• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.267-271
• /
• 1996

In this study, performance improvement of the inverse modeling as the on-line control method for the estimation, control experiment is performed. As the modeling errors is occurred in duct system arbitrarily, a case using the filtered-x LMS algorithm only as the control method, a case using tile inverse modeling method only and a case using the inverse modeling with the adaptive line enhancer are compared. The estimation errors between real secondary path transfer functions and the estimated and the control performances of primary noises with these estimated transfer functions are compared.

• ETRI Journal
• /
• v.44 no.5
• /
• pp.805-815
• /
• 2022

The model-based evolutionary algorithms are divided into three groups: estimation of distribution algorithms, inverse modeling, and surrogate modeling. Existing inverse modeling is mainly applied to solve multi-objective optimization problems and is not suitable for many-objective optimization problems. Some inversed-model techniques, such as the inversed-model of multi-objective evolutionary algorithm, constructed from the Pareto front (PF) to the Pareto solution on nondominated solutions using a random grouping method and Gaussian process, were introduced. However, some of the most efficient inverse models might be eliminated during this procedure. Also, there are challenges, such as the presence of many local PFs and developing poor solutions when the population has no evident regularity. This paper proposes inverse modeling using random forest regression and uniform reference points that map all nondominated solutions from the objective space to the decision space to solve many-objective optimization problems. The proposed algorithm is evaluated using the benchmark test suite for evolutionary algorithms. The results show an improvement in diversity and convergence performance (quality indicators).

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.7
• /
• pp.654-661
• /
• 2015

Stairs are the most popular obstacles in buildings and factories. To enlarge the application areas of a field robotic platform, stair-climbing is very important mission. One important reason why a stair-climbing is difficult is that stairs are various in sizes. To achieve autonomous climbing of various-sized stairs, dynamic modeling is essential. In this research, an inverse dynamic modeling is performed to enable an autonomous stair climbing. Stair-climbing robotic platform with flip locomotion, named FilpBot, is analyzed. The FlipBot platform has advantages of robust stair-climbing of various sizes with constant speed, but the autonomous operation is not yet capable. Based on external constraints and the postures of the robot, inverse dynamic models are derived. The models are switched by the constraints and postures to analyze the continuous motion during stair-climbing. The constraints are changed according to the stair size, therefore the analysis results are different each other. The results of the inverse dynamic modeling are going to be used in motor design and autonomous control of the robotic platform.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.24 no.5 s.176
• /
• pp.1175-1182
• /
• 2000

Diffraction systematically causes error in acoustic measurements. Most probes are designed to reduce this phenomenon. On the contrary, this paper proposes a spherical probe a] lowing acoustic inten sity measurements in three dimensions to be made, which creates a diffracted field that is well-defined, thanks to analytic solution of diffraction phenomena. Six microphones are distributed on the surface of the sphere along three rectangular axes. Its measurement technique is not based on finite difference approximation, as is the case for the ID probe but on the analytic solution of diffraction phenomena. In fact, the success of sound source identification depends on the inverse models used to estimate inverse diffraction phenomena, which has nonlinear properties. In this paper, we propose the concept of nonlinear inverse diffraction modeling using a neural network and the idea of 3 dimensional sound source identification with better performances. A number of computer simulations are carried out in order to demonstrate the diffraction phenomena under various angles. Simulations for the inverse modeling of diffraction phenomena have been successfully conducted in showing the superiority of the neural network.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2004.10a
• /
• pp.383-388
• /
• 2004

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty problem. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model, Nueral network and PID control is used as a feedback controller. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.243-246
• /
• 1994

In this paper, a self-learning fuzzy controller is designed with a fuzzy approximation of an inverse model. The aim of an identification is to find an input command which is control of a system output. It is intuitional and easy to use a classical adaptive inverse modeling method for the identification, but it is difficult and complex to implement it. This problem can be solved with a fuzzy approximation of an inverse modeling. The fuzzy logic effectively represents the complex phenomena of the real world. Also fuzzy system could be represented by the neural network that is useful for a learning structure. The rule of a fuzzy inverse model is modified by the gradient descent method. The goal is to be obtained that makes the design of fuzzy controller less complex, and then this self-learning fuzz controller can be used for nonlinear dynamic system. We have applied this scheme to a nonlinear Ball and Beam system.

• Transactions of the Korean Society of Mechanical Engineers
• /
• v.17 no.8
• /
• pp.2043-2050
• /
• 1993

This paper presents a three dimensional modeling and dynamic analysis of a hydraulic excavator. An excavator composed of a boom, a bucket, two boom cylinders, an arm cylinder, and a bucket cylinder is used for the analysis. Each cylinder is modeled to two separate bodies linked by a translational joint. Judging from the actual degrees of freedom of the excavator, proper kinematic joints are selected to exclude redundant constraints in the modeling. In order to find the reaction forces at kinematic joints during operations, inverse dynamic analysis is carried out. Dynamic analysis is also carried out to verify the results from inverse dynamic analysis. The DADS program is used for analysis, with proper modification of the DADS user routine according to various motions.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1997.10a
• /
• pp.349-352
• /
• 1997

A Piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearity is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearity can not be neglected. The hysteresis nonlinearity of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearity problem. And feedforward-feedforward-feedback controller is proposed to give a good tracking performance. The Feedforward controller is inverse hysteresis model, and PID control is sued as a feedback controller. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded hat the proposed control scheme gives good tracking performance.

• Journal of Korean Society for Atmospheric Environment
• /
• v.19 no.E2
• /
• pp.75-81
• /
• 2003

Variational data assimilation, which is recently introduced to the air quality modeling, is a promising tool for obtaining optimal estimates of initial conditions and other important parameters such as emission and deposition rates. In this paper. two advanced techniques for variational data assimilation, based on the adjoint and quasi-inverse methods, are tested for a simple air quality problem. The four-dimensional variational assimilation (4D-Var) requires to run an adjoint model to provide the gradient information in an iterative minimization process, whereas the inverse 3D-Var (I3D-Var) seeks for optimal initial conditions directly by running a quasi -inverse model. For a process with small dissipation, I3D-Vu outperforms 4D-Var in both computing time and accuracy. Hybrid application which combines I3D-Var and standard 4D-Var is also suggested for efficient data assimilation in air quality problems.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.1
• /
• pp.22-29
• /
• 2003

A piezoelectric actuator yields hysteresis effect due to its composed ferroelectric. Hysteresis nonlinearty is neglected when a piezoelectric actuator moves with short stroke. However when it moves with long stroke and high frequency, the hysteresis nonlinearty can not be neglected. The hysteresis nonlinearty of piezoelectric actuator degrades the control performance in precision position control. In this paper, in order to improve the control performance of piezoelectric actuator, an inverse modeling scheme is proposed to compensate the hysteresis nonlinearty. And feedforward - feedback controller is proposed to give a good tracking performance. The Feedforward controller is an inverse hysteresis model, base on neural network and the feedback control is implemented with PID control. To show the feasibility of the proposed controller and hysteresis modeling, some experiments have been carried out. It is concluded that the proposed control scheme gives good tracking performance.