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Sensorless Torque Estimation using Adaptive Kalman Filter and Disturbance Estimator

Sensorless Torque Estimation using Adaptive Kalman Filter an...

引用

2010 IEEE/ASME International Conference on Mechatronic and Embedded System and Applications(2010 IEEE 机电一体化和嵌入式系统与应用国际会议)

作者： Sang-Chul Lee Hyo-Sung Ahn Distributed Control and Autonomous Systems Laboratory Department of mechatronics Gwangju Institute of Science and Technology (GIST) Korea

This paper presents a stochastic estimation method and a signal processing based method for estimating disturbance torques without using any force sensors. The first method will address a robustness against measurement noises by estimating noise covariance. The second method will show several practical merits. By containing system models inside of the estimator, the total disturbance torque injected into the plant is estimated. The experimental results conducted using a master-slave manipulator show the validity of two proposed methods.

关键词： Force measurement

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Consensus of Nonlinear System using Feedback Linearization

Consensus of Nonlinear System using Feedback Linearization

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2010 IEEE/ASME International Conference on Mechatronic and Embedded System and Applications(2010 IEEE 机电一体化和嵌入式系统与应用国际会议)

作者： Taekyung Lee Hyo-Sung Ahn Distributed Control and Autonomous Systems Laboratory Department of mechatronics Gwangju Institute of Science and Technology (GIST) Korea

In this paper, consensus of nonlinear systems is suggested with the linear consensus algorithm and feedback linearization. If the nonlinear system satisfies the controllable and involutive conditions, then there exist diffeomorphism. The nonlinear system can be transformed into the form of high order integrator by feedback linearization. Therefore the high order linear consensus algorithm can be applied to achieve consensus of the nonlinear systems that is feedback linearized. By satisfying the conditions that is related with network structure and gains of the consensus algorithm, the consensus of nonlinear systems is achieved through feedback linearziation and the linear consensus algorithm. Moreover this result is verified through the simulation for consensus of robot manipulators with nonlinear dynamics.

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Convergence of multiagent Q-learning: Multi action replay process approach

Convergence of multiagent Q-learning: Multi action replay pr...

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IEEE International Symposium on Intelligent control (ISIC)

作者： Han-Eol Kim Hyo-Sung Ahn Distributed Control and Autonomous Systems Laboratory Graduate School of Mechatronics Gwangju Institute of Science and Technology Gwangju South Korea

In this paper, we first suggest a new type of Markov model extended by Watkins' action replay process. The new Markov model is called multi-action replay process (MARP), which is a process designed for multiagent coordination on the basis of reward values, state transition probabilities, and equilibrium strategy taking account of joint-action among agents. Using this model, multiagent Q-learning algorithm is then constructed as a cooperative reinforcement learning algorithm under completely connected agents. Finally, we prove that multiagent Q-learning values converge to optimal values. Simulation results are reported to illustrate the validity of the proposed multiagent Q-learning algorithm.

关键词： Mathematical model Games Equations Convergence Markov processes Algorithm design and analysis Learning

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Iterative Learning control for Optimal Multiple-Point Tracking

Iterative Learning Control for Optimal Multiple-Point Tracki...

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IEEE Conference on Decision and control

作者： Tong Duy Son Dinh Hoa Nguyen Hyo-Sung Ahn Distributed Control and Autonomous Systems Laboratory Department of Mechatronics Gwangju Institute of Science and Technology (GIST) Korea Department of Information Physics and Computing Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9781612848006

This paper presents a new optimization-based iterative learning control (ILC) framework for multiple-point tracking control. Conventionally, one demand prior to designing ILC algorithms for such problems is to build a reference trajectory that passes through all given points at given times. In this paper, we produce output curves that pass close to the desired points without considering the reference trajectory. Here, the control signals are generated by solving an optimal ILC problem with respect to the points. As such, the whole process becomes simpler;key advantages include significantly decreasing the computational cost and improving performance. Our work is then examined in both continuous and discrete systems.

关键词： Iterative learning control Discrete systems control SIGNAL trajectory

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An interpolation method of multiple terminal iterative learning control

An interpolation method of multiple terminal iterative learn...

引用

IEEE International Symposium on Intelligent control (ISIC)

作者： Tong Duy Son Dinh Hoa Nguyen Hyo-Sung Ahny Distributed Control and Autonomous Systems Laboratory Gwangju Institute of Science and Technology (GIST) Korea Department of Information Physics and Computing Graduate School of Information Science and Technology University of Tokyo Japan Department of Information Physics and Computing The University of Tokyo Japan Gwangju Institute of Science and Technology Gwangju KR

In this paper, we present an iterative learning control (ILC) algorithm to track specified desired multiple terminal points at given time instants. A framework to update the desired trajectories from given points is developed based on the interpolation technique. The approach shows better rate of convergence of the errors. The simulation with a satellite antenna control model is demonstrated to show the effectiveness of our approach.

关键词： Trajectory Convergence Interpolation Polynomials Antennas Algorithm design and analysis

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