Optimal Controller and Filter Realisations using Finite-precision, Floating- point Arithmetic.

The problem of reducing the fragility of digital controllers and filters implemented using finite-precision, floating-point arithmetic is considered. Floating-point arithmetic parameter uncertainty is multiplicative, unlike parameter uncertainty resulting from fixed-point arithmetic. Based on first- order eigenvalue sensitivity analysis, an upper bound on the eigenvalue perturbations is derived. Consequently, open-loop and closed-loop eigenvalue sensitivity measures are proposed. These measures are dependent upon the filter/ controller realization. Problems of obtaining the optimal realization with respect to both the open-loop and the closed-loop eigenvalue sensitivity measures are posed. The problem for the open-loop case is completely solved. Solutions for the closed-loop case are obtained using non-linear programming. The problems are illustrated with a numerical example

A Unifying Framework for Finite Wordlength Realizations.

A general framework for the analysis of the finite wordlength (FWL) effects of linear time-invariant digital filter implementations is proposed. By means of a special implicit system description, all realization forms can be described. An algebraic characterization of the equivalent classes is provided, which enables a search for realizations that minimize the FWL effects to be made. Two suitable FWL coefficient sensitivity measures are proposed for use within the framework, these being a transfer function sensitivity measure and a pole sensitivity measure. An illustrative example is presented

Optimal realizations of floating-point implemented digital controllers with finite word length considerations.

The closed-loop stability issue of finite word length (FWL) realizations is investigated for digital controllers implemented in floating-point arithmetic. Unlike the existing methods which only address the effect of the mantissa bits in floating-point implementation to the sensitivity of closed-loop stability, the sensitivity of closed-loop stability is analysed with respect to both the mantissa and exponent bits of floating-point implementation. A computationally tractable FWL closed-loop stability measure is then defined, and the method of computing the value of this measure is given. The optimal controller realization problem is posed as searching for a floating-point realization that maximizes the proposed FWL closed-loop stability measure, and a numerical optimization technique is adopted to solve for the resulting optimization problem. Simulation results show that the proposed design procedure yields computationally efficient controller realizations with enhanced FWL closed-loop stability performance

The Institution of Engineering and Technology

Abstract: In a debate paper, Keel and Bhattacharyya have suggested, by means of simple examples taken from the open literature, that optimal and robust controllers can be fragile in the sense that a minute perturbation in the controller parameters can make the closed-loop system unstable. However, is it true that the optimal and robust controllers presented by Keel and Bhattacharyya are actually fragile? It is demonstrated that the particular parametric stability margin used by Keel and Bhattacharyya can be very conservative and to overcome this problem, two non-conservative measures of controller fragility are proposed. In addition, it will be shown that the examples in Keel and Bhattacharyya's paper are very special and the resulting fragility cannot be linked to the H 1 optimisation but to non-appropriate H 1 optimisation criterions and to bad choice of weights. Introduction In . Different explanations for the fragility problem can be found in the literature. Mäkilä [4] examine Examples 3, 4 and 5 of [1] and present a procedure for assessing the fragility on the basis of the inherent robustness of the closed-loop system to perturbation in the physical parameters that make up implementation, using first-and second-order active RC filters in the implementation of continuous-time controllers and considering the effects of floating point erros in the implementation of digital controllers. More recently, Examples 1 and 2 of [1] have been revisited In spite of all the works listed in the previous paragraph, some questions still remain to be answered. Is it true that the optimal and robust controllers presented in [1] are actually so fragile? More importantly, is it true that the controllers obtained as solutions of the simple optimisation criteria presented in [1] are necessarily fragile? In this paper, these questions are answered and it is demonstrated that the particular stability margin used by Keel and Bhattacharyya can be very conservative and to overcome this problem, two non-conservative measures, based on necessary and sufficient conditions, are proposed here. In addition, it will be shown that the examples presented in [1] are very special and the resulting fragility cannot be associated with H 1 optimisation but to non-appropriate H 1 optimisation criterions and to bad choice of weights. This paper is organised as follows: in section 2, the relative parametric stability margin is reviewed, and an example that suggests the conservativeness of this measure is presented. In section 3, two nonconservative measures of controller fragility are proposed and a comparison between the relative parametric stability margin and the two nonconservative measures introduced in this paper is drawn. In section 4, the examples used in [1] to label H 1 controllers as fragile are re-examined. Finally, conclusions are drawn in section 5. 2 Relative parametric stability margin Definition Consider a closed-loop system with unit negative feedback, wher

