• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.401-405
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• 1993

This paper presents a neuro adaptive control method for nonlinear dynamical systems based on artificial neural network systems. The proposed neuro adaptive controller consists of 3 layers artificial neural network system and parallel PD controller. At the early stage in learning or identification process of the system characteristics the PD controller works mainly in order to compensate for the inadequacy of the learning process and then gradually the neuro contrller begins to work instead of the PD controller after the learning process has proceeded. From the simulation studies the neuro adaptive controller is seen to be robust and works effectively for nonlinear dynamical systems from a practical applicational points of view.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.265-276
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• 2004

Genetic Algorithm(GA), that is shown stable performance to find an optimal solution, has been used as a method of solving large-scaled optimization problems with complex constraints in various applications. Since it takes so much time to execute a long computation process for iterative evolution and adaptation. In this paper, a hardware-based adaptive GA was proposed to reduce the serious computation time of the evolutionary process and to improve the accuracy of convergence to optimal solution. The proposed GA, based on steady-state model among continuos generation model, performs an adaptive mutation process with consideration of the evolution flow and the population diversity. The drawback of the GA, premature convergence, was solved by the proposed adaptation. The Performance improvement of convergence accuracy for some kinds of problem and condition reached to 5-100% with equivalent convergence speed to high-speed algorithm. The proposed adaptive GAP(Genetic Algorithm Processor) was implemented on FPGA device Xilinx XCV2000E of EHW board for face recognition.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.7
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• pp.31-41
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• 1993

In this paper, a fuzzy adaptive controller is proposed for the process with large delay time and unmodelled dynamics. The fuzzy adaptive controller consists of self tuning controller and fuzzy tuning part. The self tuning controller is designed with the continuous time GMV (generalized minimum variance) using emulator and weighted least square method. It is realized by the hybrid method. The controller has robust characteristics by adapting the inference rule in design parameters. The inference processing is tuned according to the operating point of the process having the nonlinear characteristics considering the practical application. We review the characteristics of the fuzzy adaptive controller through the simulation. The controller is applied to practical electric furnace. As a result, the fuzzy adaptive controller shows the better characteristics than the simple numeric self tuning controller and the PI controller.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.259-264
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• 2006

It is well known that the uncertainty of the covariance parameters of the process noise (Q) and the observation errors (R) has a significant impact on Kalman filtering performance. Q and R influence the weight that the filter applies between the existing process information and the latest measurements. Errors in any of them may result in the filter being suboptimal or even cause it to diverge. The conventional way of determining Q and R requires good a priori knowledge of the process noises and measurement errors, which normally comes from intensive empirical analysis. Many adaptive methods have been developed to overcome the conventional Kalman filter's limitations. Starting from covariance matching principles, an innovative adaptive process noise scaling algorithm has been proposed in this paper. Without artificial or empirical parameters to be set, the proposed adaptive mechanism drives the filter autonomously to the optimal mode. The proposed algorithm has been tested using road test data, showing significant improvements to filtering performance.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.2
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• pp.138-144
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• 1992

Conventionally, model equation for cutting process has been used at adaptive control. But in this paper, the cutting force is discerned by piezo electric dynamometer and is controlled adaptively using fuzzy inferance so that the constant load feeding is possible. Main conclusions are as follows : (1) with proper design of fuzzy label, more active cutting force control is possible. (2) adaptive control is possible with only qualitative knowledge instead of model equation of cutting process.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.289-294
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• 1988

This paper describes tbe design and implementation of the adaptive controller to maintain the glood weld quality in gas metal arc welding process. The weld torch travel speed and the surface temperature are taken, respectively, as an input and an output of the welding control system. Because of the very complex phenomena of the process, the input-output dynamic model was experimentally identified by AIC (Akiake Information Criterion). Based on the model structure, the explicit model reference adaptive controller is simulated in order to regulate the output tempernture to the desired level.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.333-338
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• 1990

Most verification of improvements for adaptive control schemes are. dependent on computer simulations, but these computer simulations have much limitation, because (if complex actual conditions of system. This paper is concerned with the constructions of a thermal process system for experiments with various control schemes. This thermal process system is composed of a water tank, PC-XT, AD/DA converters power supply and thermal sensors. We estimate. the algorithms of pole-assignment adaptive control in the manifold disturbances and environments, changing system dynamics. The system equations for thermal press are included.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.629-631
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• 1998

An adaptive joint-process IIR filter with generalized lattice structure is implemented by modifying the conventional lattice filter and making an adaptive algorithm in a system identification problem.

• Journal of Welding and Joining
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• v.33 no.6
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• pp.13-20
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• 2015

Many automotive companies are endeavoring to improve the quality of resistance spot welding by updating body-in-white (BIW) production line with adaptive control spot welding system to compensate the process disturbances such as gap, electrode wear, oxidized surfaces, poor fit up and adhesive etc. Most of the commercial adaptive weld controllers require proper "Initial Welding Schedule" or "Reference weld" to achieve compensation in welding parameters during real time welding. In this study, the compensation of a commercial adaptive weld controller had been observed and analyzed thoroughly for various process disturbances to find optimal initial welding schedule. It was observed that 90 percent of the expulsion current in constant current control as reference welding schedule conferred the maximum button diameter in adaptive control welding. Finally, effects of each disturbance in combined field disturbances system with adaptive control had also been confirmed with the design of experiment (DOE) by minitab(R)16 for combined disturbances situation and suitability of optimum initial weld current had also established with real body part validation test.

• Journal of Welding and Joining
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• v.17 no.6
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• pp.90-99
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• 1999

Significant portion of the total manufacturing time for a pipe fabrication process is spent on the welding following primary machining and fit-up processes. To achieve a reliable weld bead appearance, automatic seam tracking and adaptive control to fill the groove are urgently needed. For the seam tracking in welding processes, the vision sensors have been successfully applied. However, the adaptive filling control of the multi-torches system for the appropriate welded area has not been implemented in the area of SAW(submerged arc welding) by now. The term adaptive control is often used to describe recent advances in welding process control by strictly this only applies to a system which is able to cope with dynamic changes in system performance. In welding applications, the term adaptive control may not imply the conventional control theory definition but may be used in the more descriptive sense to explain the need for the process to adapt to the changing welding conditions. This paper proposed various types of methodologies for obtaining a good bead appearance based on multi-torches welding system with the vision system in SAW. The methodologies for adaptive filling control used welding current/voltage, arc voltage/welding current/wire feed speed combination and welding speed by using vision sensor. It was shown that the algorithm for welding current/voltage combination and welding speed revealed sound weld bead appearance compared with that of voltage/current combination.