State estimation in batch processes using a nonlinear observer

This paper deals with the problem of nonlinear states estimation in batch chemical processes. It presents a reduced-order nonlinear observer approach to perform the estimation. The proposed method allows adjustment of the speed of convergence towards zero of the estimation error. The stability properties of the model-based observer are analytically treated in order to show the conditions under which exponential convergence can be achieved. In addition, the performance of the proposed observer is evaluated on batch processes.     

A modified Newton's method for minimizing factorable functions

Many functions of several variables used in nonlinear programming are factorable, i.e., complicated compositions of transformed sums and products of functions of a single variable. The Hessian matrices of twice-differentiable factorable functions can easily be expressed as sums of outer products (dyads) of vectors. A modified Newton's method for minimizing unconstrained factorable functions which exploits this special form of the Hessian is developed. Computational experience with the method is presented.This material is based upon work supported by the National Science Foundation under Grant No. MCS-79-04106.The author would like to thank Professor G. P. McCormick, George Washington University, for several enlightening discussions on factorable programming and for his valuable comments which improved an earlier version of this paper.     

Spherically invariant processes: Their nonlinear structure,discrimination, and estimation

The structure of the nonlinear space of a spherically invariant process is studied and the problem of discriminating between two spherically invariant processes as well as the problem of nonlinear estimation for spherically invariant processes are solved.     

A new framework to relax composite functions in nonlinear programs

Mathematical Programming - In this paper, we devise new relaxations for composite functions, which improve the prevalent factorable relaxations, without introducing additional variables, by...     

Adjustable dimension descriptor observer based fault estimation for switched nonlinear systems with partially unknown nonlinear dynamics

This paper focuses on the fault estimation problem for switched systems with partially unknown nonlinear dynamics, actuator and sensor faults, simultaneously. The fault estimation observers are constructed, in which the observer dimension is not fixed and can be selected in a certain range. Both the disturbance decoupling and disturbance attenuation are considered, where the unknown nonlinear dynamics can be decoupled and the effect of modeling error and measurement disturbance is attenuated. Based on the average dwell time and the piecewise Lyapunov function, the observer parameter matrices can be calculated by solving LMIs and matrix equations. Finally, two examples are listed to verify the proposed fault estimation approach.     

Computability of global solutions to factorable nonconvex programs: Part I — Convex underestimating problems

For nonlinear programming problems which are factorable, a computable procedure for obtaining tight underestimating convex programs is presented. This is used to exclude from consideration regions where the global minimizer cannot exist.This work was supported by Contract AFORS-73-2504, U.S. Air Force, Office of Scientific Research.     

Optimal input system identification for nonlinear dynamic systems

The estimation accuracy for nonlinear dynamic system identification is known to be maximized by the use of optimal inputs. Few examples of the design of optimal inputs for nonlinear dynamic systems are given in the literature, however. The performance criterion is selected such that the sensitivity of the measured state variables to the unknown parameters is maximized. The application of Pontryagin's maximum principle yields a nonlinear two-point boundary-value problem. In this paper, the boundary-value problem for a simple nonlinear example is solved using two different methods, the method of quasilinearization and the Newton-Raphson method. The estimation accuracy is discussed in terms of the Cramer-Rao lower bound.     

Parameter estimation in nonlinear stochastic differential equations

We discuss the problem of parameter estimation in nonlinear stochastic differential equations (SDEs) based on sampled time series. A central message from the theory of integrating SDEs is that there exist in general two time scales, i.e. that of integrating these equations and that of sampling. We argue that therefore, maximum likelihood estimation is computationally extremely expensive. We discuss the relation between maximum likelihood and quasi maximum likelihood estimation. In a simulation study, we compare the quasi maximum likelihood method with an approach for parameter estimation in nonlinear SDEs that disregards the existence of the two time scales.     

State and parameter estimation for nonlinear systems

State and parameter estimators are obtained for systems described by nonlinear evolution equations. Linear infinite dimensional observability theory together with a variety of fixed point theorems can be employed to obtain a finite time observer. Moreover, a nonlinear asymptotic observer is produced using stability results. The problem of joint state and parameter estimation is converted to the state estimation case, via an augmented state, so that these observer results can be utilised. Examples and remarks on the generality of the results are given.     

Weighted linear associative memory approach to nonlinear parameter estimation

The method of linear associative memory (LAM), a notion from the field of artificial neural nets, has been applied recently in nonlinear parameter estimation. In the LAM method, a model response, nonlinear with respect to the parameters, is approximated linearly by a matrix, which maps inversely from a response vector to a parameter vector. This matrix is determined from a set of initial training parameter vectors and their response vectors, and can be update recursively and adaptively with a pair of newly generated parameter response vectors. The LAM advantage is that it can yield a good estimation of the true parameters from a given observed response, even if the initial training parameter vectors are far from the true values.In this paper, we present a weighted linear associative memory (WLAM) for nonlinear parameter estimation. WLAM improves LAM by taking into account an observed response vector oriented weighting. The basic idea is to weight each pair of parameter response vectors in the cost function such that, if a response vector is closer to the observed one, then this pair plays a more important role in the cost function. This weighting algorithm improves significantly the accuracy of parameter estimation as compared to a LAM without weighting. In addition, we are able to construct the associative memory matrix recursively, while taking the weighting procedure into account, and simultaneously update the ridge parameter of the cost function further improving the efficiency of the WLAM estimation. These features enable WLAM to be a powerful tool for nonlinear parameter simulation.This work was supported by National Science Foundation, Grants BCS-93-15886 and INT-94-17206. We thank Mr. L. Yobas for fruitful discussions.     

