Guaranteed performance control is considered in this paper for discretetime LQG problems by minimizing the probability that the performance index is over a preselected threshold. It is proven that this guaranteed performance control problem can be converted into a mean-variance control problem which can be solved by using an embedding scheme. An optimal open-loop feedback control law is derived...

Linear quadratic gaussian (LQG) optimal control techniques can control linear multivariable systems that are precisely known [1,2]. Within the framework of the design, they achieve a best possible performance. However, if the plant is imprecisely known, actual performance can be intolerably poor. The guaranteed gain and phase margins for the case of the nominal plant may evaporate in the presen...

The discrete logarithm problem in Jacobians of curves of high genus g over finite fields Fq is known to be computable with subexponential complexity Lqg (1/2, O(1)). We present an algorithm for a family of plane curves whose degrees in X and Y are low with respect to the curve genus, and suitably unbalanced. The finite base fields are arbitrary, but their sizes should not grow too fast compared...

1 Grad. stud., Univ. of Michigan (UM) & intern, United Tech. Res. Center (UTRC). 2 Research eng., UTRC. 3 Prof., UM. ABSTRACT This paper studies the combined optimization of an elevator’s design (plant) and LQG controller for ride comfort. Elevator dynamics and primary vibration sources (drive motor torque ripple and guide rail irregularity) are modeled using an object-oriented language. The re...

The Linear-Quadratic Gaussian (LQG) design is the most efficient and widely used design approach in the field of linear stochastic control systems. From theoretical point of view this approach is reduced to the synthesis of a LQ state regulator and of a Kalman filter for the controlled system. From computational point of view the LQG design consists of solving a pair of matrix Riccati equations...

 The paper presents a hybrid system controller, incorporating a neural and an LQG controller. The neural controller has been optimized by genetic algorithms directly on the inverted pendulum system. The failure-free optimization process stipulated a relatively small region of the asymptotic stability of the neural controller, which is concentrated around the regulation point. The presented hyb...

This paper presented both the linear quadratic Gaussian technique (LQG) and the coefficient diagram method (CDM) as load frequency controllers in a multi-area power system to deal with the problem of variations in system parameters and load demand change. The full states of the system including the area frequency deviation have been estimated using the Kalman filter technique. The efficiency of...

Recently the Master Constraint Programme (MCP) for Loop Quantum Gravity (LQG) was launched which replaces the infinite number of Hamiltonian constraints by a single Master constraint. The MCP is designed to overcome the complications associated with the non – Lie – algebra structure of the Dirac algebra of Hamiltonian constraints and was successfully tested in various field theory models. For t...

In the previous papers (Pupeikis, 2000; Genov et al., 2006), a direct approach for estimating the parameters of a discrete-time linear time-invariant (LTI) dynamic system, acting in a closed-loop in the case of additive correlated noise with contaminating outliers uniformly spread in it, is presented. It is assumed here that the parameters of the LQG (Linear Quadratic Gaussian Control) controll...

The problem of background independent quantum gravity is the problem of defining a quantum field theory of matter and gravity in the absence of an underlying background geometry. Loop quantum gravity (LQG) is a promising proposal for addressing this difficult task. Despite the steady progress of the field, dynamics remains to a large extend an open issue in LQG. Here we present the main ideas b...