25 no.3
• /
• pp.27-33
• /
• 2016

The heat recovery system that was applied in this study, is the energy-saving type that can produce the maximum cooling capacity less power in use. In order to have a more precise control function the temperature and humidity of the constant temperature and humidity machine, control algorithm is applied to designed a fuzzy PID controller, and the outside air compensation device (air-cooled) demonstrated excellent ability to dehumidify the moisture, $-20^{\circ}C$ in winter. High efficiency and the low-noise type sirocco fan operate quitely and designed to fit the bottom-up and top-down in accordance with the characteristics of equipment. as a result of experiment data, the conversion efficiency is 95% or more, power recovery time is within 5sec, stop delay time is within 30sec, pump down time is 10sec, pump delay time is 5sec, heating delay time is 5sec, temperature deviation is ${\pm}2^{\circ}C$ (cooling deviation: $2^{\circ}C$, Heating deviation : $2^{\circ}C$), humidity deviation is a ${\pm}5%$ (humidification deviation 3.0%, dehumidification deviation 3.0%). Recently, ubiquitous technology is important. so, the constant temperature and humidity machine designed to be able to remotely control to via the mobile phone, and more scalable to support MMI software and automatic interface. Further, the life of the parts and equipment is extended by the failure.

• Journal of the Korean Society of Industry Convergence
• /
• v.20 no.1
• /
• pp.34-48
• /
• 2017

This study proposes a new approach to control a trajectory control of vertical type articulated robot arm with six revolution joints by computed torque method for manufacturing process automation. The proposed control scheme takes advantage of the properties of the fuzzy controllers. The proposed method is suitable to control of the trajectory and path control in cartesian space for vertical type articulated robot manipulator for forging manufacturing process automation. The results is illustrated that the proposed fuzzy computed torque controller is more stable and robust than the conventional computed torque controller. This study is included with an analytical methodology of inverse kinematic computation for 6 DOF manipulators. And an intelligent PID based on feed forward fuzzy control structure is applied to control the working path control with disturbances caused by uncertainty parameters of the manipulator dynamic model. Lastly, the validity of proposed is verified by simulations and experiments.

• International Journal of Ocean System Engineering
• /
• v.1 no.4
• /
• pp.192-197
• /
• 2011

This paper describes the mathematical modeling, control algorithm, system design, hardware implementation and experimental test of a Manta-type Unmanned Underwater Vehicle (MUUV). The vehicle has one thruster for longitudinal propulsion, one rudder for heading angle control and two elevators for depth control. It is equipped with a pressure sensor for measuring water depth and Doppler Velocity Log for measuring position and angle. The vehicle is controlled by an on-board PC, which runs with the Windows XP operating system. The dynamic model of 6DOF is derived including the hydrodynamic forces and moments acting on the vehicle, while the hydrodynamic coefficients related to the forces and moments are obtained from experiments or estimated numerically. We also utilized the values obtained from PMM (Planar Motion Mechanism) tests found in the previous publications for numerical simulations. Various controllers such as PID, Sliding mode, Fuzzy and $H{\infty}$ are designed for depth and heading angle control in order to compare the performance of each controller based on simulation. In addition, experimental tests are carried out in a towing tank for depth keeping and heading angle tracking.

• Proceedings of the KIEE Conference
• /
• 1996.07b
• /
• pp.1377-1379
• /
• 1996

In this paper, each parameter was identified by the gradient descent method to overcome difficulty deciding fuzzy rules of FLC for the unknown process and the type of membership Junctions. Usually PID or optimal control theories have been mostly usee in control field so far. However, optimal control requires much time for calculation because of adaptation for disturbance and nonlinearity. And intricate technique such as MRAS which can be realized only by an expert are limited to be used in the systems requiring rapid and precise response because of comparatively longer calculating time and complicateness. Gradient descent method is a method to find Z minimizing a function about a certain vector Z. And required output of FLC is gained using gradient approaching method in order to adapt control rule parameters of FLC. Simulation proved validation of this algorithm.

• 전자공학회논문지 IE
• /
• v.43 no.4
• /
• pp.28-36
• /
• 2006

In this paper, an algorithm to classify the 4 motions of arm and a control system to position control the prosthesis are studied. To classify the 4 motions, we use flex sensors which is electrical resistance type sensor that can measure warp of muscle. The flex sensors are attached to the biceps brchii muscle and coracobrachialis muscle and the sensor signals are passed the sensing system. 4 motion of the forearm - flexion and extension, the pronation and supination are classified from this. Also position of forearm is measured from the classified signals. Finally, A two D.O.F prosthesis arm with RC servo-motor is designed to verify the validity of the algorithm. At this time, fuzzy controller is used to reduce the position error by rotary inertia and noise. From the experiment, the position error had occurred within about 5 degree.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.6
• /
• pp.738-743
• /
• 2007

In this paper, a muscle motion sensing system and an artificial arm control system are studied. The artificial arm is for the people who lost one's forearm. The muscle motion sensing system detect the intention of motion from the upper arm's muscle. In sensing system we use flex sensors which is electrical resistance type sensor. The sensor is attached on the biceps brachii muscle and coracobrachialis muscle of the upper arm. We propose an algorithm to classify the one's intention of motions from the sensor signal. Using this algorithm, we extract the 4 motions which are flexion and extension of the forearm, pronation and supination of the arm. To verify the validity of the proposed algorithms we made experiments with two d.o.f. artificial arm. To reduce the control errors of the artificial arm we also proposed a fuzzy PID control algorithm which based on the errors and error rate.