Parameter estimation in nonlinear age-dependent population dynamics

This paper studies a parameter estimation problem for the Gurtin-MacCamyequation, which is a nonlinear model of age-dependent populationdynamics. In estimating parameters such as the death rate andthe fertility which depend on the age and the total populationfrom a set of fully discrete observations of the population,we use a backward finite-difference scheme. The function-spaceparameter estimation convergence (FSPEC) of this scheme is provedand numerical simulations are performed.     

On nonlinear difference and differential equations

A generalization of the logarithmic norm to nonlinear operators, the Dahlquist constant is introduced as a useful tool for the estimation and analysis of error propagation in general nonlinear first-order ODE's. It is a counterpart to the Lipschitz constant which has similar applications to difference equations. While Lipschitz constants can also be used for ODE's, estimates based on the Dahlquist constant always give sharper results.The analogy between difference and differential equations is investigated, and some existence and uniqueness results for nonlinear (algebraic) equations are given. We finally apply the formalism to the implicit Euler method, deriving a rigorous global error bound for stiff nonlinear problems.Dedicated to my teacher and friend, Professor Germund Dahlquist, on the occasion of his 60th birthday.     

Sparse regression Chebyshev polynomial interval method for nonlinear dynamic systems under uncertainty

This paper proposes a new higher-efficiency interval method for the response bound estimation of nonlinear dynamic systems, whose uncertain parameters are bounded. This proposed method uses sparse regression and Chebyshev polynomials to help the interval analysis applied on the estimation. It is also a non-intrusive method which needs much fewer evaluations of original nonlinear dynamic systems than the other Chebyshev polynomials based interval methods. By using the proposed method, the response bound estimation of nonlinear dynamic systems can be performed more easily, even if the numerical simulation in nonlinear dynamic systems is costly or the number of uncertain parameters is higher than usual. In our approach, the sparse regression method “elastic net” is adopted to improve the sampling efficiency, but with sufficient accuracy. It alleviates the sample size required in coefficient calculation of the Chebyshev inclusion function in the sampling based methods. Moreover, some mature technologies are adopted to further reduce the sample size and to guarantee the accuracy of the estimation. So that the number of sampling, which solves the certain ordinary differential equations (ODEs), can be reduced significantly in the Chebyshev interval method. Three numerical examples are presented to illustrate the efficiency of proposed interval method. In particular, the last two examples are high dimension uncertain problems, which can further exhibit the ability to reduce the computational cost.     

Maximum-likelihood recursive nonlinear filtering

The basic model for the general nonlinear filtering problem consists of a nonlinear plant driven by noise followed by nonlinear observation with additive noise. The object is to estimate, at each instant, the current state of the plant, given thea priori information and the history of the observations up to the current time. The estimation procedure studied here is that of computing, at each instant, the most probable trajectory given the data at that time, and taking its final value. The purpose of the present paper is to clarify some earlier studies of this procedure.This research was partially supported by the National Science Foundation, Grant No. GK-806.     

Coefficient estimation in a first-order nonlinear hyperbolic Cauchy problem

This paper deals with the estimation and approximation of coefficient function in a first-order, nonlinear, hyperbolic Cauchy problem. The estimation is accomplished by minimizing a functional which measures the error between a finite set of given observations and the corresponding values of the solution generated by the coefficient function. A class of admissible coefficient functions is defined, and it is proved that minimizing coefficient function always exists within this class. We also develop an approximation by a sequence of solutions of associated finite-dimensional minimization problems.     

On the estimation of nonlinear time series models

In the present paper, a framework for parametric estimation in nonlinear time series is developed. Strong consistency and asymptotic normality of minimum Hellinger distance estimates for a determined class of nonlinear models are investigated. The main Interest for these estimates is motivated by their robustness under perturbations as it has been emphazized in Beran [2]. The first part of the paper is devoted to the study of some probabilistic properties which ensure the existence and the optimal properties of the estimates     

A partially linearized sigma point filter for latent state estimation in nonlinear time series models

A new technique for the latent state estimation of a wide class of nonlinear time series models is proposed. In particular, we develop a partially linearized sigma point filter in which random samples of possible state values are generated at the prediction step using an exact moment-matching algorithm and then a linear programming based procedure is used in the update step of the state estimation. The effectiveness of the new filtering procedure is assessed via a simulation example that deals with a highly nonlinear, multivariate time series representing an interest rate process.     

A nonlinear time series model and estimation of missing observations

This paper formulates a nonlinear time series model which encompasses several standard nonlinear models for time series as special cases. It also offers two methods for estimating missing observations, one using prediction and fixed point smoothing algorithms and the other using optimal estimating equation theory. Recursive estimation of missing observations in an autoregressive conditionally heteroscedastic (ARCH) model and the estimation of missing observations in a linear time series model are shown to be special cases. Construction of optimal estimates of missing observations using estimating equation theory is discussed and applied to some nonlinear models.Authors were supported in part by a grant from the Natural Sciences and Engineering Research Council of Canada.     

Noise can create periodic behavior and stabilize nonlinear diffusions

It is shown that unlike nondegenerate (linear) diffusion processes, nonlinear diffusion processes can have a periodic law. We provide an example of a nonlinear diffusion for which periodic behavior is even created by the noise, i.e. no periodicity occurs when the noise is turned off. In the second part of the paper we give an example of a one-dimensional nonlinear diffusion which can be stabilized by noise. Finally we show also that the N-dimensional (N ≥ 2) ‘linear’ diffusion approximations of that system are stabilized by noise.     

Interval length imbedding for nonlinear two-point boundary-value problems

In problems of optimal sequential estimation, in the study of fluid and electrolyte systems, in nonlinear mechanics, and throughout applied mathematics we are confronted with solving nonlinear two-point boundary-value problems. A new approach is provided which seems especially useful when solutions are desired for a variety of interval lengths.