• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2002.05a
• /
• pp.310-313
• /
• 2002

In industrial actual control system, PID controller has been used with its high delicate control system in position control system. PID controller has simple structure and superior ability in several characteristics. When the response of system is changed by delay time, variable load , disturbances and external environment, control gain of PID controller must be readjusted on the system dynamic characteristics. Therefore, a control ability of PID controller is degraded when the control gain is inappropriately determined. When the response characteristic of system is changed under a condition, control gain of PID controller must be changed adaptively to be a waited response of system. In this paper an PIDA controller is constructed by Two-Layers Neural Network applying back-propagation(BP) algorithm. Form the result of compute. simulation in the proposed controller, its usefulness is verified.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1092-1097
• /
• 2005

In this paper, design approach of PID controller with rejection function against external disturbance in motor control system is proposed using bacterial foraging based optimal algorithm. Up to the present time, PID Controller has been used to operate for AC motor drive because of its implementational advantages in practice and simple structure. However, it is not easy to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error in the industrial system with disturbance. To design disturbance rejection tuning, disturbance rejection conditions based on $H_{\infty}$ are illustrated and the performance of response based on the bacterial foraging is computed for the designed PID controller as ITSE (Integral of time weighted squared error). Hence, parameters of PID controller are selected by bacterial foraging based optimal algorithm to obtain the required response

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2004.10a
• /
• pp.15-20
• /
• 2004

In this paper, design approach of PID controller with rejection function against external disturbance in motor control system is proposed using bacterial foraging based optimal algorithm. Up to the present time, PID Controller has been used to operate for AC motor drive because of its implementational advantages in practice and simple structure. However, it is not easy to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error in the industrial system with disturbance. To design disturbance rejection tuning, disturbance rejection conditions based on H$\_$$\infty$/ are illustrated and the performance of response based on the bacterial foraging is computed for the designed PID controller as ITSE (Integral of time weighted squared error). Hence, parameters of PID controller are selected by bacterial foraging based optimal algorithm to obtain the required response.

• The Transactions of the Korean Institute of Electrical Engineers B
• /
• v.51 no.4
• /
• pp.187-194
• /
• 2002

Recently, the application of the linear machine far industrial field is remarkable increased, especially for the gantry machine, machine tool system and CNC. In these application fields, high dynamics position control performance Is essentially required in both the steady and the transient state. This pacer presents simple but powerful position control loop based on traditional PID controller. The presented position control algorithm, named 'Unified PID Position Controller'has great features for the linear machine drives such as no over-shoot phenomena and simple gain tuning strategy. Through the experimental results with commercial linear motors, it is shown that the proposed algorithm has excellent dynamics suitable fur linear motions.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.50 no.3
• /
• pp.107-113
• /
• 2001

This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.48 no.8
• /
• pp.1014-1021
• /
• 1999

PID controllers with constant gains have been widely used in various control systems. But it is difficult to have uniformly good control performance in all operating conditions. In this paper, we propose a variable PID controller for robot manipulators. We divide total workspace of manipulators into several subspaces. PID controllers in each subspace are optimized using evolution strategy which is a kind of global search algorithm. In real operation, the desired trajectories may cross several subspaces and we select the corresponding gains in each subspace. The gains may have large difference on the boundary of subspaces, which may cause oscillatory motion. So we use artificial neural network to have continuous smooth gain curves to reduce the oscillatory motion. From the experimental results, although the proposed variable PID controller for robots should pay for some computational burden, we have found that the controller is more superior to the conventional constant gain PID controller.

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.717-719
• /
• 2003

Recently Neural Network techniques have widely used in adaptive and learning control schemes for production systems. However, generally it costs a lot of time for learning in the case applied in control system. Furthermore, the physical meaning of neural networks constructed as a result is not obvious. And in practice since it is difficult to the PID gains suitably lots of researches have been reported with respect to turning schemes of PID gains. A Neural Network-based PID control scheme is proposed, which extracts skills of human experts as PID gains. This controller is designed by using three-layered neural networks. The effectiveness of the proposed Neural Network-based PID control scheme is investigated through an application for a production control system. This control method can enable a plant to operate smoothy and obviously as the plant condition varies with any unexpected accident.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.1
• /
• pp.73-80
• /
• 2000

The stewart platform manipulator proposed by stewart is the parallel manipulator which is composed of several independent actuators connecting the upper plate with the base plate and capable of executing a six degree of freedom motion. The manipulator has a structure of a closed loop form, and provides better load-to-weight ratio and ratio and rigidity than a serial manipulator with an open loop form. Moreover, the manipulator has high positional accuracy because position errors of actuators are not additive. Because of these advantages, this manipulator is widely used in many engineering applications such as a driving simulator, a tool of machining center, a force/torque sensor and so on. When this Stewart platform manipulator is controlled in joint space, it is difficult to design a controller using an analytic method due to nonhnearity and unknown parameters of actuators. Therefore, a PID controller is often used because of easiness in applications. To find the PID control gain, a trial-and-error method is generally used. This method is time-consuming, and does not guarantee a optimal gain. Thus, this paper proposes a GA-PID controller which selects an optimal PID control gain using genetic algorithms. And this proposed controller is evaluated experimentally and shows acceptable performance.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.3
• /
• pp.518-526
• /
• 2008

The fuzzy self-tuning PID controller is a PID controller with a fuzzy logic mechanism for tuning its gains on-line. In this structure, the proportional, integral and derivative gains are tuned on-line with respect to the change of the output of system under control. This paper deals with two types of fuzzy self-tuning PID controllers, rule-based fuzzy PID controller and learning fuzzy PID controller. As a medical application of fuzzy PID controller, the proposed controllers were implemented and evaluated in a laparoscopic surgery robot system. The proposed fuzzy PID structures maintain similar performance as conventional PID controller, and enhance the position tracking performance over wide range of varying input. For precise approximation, the fuzzy PID controller was realized using the linear reasoning method, a type of product-sum-gravity method. The proposed controllers were compared with conventional PID controller without fuzzy gain tuning and was proved to have better performance in the experiment.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.20 no.7
• /
• pp.53-57
• /
• 2006

PID controller is widely uesd as automatic equipment for industry. However when a system has various characters of intermittence or continuance, a new parameter decision for accurate control is a hard task. As method of solving this problem, in this paper, a Neural Network gain automatic regulator as PID controller functions is presented. A property feedback control gain of system is decided by a rule of Delta learning. The function of proposed automatic Neural Network gain regulator is verified by speed control experiment results of Induction Motor.