Higher Order Reduction of Uncertain Systems: Affine Arithmeti

In this paper the Modified Routh Approximation (MRA) and Affine Arithmetic (AA) methods are investigates for obtaining the reduced order model (ROM) of SISO, discrete & MIMO uncertain systems into lower order system. Rigorous study and analysis of physical system direct to the outcome of systems with uncertainty instead of certain coefficients. Thus, systems having uncertain but bounded parameters known as uncertain systems are under consideration in this paper. An effective algorithm to determine the reduced order model is proposed here. This proposed methodology is verified using numerical examples available from the literature.

Model reduction of unstable systems based on balanced truncation algorithm

Model reduction of a system is an approximation of a higher-order system to a lower-order system while the dynamic behavior of the system is almost unchanged. In this paper, we will discuss model order reduction (MOR) strategies for unstable systems, in which the method based on the balanced truncation algorithm will be focused on. Since each MOR algorithm has its strengths and weakness, practical applications should be suitable for each specific requirement. Simulation results will demonstrate the correctness of the algorithms.

A new model order reduction method for the design of compensator by using moment matching algorithm

The aim of this paper is the construction of a new model reduction technique for large scale stable linear dynamic systems. It is principally focused on the dominant modes and time moments retention. This reduction implicates the translation of the overall important features confined in the large scale complete order model into the lower order system, allowing the computation of approximant denominator by using generalized pole clustering method. The approximant numerator is obtained by means of the factor division algorithm. As a result, a lower order system is obtained. To demonstrate its effectiveness, to highlight some fundamental of its features, and to accomplish its accuracy, a comparative study is done. Two standard numerical examples are taken, where approximant model computed by the proposed method is compared with the reduced order models computed from the recently proposed methods as well as well-known model reduction schemes. The paper is also emphasized on the design of compensator by using moment matching algorithm with the help of the reduced model. The design of compensator is validated and illustrated with the help of a standard numerical example taken from the literature.

Inter-Area Oscillation Damping in Large-Scale Power Systems Using Decentralized Control

Inter-area oscillation is one of the main concerns in power system small signal stability. It involves wide area in power system, therefore identifying the causes and damping these oscillations are challenging. Undamped inter-area oscillations may cause severe problems in power systems including large-scale blackouts. Designing a proper controller for power systems also is a challenging problem due to the complexity of the system. Moreover, for a large-scale system it is impractical to collect all system information in one location to design a centralized controller. Decentralized controller will be more desirable for large scale systems to minimize the inter area oscillations by using local information. In this paper, we consider a large-scale power system consisting of three areas. After decomposing the system into three subsystems, each subsystem is modeled with a lower order system. Finally, a decentralized controller is designed for each subsystem to maintain the large-scale system frequency at the desired level even in the presence of disturbances.

A NOVEL MIXED METHOD FOR THE MODEL ORDER REDUCTION OF LINEAR SYSTEMS USING PARTICLE SWARM OPTIMIZATION AND POLYNOMIAL METHOD

In this paper, a novel mixed method is used for reducing the higher order system to lower order system. The denominator polynomials are obtained by the PSO Algorithm and the numerator coefficients are derived by the polynomial method. This method is simple and computer oriented. If the original system is stable then reduced order system is also stable. The proposed method is illustrated with the help of typical numerical examples considered from the literature.

Two-Mode Models of Self-Regulation as a Tool for Conceptualizing Effects of the Serotonin System in Normal Behavior and Diverse Disorders

The serotonin system is a collection of neural pathways whose overall level of functioning (from low to high) relates to diverse kinds of psychological and behavioral variability. Individual differences in serotonergic function are important both in personality and in vulnerability to psychological disorders. These disorders range widely—from impulsive aggression to depression. One way to understand such diverse reflections of differences in serotonergic function is by viewing serotonergic function through the lens of two-mode (or dual-process) models of self-regulation. Such theories posit a lower-order system that responds quickly to associative cues of the moment and a higher-order system that responds reflectively and planfully. Low serotonergic function appears to enhance influence of the lower-order system. This often yields impulsive reactivity. Why, then, does low serotonergic function also relate to depression, which is characterized by lethargy and unresponsiveness? The answer must be that ascendance of the lower system interacts with other factors. One hypothesis is that low serotonergic function plus high sensitivity to incentives yields vulnerability to impulsive approach, whereas low serotonergic function plus low incentive sensitivity yields vulnerability to depression. Conceptualizing serotonergic function this way helps integrate information pertaining to very different disorders into a coherent picture.