H 2 And H ∞ Filtering Design Subject To Implementation Uncertainty

This paper presents new filtering design procedures for discrete-time linear systems. It provides a solution to the problem of linear filtering design, assuming that the filter is subject to parametric uncertainty. The problem is relevant, since the proposed filter design incorporates real world implementation constraints that are always present in practice. The transfer function and the state space realization of the filter are simultaneously computed. The design procedure can also handle plant parametric uncertainty. In this case, the plant parameters are assumed not to be exactly known but belonging to a given convex and closed polyhedron. Robust performance is measured by the H 2 and H ∞ norms of the transfer function from the noisy input to the filtering error. The results are based on the determination of an upper bound on the performance objectives. All optimization problems are linear with constraint sets given in the form of LMI (linear matrix inequalities). Global optimal solutions to these problems can be readily computed. Numerical examples illustrate the theory. © 2005 Society for Industrial and Applied Mathematics.442515530Gevers, M., Li, G., (1993) Parametrizations in Control, Estimation and Filtering Problems, , Springer-Verlag, LondonWilliamson, D., Finite wordlength design of digital Kalman filters for state estimation (1985) IEEE Trans. Automat. Control, 30, pp. 930-939Williamson, D., Kadiman, K., Optimal finite wordlength linear quadratic regulators (1989) IEEE Trans. Automat. Control, 34, pp. 1218-1228Liu, K., Skelton, R.E., Grigoriadis, K., Optimal controllers for finite wordlength implementation (1992) IEEE Trans. Automat. Control, 37, pp. 1294-1304Hwang, S.Y., Minimum uncorrelated unit noise in state-space digital filtering (1977) IEEE Trans. Acoustics Speech Signal Process, 25, pp. 273-281Amit, G., Shaked, U., Minimization of roundoff errors in digital realizations of Kalman filters (1989) IEEE Trans. Acoustics Speech Signal Process, 37, pp. 1980-1982De Oliveira, M.C., Skelton, R.E., Synthesis of controllers with finite precision considerations (2001) Digital Controller Implementation and Fragility: A Modern Perspective, pp. 229-251. , R. S. H. Istepanian and J. F. Whidborne eds., Springer-Verlag, New YorkKeel, L.H., Bhattacharyya, S.P., Robust, fragile or optimal (1997) IEEE Trans. Automat. Control, 42, pp. 1098-1105Keel, L.H., Bhattacharyya, S.P., Authors' reply to: "Comments on 'Robust, fragile or optimal' " by P. M. Mäkilä (1998) IEEE Trans. Automat. Control, 43, p. 1268Dorato, P., Non-fragile controller design: An overview (1998) Proceedings of the 1998 American Control Conference, 5, pp. 2829-2831. , Philadelphia, IEEE, Piscataway, NJFamularo, D., Dorato, P., Abdallah, C.T., Haddad, W.H., Jadbabaie, A., Robust non-fragile LQ controllers: The static state feedback case (2000) Internat. J. Control, 73, pp. 159-165Yang, G.H., Wang, J.L., Robust nonfragile Kalman filtering for uncertain linear systems with estimator gain uncertainty (2001) IEEE Trans. Automat. Control, 46, pp. 343-348Haddad, W.M., Corrado, J.R., Robust resilient dynamic controllers for systems with parametric uncertainty and controller gain variations (2000) Internat. J. Control, 73, pp. 1405-1423Keel, L.H., Bhattacharyya, S.P., Stability margins and digital implementation of controllers (1998) Proceedings of the 1998 American Control Conference, 5, pp. 2852-2856. , (Philadelphia), IEEE, Piscataway, NJGeromel, J.C., Optimal linear filtering under parameter uncertainty (1999) IEEE Trans. Signal Process, 47, pp. 168-175Nesterov, Y., Nemirovskii, A., (1994) Interior-Point Polynomial Algorithms in Convex Programming, , SIAM, PhiladelphiaGeromel, J.C., Bernussou, J., Garcia, G., De Oliveira, M.C., H 2 and H ∞ robust filtering for discrete-time linear systems (2000) SIAM J. Control Optim., 38, pp. 1353-1368Geromel, J.C., De Oliveira, M.C., Bernussou, J., Robust filtering of discrete-time linear systems with parameter dependent Lyapunov functions (2002) SIAM J. Control Optim., 41, pp. 700-711De Oliveira, M.C., Bernussou, J., Geromel, J.C., A new discrete-time robust stability condition (1999) Systems Control Lett., 37, pp. 261-265Sayed, A.H., A framework for state-space estimation with uncertain models (2001) IEEE Trans. Automat. Control, 46, pp. 998-1013Balakrishnan, V., Huang, Y., Packard, A., Doyle, J.C., Linear matrix inequalities in analysis with multipliers (1994) Proceedings of the 1994 American Control Conference, 2, pp. 1228-1232. , Baltimore, MD, IEEE, Piscataway, NJGeromel, J.C., Peres, P.L.D., Bernussou, J., On a convex parameter space method for linear control design of uncertain systems (1991) SIAM J. Control Optim., 29, pp. 381-40

Distributed averaging over communication networks:Fragility, robustness and opportunities

