New design of linear discrete-time predictor using covariance information

A discrete one-stage predictor algorithm using covariance information in linear systems is derived. The algorithm is obtained for white Gaussian observation noise. The signal is a nonstationary or stationary stochastic process. The auto-covariance function of the signal is expressed using a semi-degenerate kernel of discrete-time systems. The semi-degenerate kernel can represent general covariance functions of random processes by a finite sum of nonrandom functions.     

Near optimal smoothing for singularly perturbed linear systems

A two time-scale lower order design is proposed as a near optimal solution to the fixed-interval smoothing problem for systems with slow and fast modes. The slow mode smoothing solution, in the limit as the perturbation parameter μ → 0, tends to that of the reduced-order problem; and the near optimal fast mode smoother is simply a weighted sum of lower order two time-scale filters. While the near optimal solution affords a significant reduction in computational complexity, the performance degradation, as illustrated in an example, is typically negligible.     

Identification of stochastic linear systems in presence of input noise

Most identification methods rely on the assumption that the input is known exactly. However, when collecting data under an identification experiment it may not be possible to avoid noise when measuring the input signal. In the paper some different ways to identify systems from noisy data are discussed. Sufficient conditions for identifiability are given. Also accuracy properties and the computational requirements are discussed. A promising approach is to treat the measured input and output signals as outputs of a multivariable stochastic system. If a prediction error method is applied using this approach the system will be identifiable under mild conditions.     

Numerical comparison of kalman filter algorithms: Orbit determination case study

Numerical characteristics of various Kalman filter algorithms are illustrated with a realistic orbit determination study. The case study of this paper highlights the numerical deficiencies of the conventional and stabilized Kalman algorithms. Computational errors associated with these algorithms are found to be so large as to obscure important mismodeling effects and thus cause misleading estimates of filter accuracy. The positive result of this study is that the U-D covariance factorization algorithm has excellent numerical properties and is computationally efficient, having CPU costs that differ negligibly from the conventional Kalman costs. Accuracies of the U-D filter using single precision arithmetic consistently match the double precision reference results. Numerical stability of the U-D filter is further demonstrated by its insensitivity to variations in the a priori statistics.     

Square-root algorithms for parallel processing in optimal estimation

We give explicit algorithms in square-root form that allow measurements for the standard state estimation problem to be processed in a highly parallel fashion with little communication between processors. After this preliminary processing, blocks of measurements may be incorporated into state estimates with essentially the same computation as usually accompanies the incorporation of a single measurement. This formulation also leads to square-root doubling formulae for calculating the steady-state error-covariance matrix of constant models, and an extension of the class of problems for which Chandrasekhar-type algorithms offer computational reductions to include piecewise constant systems with arbitrary initial conditions.     

Identification by a combined smoothing nonlinear programming algorithm

An offline algorithm is developed for identification of parameters of linear, stationary, discrete, dynamic systems with known control inputs and subjected to process and measurement noise with known statistics. Results of the algorithm include estimates of the parameters and smoothed estimates of the state and process noise sequences. The problem is stated as the minimization of a quadratic performance index. This minimization problem is then converted to a nonlinear programming problem for determining the optimum parameter estimates. The new algorithm is shown to be cost competitive with the currently popular filtering-sensitivity function method. A third order example with simulated data is presented for comparison.     

Suboptimal control of linear stochastic multivariable systems with unknown parameters

An algorithm is proposed for suboptimal control of linear multivariable systems with unknown parameters and output noise covariances. This algorithm is based on the idea of explicitly separating the functions of identification, estimation and control. The parameters and states of the system are estimated in a bootstrap manner by the stochastic approximation method. A suboptimal controller is then obtained which utilizes a deterministic control gain derived using the estimates obtained from the parameter and state estimators. This suboptimal controller will approach the optimal strategy when the estimated system parameters approach their true values.     

A separation theorem for stochastic control problems with point-process observations

The exact solution is derived for a stochastic optimal control problem involving a linear stochastic plant, quadratic costs, and nonlinear, nongaussian observations. The observations are in the form of a point process in which each point has both a temporal and a spatial coordinate. The state of the stochastic plant influences the intensity of the observed time-space point process. The solution to this dual control problem can be realized with a separated estimator-controller in which the estimator is nonlinear, mean-square optimal, and finite dimensional, and the controller is the certainty equivalent linear controller. Motivation for the stochastic optimal control problem studied here is given in terms of position sensing and tracking for quantum-limited optical communication problems.     

Relation between filter using covariance information and Kalman filter

This paper presents sequential algorithms for the optimal impulse function, Kalman gain and the error variance in linear least squares filtering problems, when the autocovariance function of the signal is given in the form of a semi-degenerate kernel, and the additive observation noise in white Gaussian. A digital simulation result indicates that the algorithms presented in this paper are feasible, and that the values of Kalman gain and the error variance calculated by these algorithms approach to those obtained by the Kalman filter theory, for time sufficiently large.     

A dynamic state estimator for power system dynamic security assessment

A global modularized dynamic state estimator is formulated to provide the data which will be required for future dynamic security assessment and dynamic security enhancement applications. The dynamic state estimator is global because it is capable of estimating small and large dynamic fluctuations in voltage angle and frequency for an entire area. The dynamic state estimator is composed of the sum of the static state estimate, obtained by using present hardware and algorithms and a modularized dynamic state estimate based on a linearized classical transient stability model with a stochastic load model. This dynamic state estimate component is modularized to (1) eliminate the need to measure or model external system generation and (2) to permit a reduction in computation requirements for (a) updating the linearized power system dynamic model and (b) for computing the state estimate. The modularization, which is accomplished by decoupling the linearized dynamic model for each subregion by measuring the power injections on lines connecting the subregion to the rest of the power system, causes the dynamic state estimate to be locally referenced. A global referencing procedure is proposed and discussed. A linearized stochastic model for the Michigan Electric Coordinated System is developed to illustrate the procedures proposed for developing the stochastic load model and determining the constant gain approximation for the governor turbine energy system dynamics. A summary of results on the performance of the Kalman state estimator is presented.     

Adaptive,high precision,satellite attitude control for microprocessor implementation

A control system is presented for three-axis, gas jet, satellite attitude control having application to any spacecraft where precise pointing is required within stringent mass and power limitations. Several novel features are incorporated as follows: parabolic switching boundaries are employed with parameters which adapt to a disturbing acceleration estimate in order to achieve a zero offset steady-state limit cycle of preset amplitude in the arcsecond region which minimizes both fuel consumption and thruster operation frequency. The disturbing acceleration estimate is obtained from a third-order state estimator, together with angle error and rate estimates using an angle error measurement from a rate integrating gyro and a jet drive input. Time optimal recovery from large initial angle errors and rapid response to step changes in disturbing acceleration are achieved. In addition, stable control is obtained with disturbing acceleration approaching the control jet acceleration. A slew control algorithm is incorporated which enables the same control law to be utilized for fuel optimal slewing through unlimited angles, one axis at a time. Simulation results are presented, including demonstration of stochastic performance with gyro and jet noise.