Jin S. Lee

An iterative learning control of robot manipulators

An iterative learning scheme comprising a unique feedforward learning controller and a linear feedback controller is presented. In the feedback loop, the fixed-gain PD controller provides a stable open neighborhood along a desired trajectory. In the feedforward path, on the other hand, a learning control strategy is exploited to predict the desired actuator torques. It is shown that the predicted actuator torque converges to the desired one as the iteration number increases. The convergence is established based on the Lyapunov stability theory. The proposed learning scheme is structurally simple and computationally efficient. Moreover, it possesses two major advantages: the ability to reject unknown deterministic disturbances and the ability to adapt itself to the unknown system parameters. >

Global Stability of FAST TCP in Single-Link Single-Source Network

We consider a single-link single-source network with FAST TCP source, and propose a static approximation of queuing delay dynamics at the link. The static approximation turns out to be a form with network feedback delay, which enables to analyze FAST TCP reflecting the effect of network feedback delay. Based on a continuous-time dynamic model of FAST TCP, we achieve the boundedness of window size and a sufficient condition for global asymptotic stability. The simulation results illustrate the validity of the sufficient condition for global asymptotic stability.

An adaptive learning control of uncertain robotic systems

An iterative learning control scheme for precise tracking and parameter estimation of uncertain robotic systems is presented. The learning control scheme is globally convergent in the presence of disturbances and parameter variations. Under the persistent excitation condition in the domain of iteration sequence, it is proved that the estimated system parameters converge to the desired ones. The parameter estimator of the proposed learning control scheme does not use any system acceleration and inversion of the estimated inertia matrix, which makes the controller implementation more practical.<<ETX>>

Least squares identification of monotonic fuzzy systems

This paper introduces a least squares (LS) based identification method of fuzzy systems constrained by monotonic input-output relationship. We first present sufficient conditions on the parameters of a fuzzy system under which the output of the system is monotonic with respect to its input. We then formulate monotonic fuzzy systems identification method as LS problem with a nonnegativity constraint, which represents the derived sufficient conditions. Using Lawson and Hansons nonnegative LS algorithm, we suggest a systematic identification method that guarantees monotonicity of the identified fuzzy system. Moreover, using the recursive LS algorithm, we suggest a recursive fuzzy identification technique that keeps monotonicity at every identification step.

Development of Defect Classification Algorithm for POSCO Rolling Strip Surface Inspection System

Surface inspection system (SIS) is an integrated hardware-software system which automatically inspects the surface of the steel strip. It is equipped with several cameras and illumination over and under the steel strip roll and automatically detects and classifies defects on the surface. The performance of the inspection algorithm plays an important role in not only quality assurance of the rolled steel product, but also improvement of the strip production process control. Current implementation of POSCO SIS has good ability to detect defects, however, classification performance is not satisfactory. In this paper, we introduce POSCO SIS and suggest a new defect classification algorithm which is based on support vector machine technique. The suggested classification algorithm shows good classification ability and generalization performance

Global Exponential Stability of FAST TCP

We consider a single-link multi-source network with the FAST TCP sources. We propose a continuous-time dynamic model for the FAST TCP sources and a static model to describe the queuing delay behavior at the link. The proposed model turns out to be in a form revealing the network feedback delay, which allows us to analyze FAST TCP in due consideration of the network feedback delay. Based on the proposed model, we show the boundedness of both each sources congestion window and the queuing delay at the link; and the global exponential stability under a trivial condition that each sources congestion control parameter a is positive. The simulation results illustrate the validity of the proposed model and the global exponential stability of FAST TCP

Backstepping control design of flexible joint manipulator using only position measurements

A tracking controller is presented for flexible joint robot manipulators with only position measurements and without any restrictions on the joint stiffness gains. The controller is developed based on the integrator backstepping design method and on the two observers: one for the filtered link velocity errors and the other for the actuator velocities. The proposed controller achieves exponential tracking of link positions and velocities while keeping all internal signals bounded. It also guarantees exponential convergence of the estimated signals to their actual ones.

Monotonic Fuzzy Systems As Universal Approximators For Monotonic Functions

Abstract In this paper, we propose a constructive method to develop a fuzzy system having a monotonic input-output relationship and prove that the developed fuzzy system can approximate any continuously differentiable monotonic function with any desired degree of accuracy. The fuzzy system is constructed with complete and consistent input membership functions and imposes special parametric constraints on the consequent part of the fuzzy rules. The monotonicity property and approximation capability of the developed fuzzy system are demonstrated using numerical examples.

Parameter conditions and least squares identification of single-input single-output convex fuzzy system

This paper presents fuzzy parameter conditions and a least squares (LS) based method to identify fuzzy systems that have convex input-output relationships. We first suggest sufficient conditions on parameters of zeroth order Takagi- Sugeno-Kang (TSK) type convex fuzzy systems and first order TSK-type convex fuzzy systems. By using the suggested conditions, we propose an LS based fuzzy system identification method whose system preserves the convexity property. Simulation results show that the proposed identification method preserves the convexity property and has less error than the conventional LS method.

Learning control of cooperating robot manipulators handling an unknown object

A learning control scheme is proposed for learning of cooperating multiple-robot manipulators which handle a rigid object under the uncertain operating conditions such as parametric uncertainty, unknown external disturbance and the commonly held object being unknown. The learning controllers drive the multiple-robot manipulators to follow the desired Cartesian trajectory with the desired internal forces to the unknown object. It is also shown, after perfect learning, the learning controllers provide an approximate inverse dynamics solution along the desired object trajectory with the desired internal force.