• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.148.5-148
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• 2001

In this report, an adaptive flight control law based on a linear-parameter-varying (LPV) model is presented for a flight control system. The control system is designed to track an output of a vehicle to a reference signal from the guidance system, which generates a reference flight path. The proposed adaptive control law adjusts the controller gains continuously on line as flight conditions change. The obtained adaptive controller guarantees global stability over a wide flight envelope. Computer simulation involving six-degree-of-freedom nonlinear flight dynamics is applied to Japan´s automatic landing flight experimental vehicle (ALFLEX) to examine the effectiveness of the proposed adaptive flight control law.

• Journal of Advanced Marine Engineering and Technology
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• v.27 no.5
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• pp.609-616
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• 2003

The purpose of this study is to design the adaptive speed control system of a marine diesel engine by combining the Model Matching Method and the Nominal Model Tracking Method. The authors proposed already a new method to determine efficiently the PID control Parameters by the Model Matching Method. typically taking a marine diesel engine as a non-oscillatory second-order system. But. actually it is very difficult to find out the exact model of a diesel engine. Therefore, when diesel engine model and actual diesel engine are unmatched as an another approach to promote the speed control characteristics of a marine diesel engine, this paper Proposes a Model Reference Adaptive Speed Control system of a diesel engine, in which PID control system for the model of a diesel engine is adopted as the nominal model and Fuzzy controller and derivative operator are adopted as the adaptive controller.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.162-169
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• 1999

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator using digital Signal Processors. Digital signal processors DSPs. are micro-processors that are particularly developed for variables. Digital version of most advanced control algorithms can be defined as sums and products of measured variables, thus it can be programmed and executed through DSPs. In addition, DSPs are as fast in computation as most 32-bit micro-processors and yet at a fraction of their prices. These features make DSPs a biable computatinal tool in digital implementation of sophisticated controllers. 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. The proposed adaptive-neuro control scheme is illustrated to be a efficient control scheme for implementation of real-time control of robot system by the simulation and experiment.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.10
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• pp.94-104
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• 1996

This paper presents a new approach to the design of adaptive control system using DSPs(TMS320C31) for robotic manpulators to achieve accurate trajectory tracking by the joint angles Digital signal processors are used in implementing real time adaptive control algorithms to provide an enhanced motion control for robotic manipulators. In the proposed contorl scheme, adaptation laws are derived from the improved Lyapunov second stability analysis method based on the adaptive model reference control theory. The adaptive controller consists of an adaptive feedforward controller, feedback controller, and PID type time varying auxillary control elements. The proposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Morever, this scheme does not require an accurate dynamic modeling nor values of manpipulator parameters and payload. Performance of the adaptive controller is illustated by simulation and experimental results for a SCARA robot.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.316-321
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• 2003

This paper presents a design method of the wavelet neural network based controller using direct adaptive control method to deal with a stable intelligent control of chaotic systems. The various uncertainties, such as mechanical parametric variation, external disturbance, and unstructured uncertainty influence the control performance. However, the conventional control methods such as optimal control, adaptive control and robust control may not be feasible when an explicit, faithful mathematical model cannot be constructed. Therefore, an intelligent control system that is an on-line trained WNN controller based on direct adaptive control method with adaptive learning rates is proposed to control chaotic nonlinear systems whose mathematical models are not available. The adaptive learning rates are derived in the sense of discrete-type Lyapunov stability theorem, so that the convergence of the tracking error can be guaranteed in the closed-loop system. In the whole design process, the strict constrained conditions and prior knowledge of the controlled plant are not necessary due to the powerful learning ability of the proposed intelligent control system. The gradient-descent method is used for training a wavelet neural network controller of chaotic systems. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with application to the chaotic systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.1
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• pp.102-108
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• 2008

This paper presents robust and adaptive control method for complicated three dimensional crane systems with uncertain effect. We consider an overhead crane system in which a trolly located on its top is moved to x- and y-axis independently. We first approximate the complicated crane model through linearization approach to simply construct a PD control and then design an adaptive control system for compensating modeling error and control deviation which is feasibly occurred due to system perturbation in practice. An adaptive control scheme is analytically derived using Lyapunov stability theory for a given bound of system perturbation. We accomplish numerical simulation for evaluation of the proposed control system and demonstrate its superiority comparing with the traditional control strategy.

• Journal of the Korean Society of Industry Convergence
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• v.19 no.1
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• pp.18-30
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• 2016

In this paper, we propose a new technique to the design and real-time control of an adaptive controller for robotic manipulator based on digital signal processors. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot manipulator with eight joints. joint space and cartesian space.

• Proceedings of the KIEE Conference
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• 1997.11a
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• pp.136-138
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• 1997

This paper proposes a design method to improve the performance of Indirect Adaptive Controllers using a CP net. This hybrid control architecture consists of Indirect Adaptive Controllers and CP net Controller. The performance of a single Adaptive Controller, multi Adaptive Controllers and the proposed model is compared by control problems. The simulation results show that the proposed model is superior to the others in most cases, in regard of not only learning speed but also control problems.

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.249-255
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• 2017

Efficient operation of induction motor at light loads has been getting wide attention recently because the operating of induction motor at light loads occupies big portion of its operating regions in many applications such as environment friendly vehicle. As one of approaches to improve efficiency, Adaptive Maximum Torque Per Amp (Adaptive MTPA) control for induction motor drives has been proposed to achieve a desired torque with the minimum possible stator current. However, the Adaptive MTPA control was validated only at heavy load where, in general, control scheme tends to perform better than at light loads since the error in measurement of sensors is lower and signal to noise is better. Thus, although the performance of a control scheme is good at rated operating point, its performance at light load is somewhat in doubt in practice. This has led to considerable interest in efficiency of Adaptive MTPA control at light loads. This work experimentally demonstrates performance of Adaptive MTPA control at light loads regardless of rotor resistance variation, thus showing its good performance over all operating conditions.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.10a
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• pp.150-155
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• 1996

This paper presents a new approach to the design of self-tuning adaptive control system that is robust to the changing dynamic configuration as well as to the load variation factors using digital signal processors for robot manipulators. TMS3200C50 is used in implementing real-time adaptive control algorithms provide advanced performance for robot manipulator. In this paper an adaptive control scheme is proposed in order to design the pole-placement self-tuning controller which can reject the offset due to any load disturbance without a detailed description of robot dynamics. parameters of discrete-time difference model are estimated by the recursive least-square identification algorithm and controller parameters are detemined by the pole-placement method. Performance of self-tuning adaptive controller is illusrated by the simulation and experiment for a SCARA robot.