TL;DR: In this paper, the problem of the derivation of simple transfer function models from high-order state variable models is reviewed, and methods of reduction are classified according to whether they involve 1) the computation of the time or frequency responses, 2) the derivations, as an intermediate step, of a transfer function which is the ratio of two polynomials, the denominator being of the same order as the state variable model, or 3) a set of characterising functions.

TL;DR: In this article, the invariant structure of a controllable matrix pair (A, B) under a transformation group G, which includes the regular linear coordinate transformations and also transformations of'state feedback' type which take A into A + BF for some F, is studied.

TL;DR: An algorithm is described for recursively calculating the minimal partial repreentations for the sequences R"0,..., R"N, N=0, 1, ..., taken as inputs to the algorithm.

TL;DR: In this paper, a stabilizing feedback control for Haldane-Monod model of microbial growth is designed for stabilizing the growth of a set of microorganisms, which is optimal in the sense that it approaches the maximum production steady state from any initial state.

TL;DR: Arbib and Zeiger's generalization of Ho's algorithm for system identification is presented from an alternative viewpoint called ''state characterization'' and it is proposed that state characterization may have practical application in determining an approximate, low order description of a complex system about which the authors have little prior information.

TL;DR: In this paper, the problem of optimal control of a system whose mathematical model involves dependence on both a history of state type variables and the control variables is considered, and the canonical equations which one obtains by application of a maximum principle for the optimal control problem are transformed into equivalent integral equations which can be solved by use of the Fredholm resolvent theory.

TL;DR: In this paper, the problem of finding all nonnegative definite solutions to the ''Riccati'' algebraic equation PA + A'P - PBB'P + C'C = 0 is solved in general.

TL;DR: In this paper, a method for synthesizing suboptimal feedback control laws for nonlinear systems optimized with respect to a quadratic performance index is presented, which allows the designer to easily calculate a second-order approximation to the optimal control.

TL;DR: In this article, Luenberger's minimal-order observer is considered as an alternate to the Kalman filter for obtaining state estimates in linear discrete-time stochastic systems and the observer solution is extended to systems for which the noise disturbances are time-wise correlated processes of the Markov type.

TL;DR: A new algorithm for the solution of the Parameter-Adaptive Self-Organizing Control Problem is proposed, based on the learning property of the a posteriori probabilities of the parameter estimates, which forces the algorithm to converge to the optimal solution rapidly.

TL;DR: In this article, the formulation of the optimal, linear, time-invariant, multivariable control problem with quadratic performance index is extended so that the solution includes multivariability integral feedback and model-following capabilities in addition to the normal proportional state feedback.

TL;DR: In this article, a bound on the accuracy of causally estimating a Gaussian process from nonlinear observations is derived, where both additive Gaussian noise and Poisson observations are included.

TL;DR: This paper attempts to examine the interdependence of these two problems and to develop an iterative computational approach for solving the joint problem which actually arises in practice; namely, the optimization of a plant whose parameters are in fact unknown.

TL;DR: In this article, a new approach to the design of state estimators for systems with large, but bounded uncertainties in plant and measurement noise covariances is proposed and explored, where a linear estimator with unspecified gain is chosen a priori.

TL;DR: In this paper, the Riccati equation reached equilibrium in finite time in constant or invariant directions of a class of singular, autonomous discrete-time optimization problems, and a control point of view was used to completely characterize the space of constant directions for the case of multi-input control.

TL;DR: It is shown that approximation at the end has no advantage over approximation atThe beginning for the distributed system and the method of approximation considered and the question of obtaining numerical solutions by Galerkin's approximation done at the beginning and at theend is considered.

TL;DR: This paper presents some new research results concerning the sample stability, as opposed to statistical, or ensemble stability, of linear stochastic delay-differential equations.

TL;DR: In this paper, the authors describe an application of modal analysis for determining the control system of a chemical plant described by 41 linearized differential equations with 8 inputs, where a proportional feedback control system is found so that the dominant time constant of the closed loop system is reduced and then an integral feedback control is found to eliminate the effect of constant unknown disturbances on the system.

TL;DR: In this article, a family of variable-altitude turns obtained by numerical integration in the reduced-order approximation is presented for a hypothetical supersonic aircraft, including the effects of constraints on altitude, dynamic pressure, Mach number, lift coefficient, and normal load factor.

TL;DR: In this article, an optimal control policy for a discrete-time linear system with interrupted observations and an expected quadratic cost is proposed, which is realized by cascading a nonlinear estimator, which computes the conditional mean of the state vector, with the optimal feedback gain matrix in which all uncertainties are removed.

TL;DR: For a completely specified strongly connected synchronous sequential machine, the problem of constructing the input-output sequences that will be sufficient to identify the machine, given its flow table, is first studied and an adaptive procedure is suggested.

TL;DR: In this paper, a modified form of the Leverrier algorithm is presented which has been found satisfactory for matrices whose elements differ by several orders of magnitude, and the results are compared with a method presented by Morgan [4] and a modified version of the leverrier algorithm.

TL;DR: The class of learning systems under consideration uses generalized linear algorithms which evaluate the appropriate parameters after processing the arbitrary groups of data.

TL;DR: The manifold imbedding method may be regarded as a variation of the Newton-Raphson iterative procedure characterized by an extended region of convergence and the resulting reduction of guesswork and the unified approach to a wide range of optimization problems are amongst the favorable features.

TL;DR: In this article, the authors present an algorithm for generating a sequence with a limit point which satisfies a necessary condition for a minimax solution, using only linear programming, quadratic programming, and one-dimensional direct search.

TL;DR: In this article, the authors considered the problem of optimal periodic step function control with a bounded number of switches per period and showed that for certain types of objective functions and integral constraints.

TL;DR: In this article, measurements and on-line identification carried out on a tandem steel mill as part of a project to develop an improved computer control system for the mill were described and analyzed.

TL;DR: The convergent characteristics of the optimizing control using the fuzzy automata are investigated with regard to the convergent coefficient of the reinforcement algorithm for the learning operation of the automata.

TL;DR: In this article, the authors considered a set of time-invariant linear partial differential equations with spatially generalized Wiener process disturbance of the state and point measurement, and used the Caratheodory lemma to obtain sufficient conditions for the separation into linear optimal state estimator and linear optimal deterministic controller.