Design of noninteracting flight control systems in the presence of large parameter variations

The perfect noninteraction for a discrete parameter set is examined along with the sensitivity of a decoupled flight control system with respect to system parameter variations. A set of computer programs developed for this project are described

Minimum sensitivity design of attitude control systems Final report

Minimum sensitivity design of attitude control systems for spacecraf

Stability and finite-time stability analysis of discrete-time nonlinear networked control systems

In this paper we present an approach to model networked control systems with a discrete-time nonlinear plant, operating in the presence of arbitrary but finite data dropout of state observations. Sufficient conditions for stability of the global system and finite-time stability over transmission intervals are provided

Finite-time stability of discrete-time nonlinear systems: analysis and design

Finite-time stability of nonlinear discrete-time systems is studied. Some new analysis results are developed and applied to controller design

Model-based networked control for finite-time stability of nonlinear systems: the stochastic case

In this paper we analyze model-based networked control systems for a discrete-time nonlinear plant model, operating in the presence of stochastic dropout of state observations. The dropout is modeled as a Markov chain, and sufficient conditions for finite-time stochastic stability are provided using the stochastic version of Lyapunov second method. In a companion paper we model the dropout as a deterministic sequence

Assessment of the resistance model uncertainties in plane stress NLFEA of cyclically loaded reinforced concrete systems

The present work is devoted to estimate the resistance model uncertainty within plane stress non-linear finite element analyses (NLFEAs) of reinforced concrete structures subjected to cyclic loads. Specifically, various shear walls experimentally tested are considered for the investigation. The comparison between the plane stress NLFE structural model results and the experimental outcomes is carried out considering the possible modelling hypotheses available to describe the mechanical behaviour of reinforced concrete members subjected to cyclic loads. Several NLFE structural models are defined for each experimental test in order to investigate the resistance model uncertainty

Output Stabilizability

In this report, we provide algebraic tests to determine whether a linear Single-Input-Single-Output (SISO) system, is stabilizable with a constant output feedback

Finite time stability design via feedback linearization

A new nonlinear design technique for Finite-Time Stability for a class of nonlinear systems is developed using feedback linearization. Moreover, a new concept, namely the Finite-Time Contractive Stability with fixed settling time is introduced, giving sufficient conditions for analysis and design. An example illustrates the theoretical results

An Overview of Quasi-Monte Carlo Methods in Control Systems

Many control problems are so complex that analytic techniques fail to solve them [2]. Furthermore, even when analytic solutions are available, they may be computationally costly [2] and generally result in very high-order compensators [3]. Due to these reasons, we tend to accept approximate answers which provide us with certain performance guarantees for such problems. Sampling methods thus come into the picture to try and remedy the “cost of solution” problem by drawing samples from an appropriate space, and providing an approximate answer. For many years, random sampling has dominated the afore mentioned arena [8, 11, 4]. Random sample generation, with a uniform underlying distribution, however tends to cluster the samples on the boundary of the sample space in higher dimensions. It is for this reason that we are interested in presenting a method that distributes the points regularly in the sample space while providing deterministic guarantees on the error involved. Recently, deterministic or quasi-Monte Carlo (QMC) methods have proven superior to random methods in several applications such as the calculation of certain integrals [6], financial derivatives [7] and motion planning in robotics [10]. They have also been used for stability analysis of high speed networks [9]. In this work, we provide an overview of such deterministic quasi-Monte Carlo method of sampling, and their applications to control systems analysis and design. We present the basic concepts pertaining to quasi-Monte Carlo deterministic sampling. Such concepts include the following: Indicator functions, performance objective, generation of point sets, total variation, and error bounds

Analytic Metaphysics versus Naturalized Metaphysics: The Relevance of Applied Ontology

The relevance of analytic metaphysics has come under criticism: Ladyman & Ross, for instance, have suggested do discontinue the field. French & McKenzie have argued in defense of analytic metaphysics that it develops tools that could turn out to be useful for philosophy of physics. In this article, we show first that this heuristic defense of metaphysics can be extended to the scientific field of applied ontology, which uses constructs from analytic metaphysics. Second, we elaborate on a parallel by French & McKenzie between mathematics and metaphysics to show that the whole field of analytic metaphysics, being useful not only for philosophy but also for science, should continue to exist as a largely autonomous field