Identification of linear multivariable systems from a single set of data by identification of observers with assigned real eigenvalues

A formulation is presented for identification of linear multivariable from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identifications, by extension of the repetition domain concept to include shifting time intervals. This method contrasts with existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate

On Markov parameters in system identification

A detailed discussion of Markov parameters in system identification is given. Different forms of input-output representation of linear discrete-time systems are reviewed and discussed. Interpretation of sampled response data as Markov parameters is presented. Relations between the state-space model and particular linear difference models via the Markov parameters are formulated. A generalization of Markov parameters to observer and Kalman filter Markov parameters for system identification is explained. These extended Markov parameters play an important role in providing not only a state-space realization, but also an observer/Kalman filter for the system of interest

Comparison of several system identification methods for flexible structures

In the last few years various methods of identifying structural dynamics models from modal testing data have appeared. A comparison is presented of four of these algorithms: the Eigensystem Realization Algorithm (ERA), the modified version ERA/DC where DC indicated that it makes use of data correlation, the Q-Markov Cover algorithm, and an algorithm due to Moonen, DeMoor, Vandenberghe, and Vandewalle. The comparison is made using a five mode computer module of the 20 meter Mini-Mast truss structure at NASA Langley Research Center, and various noise levels are superimposed to produced simulated data. The results show that for the example considered ERA/DC generally gives the best results; that ERA/DC is always at least as good as ERA which is shown to be a special case of ERA/DC; that Q-Markov requires the use of significantly more data than ERA/DC to produce comparable results; and that is some situations Q-Markov cannot produce comparable results

System identification from closed-loop data with known output feedback dynamics

This paper presents a procedure to identify the open loop systems when it is operating under closed loop conditions. First, closed loop excitation data are used to compute the system open loop and closed loop Markov parameters. The Markov parameters, which are the pulse response samples, are then used to compute a state space representation of the open loop system. Two closed loop configurations are considered in this paper. The closed loop system can have either a linear output feedback controller or a dynamic output feedback controller. Numerical examples are provided to illustrate the proposed closed loop identification method

Passive dynamic controllers for nonlinear mechanical systems

A methodology for model-independant controller design for controlling large angular motion of multi-body dynamic systems is outlined. The controlled system may consist of rigid and flexible components that undergo large rigid body motion and small elastic deformations. Control forces/torques are applied to drive the system and at the same time suppress the vibration due to flexibility of the components. The proposed controller consists of passive second-order systems which may be designed with little knowledge of the system parameter, even if the controlled system is nonlinear. Under rather general assumptions, the passive design assures that the closed loop system has guaranteed stability properties. Unlike positive real controller design, stabilization can be accomplished without direct velocity feedback. In addition, the second-order passive design allows dynamic feedback controllers with considerable freedom to tune for desired system response, and to avoid actuator saturation. After developing the basic mathematical formulation of the design methodology, simulation results are presented to illustrate the proposed approach to a flexible six-degree-of-freedom manipulator

Identification of linear systems by an asymptotically stable observer

A formulation is presented for the identification of a linear multivariable system from single or multiple sets of input-output data. The system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded eigenvalue assignment procedure. The prescribed eigenvalues for the observer may be real, complex, mixed real and complex, or zero. In this formulation, the Markov parameters of the observer are identified from input-output data. The Markov parameters of the actual system are then recovered from those of the observer and used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and extensive numerical examples using simulated noise-free data are presented to illustrate the proposed method

Trans Politics, Social Change, and Justice

On May 6 and 7, 2005, Trans Politics, Social Change, and Justice brought over four hundred trans people and allies into a single building. A feeling of electricity was everywhere. Not because this was the first trans conference — it was not. Nor was it the largest. What participants felt came from the fact that the real lives of trans people were being addressed by trans people. For a time, the ground had shifted; the complex webs of institutions and politics that surround the lives of people everywhere were being addressed primarily from the perspective of transgender peoples and their communities

Transgender Justice

Proceedings from CLAGS\u27s Trans Politics, Social Change and Justice conference will be available in January 2007 under the title Transgender Justice

LGBT Studies: Past, Presences and Futures

When I rolled out of bed at 4 am on April 20 to make the trip to New York for Futures of the Field: Building LGBT Studies into the 21st Century University, the idea of discussing institutionalization was less than appealing. In a time of staff cutbacks, increasing courseloads and notoriously poor job markets, going back to sleep seemed a much better idea

Do Dimensions of Ethnic Identity Mediate the Association Between Perceived Ethnic Group Discrimination and Depressive Symptoms?

Ethnic group discrimination represents a notable risk factor that may contribute to mental health problems among ethnic minority college students. However, cultural resources (e.g., ethnic identity) may promote psychological adjustment in the context of group-based discriminatory experiences. In the current study, we examined the associations between perceptions of ethnic group discrimination and depressive symptoms, and explored dimensions of ethnic identity (i.e., exploration, resolution, and affirmation) as mediators of this process among 2,315 ethnic minority college students (age 18 to 30 years; 37% Black, 63% Latino). Results indicated that perceived ethnic group discrimination was associated positively with depressive symptoms among students from both ethnic groups. The relationship between perceived ethnic group discrimination and depressive symptoms was mediated by ethnic identity affirmation for Latino students, but not for Black students. Ethnic identity resolution was negatively and indirectly associated with depressive symptoms through ethnic identity affirmation for both Black and Latino students. Implications for promoting ethnic minority college students’ mental health and directions for future research are discussed