• Journal of the Korean Institute of Telematics and Electronics
• /
• v.27 no.3
• /
• pp.145-155
• /
• 1990

In this paper, an adaptive Infinite Impulse Response (IIR) filter algorithm using output error method, which prevents poles of a system transfer function from being out of unit circle, is proposed, and it is proved that the proposed algorithm always satisfies hyperstability. The proposed algorithm is applied to an Adaptive Noise Canceller (ANC), and compared with a Least Square (LS) method adaptive IIR filter algorithm and an adaptive Finite Inpulse Response (FIR) filter algorithm. As a result, the validity of the proposed algorithm is proved.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.05a
• /
• pp.574-577
• /
• 2000

The purpose of robot manipulator control is to make for manipulator take a trace of pre-planned trajectory. In this study, the algorithm of MRAC(Model Reference Adaptive Control) on reference to adaptive control theory was studied. The experiments were performed on 6-DOF robot manipulator with respect to p-d(proportional-differential) controller and adaptive controller. The property of adaptive control was studied and its efficiency proved by being compared to p-d controller.

• Journal of Ocean Engineering and Technology
• /
• v.10 no.1
• /
• pp.108-118
• /
• 1996

This paper presents automatic reentry of a deepsea reser by adaptive control. Reentry is one of the major pro blems regarding a deepsea riser. In the reentry operation, the lower end of riser must be accurately positioned over the tarket point on the seabed. But the deepsea riser shows complex elastic response due to flexibility and nonlinearity of the riser dynamics and the required positioning accuracy is high. Moreover, elastic deformation must by controlled for securing structural integrity. In adaptive control, uncertainly known parameters like added mass and drag coefficient in the riser dynamics are identified and control forces at the floating body and the riser are calculated simultaneously. An Adaptive algorithm for MIMO linear discrete time system without requiring a persistent excitation is adopted in this study. The effectiveness of adaptive control logic is tested by numerical simulation and model experiment. The designed control system shows good overall performances, so that the present study can be applied to the control of the deepsea riser.

• Proceedings of the IEEK Conference
• /
• 2001.06a
• /
• pp.89-92
• /
• 2001

In this paper, adaptive multi-stage parallel interference cancellation (PIC) receiver is considered for a multi-rate DS-CDMA system. In each stage of the adaptive multi-stage PIC receiver, multiple access interference (MAI) estimates are obtained using the sub-bit estimates from the Previous stage and the adaptive weights for the sub-bit estimates. The adaptive weights are obtained by minimizing the mean squared error between the received signal and its estimate through a least mean square (LMS) algorithm. It is shown that the adaptive multi- stage PIC receiver achieves smaller BER than the matched filter receiver, multi-stage PIC receiver, and multi-stage partial PIC receiver for the multi-rate DS-CDMA system in a Rayleigh fading channel.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.1507-1508
• /
• 2008

In this paper, we propose a direct robust adaptive backstepping speed controller for induction motors system. A robust adaptive backstepping controller is designed using high order neural networks(HONN), which avoids the singularity problem in adaptive nonlinear control. The stability of the resulting adaptive system with proposed adaptive controller is guaranteed by suitable choosing the design parameter and initial conditions. HONN are used to approximate most of uncertainties which are derived from unknown motor parameters, load torque disturbances and unknown nonlinearities. The applicability of the proposed scheme is tested simulation.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2004.10a
• /
• pp.193-200
• /
• 2004

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.19 no.2
• /
• pp.241-250
• /
• 2010

A recurrent adaptive model-free intelligent control with a friction estimation law is proposed to enhance the positioning performance of the mover in PMLSM system. For the PMLSM with nonlinear friction and uncertainty, an adaptive recurrent fuzzy neural network(ARFNN) and compensated control law in $H_{\infty}$ performance criterion are designed to mimic a perfect control law and compensate the approximated error between ideal controller and ARFNN. Combined with friction observer to estimate nonlinear friction parameters of the LuGre model, on-line adaptive laws of the controller and observer are derived based on the Lyapunov stability criterion. To analyze the effectiveness our control scheme, some simulations for the PMLSM with nonlinear friction and uncertainty were executed.

• Journal of Advanced Marine Engineering and Technology
• /
• v.27 no.3
• /
• pp.388-394
• /
• 2003

Hydraulic system is difficult to obtain a suitable performance due to the nonlinearity load pressure change and system parameter variation. The requirement of control a1gorithm has been complex in order to satisfy the performance. The adaptive control is a control method which is suggested to achieve the control object under the plant characteristics change. In spite of the case that plant characteristics and the degree of variation are difficult to grasp. the adaptive control could keep the characteristics of closed-loop system generally. In this study. a method of combined generalized minimum variance adaptive control (GMVAC) and output error feedback is proposed, in order to solve the problem of non-minimum phase of plant and the vibration and overshoot in initial response. The control performance according to the variation of characteristics of plant is evaluated by changing the supply pressure. The experimental results show the effectiveness of the proposed scheme.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.1
• /
• pp.231-238
• /
• 2014

Adaptive Maximum Torque Per Amp (Adaptive MTPA) control for induction motor drives seeks to achieve a desired torque with the minimum possible stator current regardless of operating points. This is favorable in terms of inverter operation and nearly optimal in terms of motor efficiency. However, the Adaptive MTPA control was validated only from the viewpoint of tracking a desired torque and was not shown that the desired torque is achieved with minimum possible stator current. This work experimentally demonstrates that optimal condition for Adaptive Maximum Torque Per Amp Control Strategy is achieved regardless of rotor resistance variation.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1995.04b
• /
• pp.262-266
• /
• 1995

This paper presents a new adaptive-neuro control scheme to control the velocity and position of SCARA robot with parameter uncertainties. The adaptive control of linear system found wiedly in many areas of control application. While techniques for the adaptive control of linear systems have been well-established in the literature, there are a few corresponding techniques for nonlinear systems. In this paper an attempt is made to present a newcontrol scheme for theadaptive control of ponlinear robot based on a feedforward neural network. The proposed approach incorporates a neuro controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface Emphasis is focused on the fact that the adaptive-neuro controoler dose not need any input/output information about the controlled system. The simulation result illustrates the effectiveness of the proposed adaptive-neuro control scheme.