Distributed averaging, a canonical operation among many natural interconnected systems, has found applications in a tremendous variety of applied fields, including statistical physics, signal processing, systems and control, communication and social science. As information exchange is a central part of distributed averaging systems, it is of practical as well as theoretical importance to understand various properties/limitations of those systems in the presence of communication constraints and devise new algorithms to alleviate those limitations. We study the fragility of a popular distributed averaging algorithm when the information exchange among the nodes is limited by communication delays, fading connections and additive noise. We show that the otherwise well studied and benign multi-agent system can generate a collective global complex behavior. We characterize this behavior, common to many natural and human-made interconnected systems, as a collective hyper-jump diffusion process and as a L\\u27{e}vy flights process in a special case. We further describe the mechanism for its emergence and predict its occurrence, under standard assumptions, by checking the Mean Square instability of a certain part of the system. We show that the strong connectivity property of the network topology guarantees that the complex behavior is global and manifested by all the agents in the network, even though the source of uncertainty is localized. We provide novel computational analysis of the MS stability index under spatially invariant structures and gain certain qualitative as well as quantitative insights of the system. We then focus on design of agents\u27 dynamics to increase the robustness of distributed averaging system to topology variations. We provide a general structure of distributed averaging systems where individual agents are modeled by LTI systems. We show the problem of designing agents\u27 dynamics for distributed averaging is equivalent to an $\mathcal{H}_{\infty}$ minimization problem. In this way, we could use tools from robust control theory to build the distributed averaging system where the design is fully distributed and scalable with the size of the network. It is also shown that the agents could be used in different fixed networks and networks with speical time varying interconnections. We develop new iterative distributed averaging algorithms which allow agents to compute the average quantity in the presence of additive noise and random changing interconnections. The algorithm relaxes several previous restrictive assumptions on distributed averaging under uncertainties, such as diminishing step size rule, doubly stochastic weights, symmetric link switching styles, etc, and introduces novel mechanism of network feedback to mitigate effects of communication uncertainties on information aggregation. Based on the robust distributed averaging algorithm, we propose continuous as well as discrete time computation models to solve the distributed optimization problem where the objective function is formed by the summation of convex functions of the same variable. The algorithm shows faster convergence speed than existing ones and exhibits robustness to additive noise, which is the main source of limitation on algorithms based on convex mixing. It is shown that agents with simple dynamics and gradient sensing abilities could collectively solve complicated convex optimization problems. Finally, we generalize this algorithm to build a general framework forconstrained convex optimization problems. This framework is shown to be particularly effective to derive solutions for distributed decision making problems and lead to a systems perspective for convex optimization

Finite Wordlength Controller Realizations using the Specialized Implicit Form

Une forme d'état implicite spécialisée est présentée pour étudier les effets de l'implantation en précision finie des régulateurs. Cette forme permet une description macroscopique des algorithmes à implanter. Elle constitue un canevas unificateur permettant de décrire les différentes structures utilisées pour l'implantation, telles que les réalisations avec l'opérateur delta, la forme directe II en rho, la forme d'état-observateur et bien d'autres formes qui sont d'habitude traitées séparément dans la littérature. Différentes mesures quantifiant les effets de l'implantation sur le comportement en boucle fermée sont définis dans ce contexte. Elles concernent aussi bien la stabilité que la performance. L'écart entre la réalisation à précision infinie et la réalisation à précision finie est évaluée selon la mesure de sensibilité des coefficients et la mesure du bruit de quantification. Le problème consistant à trouver une réalisation dont l'implantation amène un minimum de dégradation peut alors est résolut numériquement. Cette approche est illustrée avec deux exemples

Stochastic System Design and Applications to Stochastically Robust Structural Control

The knowledge about a planned system in engineering design applications is never complete. Often, a probabilistic quantification of the uncertainty arising from this missing information is warranted in order to efficiently incorporate our partial knowledge about the system and its environment into their respective models. In this framework, the design objective is typically related to the expected value of a system performance measure, such as reliability or expected life-cycle cost. This system design process is called stochastic system design and the associated design optimization problem stochastic optimization. In this thesis general stochastic system design problems are discussed. Application of this design approach to the specific field of structural control is considered for developing a robust-to-uncertainties nonlinear controller synthesis methodology. Initially problems that involve relatively simple models are discussed. Analytical approximations, motivated by the simplicity of the models adopted, are discussed for evaluating the system performance and efficiently performing the stochastic optimization. Special focus is given in this setting on the design of control laws for linear structural systems with probabilistic model uncertainty, under stationary stochastic excitation. The analysis then shifts to complex systems, involving nonlinear models with high-dimensional uncertainties. To address this complexity in the model description stochastic simulation is suggested for evaluating the performance objectives. This simulation-based approach addresses adequately all important characteristics of the system but makes the associated design optimization challenging. A novel algorithm, called Stochastic Subset Optimization (SSO), is developed for efficiently exploring the sensitivity of the objective function to the design variables and iteratively identifying a subset of the original design space that has v i high plausibility of containing the optimal design variables. An efficient two-stage framework for the stochastic optimization is then discussed combining SSO with some other stochastic search algorithm. Topics related to the combination of the two different stages for overall enhanced efficiency of the optimization process are discussed. Applications to general structural design problems as well as structural control problems are finally considered. The design objectives in these problems are the reliability of the system and the life-cycle cost. For the latter case, instead of approximating the damages from future earthquakes in terms of the reliability of the structure, as typically performed in past research efforts, an accurate methodology is presented for estimating this cost; this methodology uses the nonlinear response of the structure under a given excitation to estimate the damages in a detailed, component level