• 한국기계연구소 소보
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• s.19
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• pp.125-139
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• 1989

As an adaptive control function generator, the CMAC (Cerebellar Model Arithmetic or Articulated Controller) based learning control has drawn a great attention to realize a rather robust real-time manipulator control under the various uncertainties. There remain, however, inherent problems to be solved in the CMAC application to robot motion control or perception of sensory information. To apply the CMAC to the various unmodeled or modeled systems more efficiently, it is necessary to analyze the effects of the CMAC control parameters on the trained net. Although the CMAC control parameters such as size of the quantizing block, learning gain, input offset, and ranges of input variables play a key role in the learning performance and system memory requirement, these have not been fully investigated yet. These parameters should be determined, of course, considering the shape of the desired function to be trained and learning algorithms applied. In this paper, the interrelation of these parameters with learning performance is investigated under the basic learning schemes presented by authors. Since an analytic approach only seems to be very difficult and even impossible for this purpose, various simulations have been performed with pre specified functions and their results were analyzed. A general step following design guide was set up according to the various simulation results.

• ETRI Journal
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• v.32 no.5
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• pp.827-830
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• 2010

For an adaptive cruise control (ACC) stop-and-go system in automotive applications, three radar sensors are needed because two 24 GHz short range radars are used for object detection in an adjacent lane, and one 77 GHz long-range radar is used for object detection in the center lane. In this letter, we propose a single sensor-based 24 GHz radar with a detection capability of up to 150 m and ${\pm}30^{\circ}$ for an ACC stop-and-go system. The developed radar is highly integrated with a high gain patch antenna, four channel receivers with GaAs RF ICs, and back-end processing board with subspace based digital beam forming algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.469-474
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• 1990

The implementation of modern guidance law derived from optimal control theory requires accurate current states of target, for example, position, velocity and acceleration etc. But there is no sensors that measure the target states directly. So they are estimated from measurable data. For atmospheric missile engagement, direct application of the modern guidance laws may result In deterioration of Intercept performance because of poor observability associated with angles only-measurements by passive seeker and homing geometry. In this paper, a trajectory modulation method called "adaptive Intermittent maneuvers" is added to the modern guidance law, so the observability is enhanced and, consequently, improved the intercept performance. The estimation algorithm called "modified gain pseudo-measurement filter" is used for tracking filter. It is assumed that the passive seeker measure the angles between line of sight and Inertial frame. The Monte-Carlo simulation for realistic air-to-air Intercept scenario are conducted to demonstrate the effectiveness of intermittent maneuvers.ermittent maneuvers.

• The Journal of the Acoustical Society of Korea
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• v.15 no.3E
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• pp.66-73
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• 1996

In this paper, we propose the filtered-x least mean fourth (LMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the filtered-x LMF adaptive filter to active noise control(ANC) requires estimating of the transfer characteristic of the acoustic path between the output and error signal of the adaptive controller. The results of the convergence analysis of the filtered-x LMF algorithm indicates that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that convergence behavior can differ depending on the relative sizes of the Gaussian measurement noise and convergence constant.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2729-2731
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• 2000

In this paper, we propose an adaptive controller using RBFN(radial basis function network) for robot manipulators. The structure of the proposed controller consists of a RBFN and a fixed gain PD controller. On the basis of the Lyapunov stability theorem, we guarantee the UUB (uniformly ultimately boundedness) for the total system. And the learning law of RBFN is established by the Lyapunov method. Finally, we apply the proposed controller to tracking control for the 2 link SCARA type robot manipulator.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.11
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• pp.1506-1516
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• 1995

In this paper, we propose the filtered-x least mean fourth (FXLMF) algorithm where the error raised to the power of four is minimized and analyze its convergence behavior for a multiple sinusoidal acoustic noise and Gaussian measurement noise. Application of the FXLMF adaptive filter to active noise control requires to estimate the transfer characteristics of the acoustic path between the output and the error signal of the adaptive controller. The results of the convergence analysis of the FXLMF algorithm indicate that the effects of the parameter estimation inaccuracy on the convergence behavior of the algorithm are characterized by two distinct components : Phase estimation error and estimated gain. In particular, the convergence is shown to be strongly affected by the accuracy of the phase response estimate. Also, we newly show that the convergence behavior can differ depending on the relative sizes of the Gaussian noise and the convergence constant.

• Journal of Aerospace System Engineering
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• v.11 no.5
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• pp.28-35
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• 2017

Magnetic Suspension and Balance System (MSBS) demonstrates the capacity to levitate an experimental model absent any mechanical contact using magnetic forces and moments. It allows precise control of position and attitude of the model, and measures external forces and moments acting on the model. For the purpose of acquisition of reliable experimental results under stable and safe conditions, the performance and robustness of the position and attitude control system of MSBS needs to be improved. To this end, Iterative Feedback Tuning (IFT) and L1 adaptive output feedback algorithm were employed to automatically increase command following performance and to ensure robust operation of MSBS with failure of electric power supply. The applicability was validated using computational simulation.

• Journal of the Korean Society of Propulsion Engineers
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• v.16 no.3
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• pp.34-40
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• 2012

A pressure control law to regulate pressure of gas generator is suggested. To design a model based control law, the governing equation which consists of Robert and conservation equation is built and verified through the ground burning test. PID and nonlinear adaptive control laws are designed to analyze the loop response characteristics under the system which has varying eigen properties arisen from combustion volume change. It is suggested that new approach, gain scheduling design, is required to overcome the defects identified from numerical simulation results of the two control laws. The newly suggested scheme shows good control performance even under disturbances and measurement noise.

• International Journal of Aeronautical and Space Sciences
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• v.12 no.1
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• pp.78-83
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• 2011

Traditional autopilot design requires an accurate aerodynamic model and relies on a gain schedule to account for system nonlinearities. This paper presents the control architecture applied to a dynamic model inversion at a single flight condition with an on-line neural network (NN) in order to regulate errors caused by approximate inversion. This eliminates the need for an extensive design process and accurate aerodynamic data. The simulation results using a developed full nonlinear 6 degree of freedom model are presented. This paper also presents the stability evaluation for control systems to which NNs were applied. Although feedback can accommodate uncertainty to meet system performance specifications, uncertainty can also affect the stability of the control system. The importance of robustness has long been recognized and stability margins were developed to quantify it. However, the traditional stability margin techniques based on linear control theory can not be applied to control systems upon which a representative non-linear control method, such as NNs, has been applied. This paper presents an alternative stability margin technique for NNs applied to control systems based on the system responses to an inserted gain multiplier or time delay element.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.618-623
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• 2011

A pressure control law to regulate pressure of gas generator is suggested. To design a model based control law, the governing equation which is consisted of Robert and conservation equation is built and verified through the ground burning test. PID and nonlinear adaptive control laws are designed to evaluate the loop response characteristics under the system which has varying eigen properties as combustor volume is increased. It is suggested that new approach, gain scheduling design, is required to overcome the defects identified from numerical simulation results of the two control laws. The newly suggested scheme showed good control performance even under disturbances and measurement noise.