Imad M. Jaimoukha

Mixed-sensitivity approach to H/sub /spl infin// control of power system oscillations employing multiple FACTS devices

This paper demonstrates the enhancement of inter-area mode damping by multiple flexible AC transmission systems (FACTS) devices. Power system damping control design is formulated as an output disturbance rejection problem. A decentralized H/sub /spl infin// damping control design based on the mixed-sensitivity formulation in the linear matrix inequality (LMI) framework is carried out. A systematic procedure for selecting the weights for shaping the open loop plant for control design is suggested. A 16-machine, five-area study system reinforced with a controllable series capacitor (CSC), a static VAr compensator (SVC), and a controllable phase shifter (CPS) at different locations is considered. The controllers designed for these devices are found to effectively damp out inter-area oscillations. The damping performance of the controllers is examined in the frequency and time domains for various operating scenarios. The controllers are found to be robust in the face of varying power-flow patterns, nature of loads, tie-line strengths, and system nonlinearities, including device saturations.

A linear matrix inequality approach to robust damping control design in power systems with superconducting magnetic energy storage device

This paper presents a systematic design procedure for a robust damping controller based on the linear matrix inequality (LMI) approach employing a superconducting magnetic energy storage (SMES) device. The design procedure takes advantage of the multi-objective features of LMI based design techniques. The procedure is applied to enhance the damping of inter-area oscillations in a two-area four-machine test system.

Computation of a reference model for robust fault detection and isolation residual generation

This paper considers matrix inequality procedures to address the robust fault detection and isolation (FDI) problem for linear time-invariant systems subject to disturbances, faults, and polytopic or norm-bounded uncertainties.We propose a design procedure for an FDI filter that aims to minimize a weighted combination of the sensitivity of the residual signal to disturbances and modeling errors, and the deviation of the faults to residual dynamics from a fault to residual reference model, using the H∞-norm as a measure. A key step in our procedure is the design of an optimal fault reference model. We show that the optimal design requires the solution of a quadratic matrix inequality (QMI) optimization problem. Since the solution of the optimal problem is intractable, we propose a linearization technique to derive a numerically tractable suboptimal design procedure that requires the solution of a linear matrix inequality (LMI) optimization. A jet engine example is employed to demonstrate the effectiveness of the proposed approach.

Observer-based fault detection and isolation filter design for linear time-invariant systems

In this paper we consider a model-based fault detection and isolation problem for linear time-invariant dynamic systems subject to faults and disturbances. We use a state observer scheme that cancels the system dynamics and defines a residual vector signal that is sensitive only to faults and disturbances. We then design a stable fault detection and isolation filter such that the ℋ∞-norm of the transfer matrix function from disturbances to the residual is minimised (for fault detection) subject to the constraint that the transfer matrix function from faults to residual is equal to a pre-assigned diagonal transfer matrix (for isolation of possibly simultaneous occurring faults). Our solution is given in the form of linear matrix inequalities using state-space techniques, as well as a model matching problem using matrix factorisation techniques. A numerical example is given to illustrate the efficiency of the fault detection and isolation filter.

State Space Solution to the H - /H ∞ Fault Detection Problem

In this paper we give a state space solution to the H_-/H_∞ fault detection (FD) problem for linear time invariant dynamic systems. An H_-/H_∞ FD filter minimizes the sensitivity of the residual signal to disturbances while maintaining a minimum level of sensitivity to faults. We provide a state space realization of the optimal filter in an observer form via the solution of linear matrix inequalities (LMIs). A numerical example is given to illustrate the algorithm

All solutions to the four block general distance problem

The authors characterize all solutions to the four-block general distance problem which arises in H/sup infinity /-optimal control. The procedure is to embed the original problem in an all-pass matrix constructed by the authors. It is then demonstrated that part of this all-pass matrix acts as a generator of all solutions. As an application, the authors find a representation formula for all solutions to H/sup infinity /-optimal control problems of the third kind.<<ETX>>

Model Predictive Control based on Mixed H2/H Control Approach

A novel approach to the design of model predictive control is proposed, using mixed H 2/H infin design method for time invariant discrete-time linear systems. The controller has the form of state feedback, satisfies quadratic input and state constraints and is constructed from the solution of a set of feasibility linear matrix inequalities. The control law takes account of disturbances naturally. A numerical example demonstrates the applicability of the algorithm.

Model predictive control based on mixed ℋ2/ℋ∞control approach for active vibration control of railway vehicles

This paper investigates the application of model predictive control technology based on mixed ℋ2/ℋ∞ control approach for active suspension control of a railway vehicle, the aim being to improve the ride quality of the railway vehicle. Comparisons are made with more conventional control approaches, and the applicability of the linear matrix inequality approach is illustrated via the railway vehicle example.

Fault isolation filter with linear matrix inequality solution to optimal decoupling

In this paper we consider a model-based fault detection and isolation problem for linear time-invariant dynamic systems subject to faults and disturbances. We use an observer scheme that cancels the system dynamics and defines a residual vector signal that is sensitive only to faults and disturbances. We then design a stable fault isolation filter such that the Hinfin-norm of the transfer matrix function from disturbances to the residual is minimized (for fault detection) subject to the constraint that the transfer matrix function from faults to residual is equal to a pre-assigned diagonal transfer matrix (for fault isolation). The optimization of disturbance decoupling is accomplished via the help of linear matrix inequalities. A numerical example is also presented to illustrate the algorithm

Modeling and control of TCV

A new approach to the modeling and control of tokamak fusion reactors is presented. A nonlinear model is derived using the classical arguments of Hamiltonian mechanics and a low-order linear model is derived from it. The modeling process used here addresses flux and energy conservation issues explicitly and self-consistently. The model is of particular value, because it shows the relationship between the initial modeling assumptions and the resulting predictions. The mechanisms behind the creation of uncontrollable modes in tokamak models are discussed. A normalized coprime factorization H/sub /spl infin// controller is developed for the Tokamak a/spl grave/ Configuration Variable (TCV), CRPP-EPFL, Lausanne, Switzerland, tokamak using the linearized model, which has been extensively verified on the TCV and JT-60U, JAERI, Naka, Japan, tokamaks. Recent theory is applied to reduce the controller order significantly whilst guaranteeing a priori bounds on the robust stability and performance. The controller is shown to track successfully reference signals that dictate the plasmas shape, position and current. The tests used to verify this were carried out on linear and nonlinear models.