• 유공압시스템학회:학술대회논문집
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• 유공압시스템학회 2010년도 춘계학술대회
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• pp.81-85
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• 2010

This paper deals with the issue of motion control of a single rod cylinder-load system using simple adaptive control (SAC) method. Prior to controller design, the experiment of open-loop response has been performed. Based on it, design parameters of transfer function are obtained. The effect of parallel feedforward compensator has been investigated by computer simulation, suppressing the oscillatory motion. Through experiments it is conformed that the SAC method gives good tracking performance compared to the PD control method.

• 제어로봇시스템학회논문지
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• 제18권3호
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• pp.237-242
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• 2012

AFC (Adaptive Feedforward Control) is significantly employed for improving control performance of dynamic systems particularly involving periodic disturbance signals in engineering fields. This paper presents a novel hybrid AFC approach for discrete-time systems with multiple disturbances in terms of control input and state variables. The proposed AFC mechanism is hierarchically composed of a conventional feedforward control framework and PID auxiliary control configuration in parallel. The former is generic to decrease periodic disturbance excited to control actuators and the latter is additionally constructed to overcome control deterioration due to time-varying uncertainty under given systems. We carry out numerical simulation to test reliability of our proposed hybrid AFC system and compare its control performance to a well-known conventional AFC method with respect to time and frequency domains for proving of its superiority.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.418-423
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• 1992

In this paper, a model reference adaptive control algorithm is applied to the design of the normal acceleration controller for missiles with nonminimum-phase characteristics. The method used in this paper is due to Ohkubo. In this scheme, a feedforward compensator is designed first so that the extended system becomes minimum-phase and after that an adaptive control algorithms is designed for the extended system. The feedforwrd compensator is obtained by solving the robust stabilization problem. It is shown that the performance of the designed controller is satisfied via computer simulation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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• pp.183-187
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• 1989

This paper presents on approach to the position control of a robot manipulator by using a decentralized adaptive control scheme. The large scale system is regarded as the system which consists of many subsystems having interconnection. In each subsystem, a local control system is composed by feedforward and feedback component, one computes the nominal torque from the Newton-Euler equation, the other computes the perturbation equation which reduce the position error of the manipulator along the nominal trajectory. A computer simulation studies was conducted to evaluate and compare the performances of the proposed manipulator control scheme with those of the PD control and centralized control schemes.

• 한국해양공학회지
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• 제8권2호
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• pp.131-140
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• 1994

Dynamics of AUV has heavy nonlinearities and many unknown parameters due to its bluff shape and low cruising speed. Intelligent algorithms, therefore, are required to overcome these nonlinearities and unknown system dynamics. Several identification techniques have been suggested for the application of control of underwater vehicles during last decade. This paper applies the neural network to identification and motion control problem of AUVs. Nonlinear dynamic systems of an AUV are identified using feedforward neural network. Simulation results show that the learned neural network can generate the motion of AUV. This paper, also, suggest an adaptive control scheme up-dates the controller weights with reference model and feedforward neural network using error back propagation.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1911-1916
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• 2005

In this study, the basic motor control system had been investigated. The Discrete-Time Feedback Error Learning (DTFEL) method is used to control this system. This method is anologous to the original continuous-time version Feedback Error Learning(FEL) control which is proposed as a control model of cerebellum in the field of computational neuroscience. The DTFEL controller consists of two main parts, a feedforward controller part and a feedback controller part. Each part will deals with different control problems. The feedback controller deals with robustness and stability, while the feedforward controller deals with response speed. The feedforward controller, used to solve the tracking control problem, is adaptable. To make such the tracking perfect, the adaptive law is designed so that the feedforward controller becomes an inverse system of the controlled plant. The novelty of FEL method lies in its use of feedback error as a teaching signal for learning the inverse model. The PD control theory is selected to be applied in the feedback part to guarantee the stability and solve the robust stabilization problems. The simulation of each individual part and the integrated one are taken to clarify the study.

• Advances in aircraft and spacecraft science
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• 제6권5호
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• pp.349-369
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• 2019

In the design of flight control systems there are issues that deserve special consideration and attention such as external perturbations or systems failures. A Simple Adaptive Controller (SAC) that does not require a-priori knowledge of the faults is proposed in this paper with the aim of realizing a fault tolerant flight control system capable of leading the pitch motion of an aircraft. The main condition for obtaining a stable adaptive controller is the passivity of the plant; however, since real systems generally do not satisfy such requirement, a properly defined Parallel Feedforward Compensator (PFC) is used to let the augmented system meet the passivity condition. The design approach used in this paper to synthesize the PFC and to tune the invariant gains of the SAC is the Population Decline Swarm Optimization ($P_DSO$). It is a modification of the Particle Swarm Optimization (PSO) technique that takes into account a decline demographic model to speed up the optimization procedure. Tuning and flight mechanics results are presented to show both the effectiveness of the proposed $P_DSO$ and the fault tolerant capability of the proposed scheme to control the aircraft pitch motion even in presence of elevator failures.

• 한국소음진동공학회논문집
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• 제19권3호
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• pp.288-293
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• 2009

This study presents the active noise control of duct noise. The duct was excited by a steady-state harmonic and white noise force and the control was performed by one control speaker attached to surface of the duct. An adaptive controller based on filtered x LMS(FXLMS) algorithm was used and controller was defined by minimizing the square of the response of the error microphone. The assemble controller, which is called a hybrid ANC(active noise control) system, was combined with feedforward and feedback controller. The feedforward ANC attenuates primary noise that is correlated with the reference signal, while the feedback ANC cancels the narrowband components of the primary noise that are not observed by the reference sensor. Furthermore, in many ANC applications, the periodic components of noise are the most intense and the feedback ANC system has the effect of reducing the spectral peaks of the primary noise, thus easing the burden of the feedforward ANC filter.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.262-266
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• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1996년도 춘계학술대회 논문집
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• pp.472-477
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• 1996

This paper presents a new approachtothe design of adaptive control system using DSPs(TMS320C30) for robotic manipulators to achieve 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 control 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 adaaptive feedforward controller, feedback controller, and PID type time-varying auxillary control elements. The prpposed adaptive control scheme is simple in structure, fast in computation, and suitable for implementation of real-time control. Moreover, this scheme does not require an accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the adaptive controller is illustrated by simulation and experimental results for a SCARA robot.