An invariance principle for dynamical systems in Hilbert spaces
Invariance method for extending Liapunov function to distributed parameter system
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Invariance method for extending Liapunov function to distributed parameter system
Invariance principle on Banach space for generalized thermoelastic stability analysis
Multiparameter optimum damping for harmonically excited linear stable strictly dissipative n degrees of freedom system, locating multivariable saddle points
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Research effort is described which is directed toward several aspects of the stability problem pertinent to the analysis of space vehicle guidance systems. Problems encountered during this study are mentioned.
Statistical properties of transients distributed in multivariable control system performance
Matrix Riccati equations for optimization in control theory
Indirect method considers effect of nonlinearity and obtains approximate probability densities of certain characteristics, from these densities various statistical properties are calculated. Direct method determines response moments directly. Neither method is restricted by system motion with regard to whether or not it is stationary.
Linear transfer function for describing human response to aircraft control
Techniques for obtaining quantitative information about boundedness properties are developed and applied to the sampled-data control of satellite attitude with quantization. Relevant stability concepts are introduced as a series of definitions, and interrelationships between various definitions are discussed. The boundedness regions are estimated by means of quadratic Liapunov functions, and a sufficient condition for the existence of a boundedness region is given for a certain class of systems. A quadratic Liapunov function is applied to the Lur'e-Postinkov class of systems, where the linear part of the system is not asymptotically stable and the quantizer represents the nonlinear characteristic. A numerical calculation of the region of boundedness estimates is performed for satellite attitude control and is compared with simulation results. It is tentatively concluded that the Liapunov results may be good and that simulation results may be difficult to interpret and time-consuming to generate. The Lur'e-based technique yields estimates of regions of absolute boundedness, but at the cost of greater analytical complexity.
This paper presents a Liapunov stability theory applicable to hybrid systems with multi-elastic domains. The mathematical formulation consists of a simultaneous set of ordinary and partial differential equations. A new stability theorem, particularly suited to such hybrid systems, is introduced. To predict the system stability by means of the theorem, it is necessary to construct a functional k, where k is free of spatial derivatives and bounding the Hamiltonian H from below. The conditions under which the construction of such a functional is possible are shown. As an application of the theory, the attitude stability of an earth-pointing satellite with multi-elastic domains is investigated and closed-form stability criteria derived.
An advanced two-body six-degree-of-freedom computer model employing an indeterminate structures approach has been developed for the parachute deployment process. The program determines both vehicular and decelerator responses to aerodynamic and physical property inputs. A better insight into the dynamic processes that occur during parachute deployment has been developed. The model is of value in sensitivity studies to isolate important parameters that affect the vehicular response.
The problem of determining the limiting performance characteristics of mechanical systems subject to random input is studied. A review is presented of the classical work in the optimal design of stochastic systems. Some recent results of stochastic optimal control theory are employed. The solution to the limiting performance problem is formulated in both the frequency and time domains. Both formulations require substantial, burdensome computations when applied to large scale systems.
Consider the situation in which the unknown parameters of a stationary linear system may be parametrized by a set of unknown parameters. The question thus arises of when such a set of parameters can be uniquely identified on the basis of observed data. This problem is considered here both in the case of input and output observations and in the case of output observations in the presence of a white noise input. Conditions for local identifiability are derived for both situations and a sufficient condition for global identifiability is given for the former situation, i.e., when simultaneous input and output observations are available.
A special class of decentralized control problem is discussed in which the objectives of the control agents are to steer the state of the system to desired levels. Each agent is concerned about certain aspects of the state of the entire system. The state and control equations are given for linear time-invariant systems. Stability and coordination, and the optimization of decentralized control are analyzed, and the information structure design is presented.
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A multilevel feedback control scheme is proposed for optimization of large-scale systems composed of a number of (not necessarily weakly coupled) subsystems. Local controllers are used to optimize each subsystem, ignoring the interconnections. Then, a global controller may be applied to minimize the effect of interconnections and improve the performance of the overall system. At the cost of suboptimal performance, this optimization strategy ensures invariance of suboptimality and stability of the systems under structural perturbations whereby subsystems are disconnected and again connected during operation.
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.