• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1380-1385
• /
• 2004

In this paper, auto-tuning technique of the PID controller gain by particle swarm optimization algorithm is presented. PID controller is easy to implement to numerous control systems. After PID gain tuning is completed, its result could be implemented to control spacecraft vibration such as jitter that is high frequency vibration usually over 10Hz. The off-line PID controller tuning is done under system nonlinearities and uncertainties existence, then its result is applied to control experiment device to prove the PSO efficiencies.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.12
• /
• pp.1150-1158
• /
• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of the Korean Society for Precision Engineering
• /
• v.30 no.1
• /
• pp.11-17
• /
• 2013

'Multi-scale mass-deployable cooperative robots' is a next generation robotics paradigm where a large number of robots that vary in size cooperate in a hierarchical fashion to collect information in various environments. While this paradigm can exhibit the effective solution for exploration of the wide area consisting of various types of terrain, its technical maturity is still in its infant state and many technical hurdles should be resolved to realize this paradigm. In this paper, we propose to develop new design and manufacturing methodologies for the multi-scale mass-deployable cooperative robots. In doing so, we present various fundamental technologies in four different research fields. (1) Adaptable design methods consist of compliant mechanisms and hierarchical structures which provide robots with a unified way to overcome various and irregular terrains. (2) Soft composite materials realize the compliancy in these structures. (3) Multi-scale integrative manufacturing techniques are convergence of traditional methods for producing various sized robots assembled by such materials. Finally, (4) the control and communication techniques for the massive swarm robot systems enable multiple functionally simple robots to accomplish the complex job by effective job distribution.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.5
• /
• pp.526-538
• /
• 2007

This paper presents a neural network based decentralized excitation controller design for large-scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem control activities are guaranteed through rigorous stability analysis. Neural networks in the controller design are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded. To evaluate its performance, the proposed controller design is compared with conventional controllers optimized using particle swarm optimization. Simulations with a three-machine power system under different disturbances demonstrate the effectiveness of the proposed controller design.