Adaptive Hammerstein Predistorter Using the Recursive Prediction Error Method

The digital baseband predistorter is an effective technique to compensate for the nonlinearity of power amplifiers （PAs） with memory effects. However, most available adaptive predistorters based on direct learning architectures suffer from slow convergence speeds. In this paper, the recursive prediction error method is used to construct an adaptive Hammerstein predistorter based on the direct learning architecture, which is used to linearize the Wiener PA model. The effectiveness of the scheme is demonstrated on a digital video broadcasting-terrestrial system. Simulation results show that the predistorter outperforms previous predistorters based on direct learning architectures in terms of convergence speed and linearization. A similar algorithm can be applied to estimate the Wiener PA model, which will achieve high model accuracy.

Subsystem model-based close-loop grey-box identification method for hydraulic stewart platform

In order to solve the problem of difficult modeling and identification caused by time-variable parameters,multiple inputs and outputs and unstable open loop,a subsystem model-based close-loop grey-box identification method was put forward when consider the main coupling effects of hydraulic Stewart platform.Firstly,the whole system is divided into three TITO(Two Input Two Output) subsystems according to the characteristics of the pseudo-mass matrix,hence transfer function matrix model of the subsystem can also be found.Secondly,since the Stewart platform is unstable,the close-loop transfer model of the subsystem is derived under the proportional controllers.The inverse M serial is adopted as the identification signal to get the experimental data.All parameters of the subsystem are determined in close-loop indirect identification by PEM(Prediction Error Method).Finally,a case study validates the correctness and effectiveness of the subsystem model-based close-loop grey-box identification method for hydraulic Stewart platform.

Design, Control and Analysis of Low Cost Archetype Dual Rotor Helicopter for Educational Institution

This paper presents the design and development of low cost archetype dual rotor helicopter (LCADRH) for academic research in an educational institution. The LCADRH is installed with optical pitch encoder and yaw encoder which measure elevation and side to side motion of helicopter. The objective of the project is to design and integrate the helicopter with data acquisition board and sensors to provide hardware features, software support capability for its rapid real time measurement and control. The low cost designed LCADRH facilitates the academic research for students in the institution and is able to provide hands on training to understand the concept of nonlinearity, system modelled and unmodelled dynamics and uncertainty, modelling, simulation and control by doing practical experiments. The mathematical model of the LCADRH is derived using grey box modelling method. The control of LCADRH is challenging due to its nonlinearity and effect of strong coupling between aerodynamic forces and torques generated by the both pitch and yaw actuators. In closed loop position control of LCADRH, pitch and yaw axis motion is regulated using linear quadratic controller (LQR). Encouraging results are obtained both in simulation and hardware.

Initial Error Growth and Predictability of Chaotic Low-dimensional Atmospheric Model

The growth of small errors in weather prediction is exponential on average. As an error becomes larger, its growth slows down and then stops with the magnitude of the error saturating at about the average distance between two states chosen randomly.This paper studies the error growth in a low-dimensional atmospheric model before, during and after the initial exponential divergence occurs. We test cubic, quartic and logarithmic hypotheses by ensemble prediction method. Furthermore, the quadratic hypothesis suggested by Lorenz in 1969 is compared with the ensemble prediction method. The study shows that a small error growth is best modeled by the quadratic hypothesis. After the error exceeds about a half of the average value of variables, logarithmic approximation becomes superior. It is also shown that the time length of the exponential growth in the model data is a function of the size of small initial error and the largest Lyapunov exponent. We conclude that the size of the error at the least upper bound(supremum) of time length is equal to 1 and it is invariant to these variables. Predictability, as a time interval, where the model error is growing, is for small initial error, the sum of the least upper bound of time interval of exponential growth and predictability for the size of initial error equal to 1.