A Composite State Convergence Scheme for Bilateral Teleoperation Systems

State convergence is a novel control algorithm for bilateral teleoperation of robotic systems. First, it models the teleoperation system on state space and considers all the possible interactions between the master and slave systems. Second, it presents an elegant design procedure which requires a set of equations to be solved in order to compute the control gains of the bilateral loop. These design conditions are obtained by turning the master-slave error into an autonomous system and imposing the desired dynamic behavior of the teleoperation system. Resultantly, the convergence of master and slave states is achieved in a well-defined manner. The present study aims at achieving a similar convergence behavior offered by state convergence controller while reducing the number of variables sent across the communication channel. The proposal suggests transmitting composite master and slave variables instead of full master and slave states while keeping the operator's force channel intact. We show that,with these composite and force variables;it is indeed possible to achieve the convergence of states in a desired way by strictly following the method of state convergence. The proposal leads to a reduced complexity state convergence algorithm which is termed as composite state convergence controller. In order to validate the proposed scheme in the absence and presence of communication time delays, MATLAB simulations and semi-real time experiments are performed on a single degree-of-freedom teleoperation system.

MODELING AND FORECASTING OF STOCK MARKETS UNDER A SYSTEM ADAPTATION FRAMEWORK

This paper adopts the concept of dynamic feedback systems to model the behavior of financial markets, or more specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, the authors model the movement of a stock market index within a framework that is composed of an internal dynamic model and an adaptive filter. The output-error model is adopted as the internal model whereas the adaptive filter is a time-varying state space model with instrumental variables. Its input-output behavior, and internal as well as external forces are then identified. Special attention has also been paid to the recent financial crisis by examining the movement of Dow Jones Industrial Average （DJIA） as an example to illustrate the advantage of the proposed framework. Supported by time-varying causality tests, five influential factors from economic and sentiment aspects are introduced as the input of this framework. Testing results show that the proposed framework has a much better prediction performance than the existing methods, especially in complicated economic situations. An application of this framework is also presented with focuses on forecasting the turning periods of the market trend. Realizing that a market trend is about to change when the external force begins to exhibit clear patterns in its frequency responses, the authors develop a set of rules to recognize this kind of clear patterns. These rules work well for stock indexes from US, China and Singapore.