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At least 595 records · Page 33

The state space and transfer function approaches in practical linear multivariable systems design.

A procedure using transfer function formalism for stabilizing a multivariable feedback system with significant plant uncertainty is presented. The method shapes nominal loop functions and decreases their magnitudes with frequency in such a manner that the system remains stable throughout the plant uncertainty range. An example illustrates the application of the described procedure.

Shaked, U.↗

A direct approach to the design of linear multivariable systems

Design of multivariable systems is considered and design procedures are formulated in the light of the most recent work on model matching. The word model matching is used exclusively to mean matching the input-output behavior of two systems. The term is used in the frequency domain to indicate the comparison of two transfer matrices containing transfer functions as elements. Design methods where non-interaction is not used as a criteria were studied. Two design methods are considered. The first method of design is based solely upon the specification of generalized error coefficients for each individual transfer function of the overall system transfer matrix. The second design method is called the pole fixing method because all the system poles are fixed at preassigned positions. The zeros of terms either above or below the diagonal are partially fixed via steady state error coefficients. The advantages and disadvantages of each method are discussed and an example is worked to demonstrate their uses. The special cases of triangular decoupling and minimum constraints are discussed.

Agrawal, B. L.↗

Analytical method for determining the stability of linear retarded systems with two delays

The stability is considered of the solution differential-difference equations of the retarded type with constant coefficients and two constant time delays. A method that makes use of analytical expressions to determine stability boundaries, and the stability of the system, is derived. The method was applied to a system represented by a second-order differential equation with constant coefficients and time delays in the velocity and displacement terms. The results obtained is in agreement with those obtained by other investigators.

Barker, L. K.↗

Response of linear dynamic systems with random coefficients

Numerous models of physical systems contain parameters whose values are not known exactly. The physical and mathematical complexities arising in the prediction of the statistical behavior of such systems are discussed. Although the discussions are far from providing a satisfactory solution to such problems, they perhaps, by utilization of simple examples, will create a greater awareness of the statistical effect of random parameters.

Dickerson, J.↗

An application of the square root information filter to large scale linear interconnected systems

It is demonstrated that use of the square root information filter (SRIF) can reduce the storage and computation required for estimation of certain classes of large-scale interconnected systems. The SRIF uses an information array that is related to the Kalman filter covariance and estimate. The SRIF algorithm, which is optimal, is a direct application of matrix partitioning to some optimal filtering algorithms described in the literature. The SRIF algorithm is able to reduce the storage requirements of a 40-subsystem 10-state problem by a full order of magnitude.

Bierman, G. J.↗

Riccati group invariants of linear hamiltonian systems

The action of the Riccati group on the Riccati differential equation is associated with the action of a subgroup of the symplectic group on a set of hamiltonian matrices. Within this framework various sets of canonical forms are developed for the matrix coefficients of the Riccati differential equation. The canonical forms presented are valid for arbitrary Kronecker indices, and it is shown that the Kronecker indices are invariants for this group action. These canonical forms are useful for studying problems arising in the areas of optimal decentralized control and the spectral theory of optimal control problems.

Garzia, M. R.↗

Linear Hamiltonian systems - The Riccati group and its invariants

The action of the Riccati group on the Riccati differential equation is associated with the action of a subgroup of the symplectic group on a set of Hamiltonian matrices. Within this framework canonical forms are developed for the matrix coefficients of the Riccati differential equation.

Garzia, M. R.↗

Development and validation of a general purpose linearization program for rigid aircraft models

A FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models is discussed. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high performance aircraft.

Duke, E. L.↗

Development and validation of a general purpose linearization program for rigid aircraft models

This paper discusses a FORTRAN program that provides the user with a powerful and flexible tool for the linearization of aircraft models. The program LINEAR numerically determines a linear systems model using nonlinear equations of motion and a user-supplied, nonlinear aerodynamic model. The system model determined by LINEAR consists of matrices for both the state and observation equations. The program has been designed to allow easy selection and definition of the state, control, and observation variables to be used in a particular model. Also, included in the report is a comparison of linear and nonlinear models for a high-performance aircraft.

Duke, E. L.↗