is zero during this interval. Clearly in most applications, this is not the case. Hence, a class of model In this paper we consider the problem of model rereduction techniques has emerged based on viewing duction for a nonlinear plant under the in°uence of a the plant as part of a larger closed-loop system. The control system. We relate this problem to the notion usual assumption is that the sy...

Balanced truncation (BT) is a well established model reduction technique for linear ordinary differential equations. If the original dynamics are described by an instationary PDE, then BT is usually applied to the large-scale linear system resulting from a spatial semidiscretization using finite elements/volumes/differences. We will discuss this approach as well as a variant of BT based on bala...

Linear Quadratic Gaussian (LQG) control has a known analytical solution [1] but non-linear problems do not [2]. The state of the art method used to find approximate solutions to non-linear control problems (iterative LQG) [3] carries a large computational cost associated with iterative calculations [4]. We propose a novel approach for solving nonlinear Optimal Control (OC) problems which combin...

Abstract. We consider a linear-quadratic-Gaussian (LQG) optimal control problem where the generalized state space is the product of an Euclidian space and an infinite dimensional function space. This model originates from a mean field LQG game with a major player and a large number of minor players, and has importance in designing decentralized strategies in the game. We show that the underlyin...

When only input/output data of a system are available the classical way to design a linear quadratic Gaussian controller consists of mainly three separate parts. First a system identi cation step is performed to nd the system parameters. With these parameters a Kalman lter is designed to nd an estimate of the state of the system. Finally, this state is then used in an LQ-controller. In literatu...

Within the framework of stochastic two-person nonzero-sum games, we deal with two commonly used models in engineering and economics-namely, the LQG (Linear-Quadratic-Gaussian) and the duopoly problems. We investigate how variations in information available to either player affect the equilibrium Nash strategies for these two models, whose existence and uniqueness have been proven in the paper. ...

This paper is concerned with the development of a general theory for the class of multistage linear-quadratic-Gaussian (LQG) decision problems characterized by (i) two decision makers (DM) each with a different objective functional to optimize, (ii) one-step delay observation sharing information pattern which provides each DM with the observation (but not the action) of the other DM with a one-...

This paper presents LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning that takes into account the sensors and the controller that will be used during execution of the robot’s path. LQGMP is based on the linear-quadratic controller with Gaussian models of uncertainty, and explicitly characterizes in advance (i.e., before execution) the a-priori probabili...

The presence of fast and slow modes in vehicle suspension systems, based on a half car model, is utilized in the design of active suspension control using singular perturbation theory. This strategy is based on the slow-fast control design. The suspension system performance is optimised with respect to ride comfort, road holding and suspension rattle space as expressed by the mean-square-values...

In this paper we consider the synthesis of optimal feedback controllers for a stochastically-excited passive electromechanical network, subject to the constraint that in stationarity, the feedback law must be realizable with a regenerative actuation system. Regenerative systems are similar to passive systems but their dynamic constraints are more relaxed, in the sense that they only need to con...