Nonlinear Compensation Method Research Articles

On an Innovative Method of Modeling General Nonlinear Mechanical Systems, Part 1: Theory and Numerical Simulations

This is the first part of a paper on an innovative approach of studying general nonlinear structural dynamic systems. In this part, the theoretical background and some numerical simulations are presented. Firstly, a frequency domain nonlinear compensation method is proposed. It can be used to recover the linear path of a general non-linear system as long as the Volterra expansion is held between the input and the output. Secondly, a new nonlinear element location scheme is developed based on the system linear path identification. Lastly, as a natural extension in the development of the contents of this paper, an in-situ nonlinear restoring force state mapping technique is proposed. The use of these new methods is demonstrated through several numerical examples of either single or multiple DOF systems incorporating some typical types of nonlinearities.

Acceleration based learning control of robotic manipulators using a multi-layered neural network

This paper presents a nonlinear compensation method based on neural networks for trajectory control of robotic manipulators. A multi-layered perceptron neural network (MLP) is used to predict the required actuator torques of a robot to follow a desired trajectory, and these predicted torques are applied to the robot as feedforward compensations in parallel to a linear feedback controller. An acceleration based learning scheme is proposed to adjust the connection weights in the neural network to form an approximated dynamic model of the robot. Simulation results show that the proposed learning scheme improves the speed of error convergence of the system and reduces the convergent error with the efficient adaptation to the changing system dynamics. The validity of the proposed learning scheme is verified through experiments. >

A simple and rapid method for determining the linearity of a flow cytometer amplification system.

We describe a simple and rapid method for determining the linearity of a flow cytometer amplification system. The method is based on a fundamental characteristic of linear amplifiers: The difference between two amplified signals increases linearly with increasing amplifier gain. Two populations of beads or cells, differing slightly in fluorescence intensity, are analyzed by the flow cytometer at increasing photomultiplier tube high-voltage settings. The distribution of the populations' mean difference versus mean position is a straight line intersecting the origin for linear amplifiers. Although some types of nonlinearities cannot be detected with this technique, deviations from linearity indicate nonlinear components in the flow cytometer amplification system. The correlation coefficient is used to quantify degree of nonlinearity. We also describe a method for amplifier nonlinearity compensation.