On average-optimal nonlinear feedback control systems
Suboptimal nonlinear feedback control synthesis for linear time-invariant system with convex cost functional
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Suboptimal nonlinear feedback control synthesis for linear time-invariant system with convex cost functional
Method of predicting step response of control systems described by third order nonlinear differential equations
Synthesis of high order nonlinear control systems with ramp input
Nonlinear filtering for linear parabolic distributed parameter systems with white noise, considering stochastic boundary value problem
An iterative algorithm suitable for the solution of a system of nonlinear hyperbolic partial differentiation equations in multiple dimensions is discussed. Current numerical methods for systems of nonlinear PDEs have limited parallelism due to strong coupling between the equations. This method decouples the PDEs by linearizing the convention coefficient for a space-time domain. This provides large grain parallelism. The linearization also allows the treatment of some terms in the equations as source terms, providing more freedom to choose from a wider variety of numerical methods. Smaller grain parallelism may be exploited within the solves for each equation. Thus, the method has potential for parallelism at several levels.
A technique based on the Minimum Model Error optimal estimation approach is employed for robust identification of a nonlinear dynamic system. A simple harmonic oscillator with quadratic position feedback was simulated on an analog computer. With the aid of analog measurements and an assumed linear model, the Minimum Model Error Algorithm accurately identifies the quadratic nonlinearity. The tests demonstrate that the method is robust with respect to prior ignorance of the nonlinear system model and with respect to measurement record length, regardless of initial conditions.
For applying linear parameter varying (LPV) control synthesis and analysis to a nonlinear system, it is required that a nonlinear system be represented in the form of an LPV model. In this paper, a new representation method is developed to construct an LPV model from a nonlinear mathematical model without the restriction that an operating point must be in the neighborhood of equilibrium points. An LPV model constructed by the new method preserves local stabilities of the original nonlinear system at "frozen" scheduling parameters and also represents the original nonlinear dynamics of a system over a non-trim region. An LPV model of the motion of FASER (Free-flying Aircraft for Subscale Experimental Research) is constructed by the new method.
Input-output stability conditions of time-varying nonlinear feedback systems obtained, using concepts of loop gain, conicity and positivity
Stability theory based on functional methods, examining feedback system with linear time invariant and nonlinear elements
Monte Carlo methods for conditional expectation for nonlinear dynamic systems with acceptable computing time expenditure
Computation of regions of constrained stability for nonlinear control systems
Almost periodic behavior of solutions of nonlinear Volterra system
The Kronecker indices of smooth affine-nonlinear control systems presently defined are noted to determine the collection of controllable linear systems that are obtainable on the basis of local feedback equivalences and state-space reductions. Because attention is given to the invariants of systems under equivalences, and the behavior of these invariants under mappings that arise from state submersions, local representations of systems are used wherever possible to render cases more accessible.
The design of a modified fault inferring nonlinear detection system (FINDS) algorithm for a dual-processor configured flight computer is described. The algorithm was changed in order to divide it into its translational dynamics and rotational kinematics and to use it for parallel execution on the flight computer. The FINDS consists of: (1) a no-fail filter (NFF), (2) a set of test-of-mean detection tests, (3) a bank of first order filters to estimate failure levels in individual sensors, and (4) a decision function. NFF filter performance using flight recorded sensor data is analyzed using a filter autoinitialization routine. The failure detection and isolation capability of the partitioned algorithm is evaluated. A multirate implementation for the bias-free and bias filter gain and covariance matrices is discussed.
Inverse optimum control problem for nonlinear closed loop autonomous system, determining performance criteria for given control law
Computational techniques developed for identifying linear and nonlinear mechanical systems subject to random excitation
Stability domain determination for nonlinear dynamical system
The motivation for using polynomic combinations of system states and inputs to model nonlinear dynamics systems is founded upon the classical theories of analysis and function representation. A feature of such representations is the need to make available all possible monomials in these variables, up to the degree specified, so as to provide for the description of widely varying functions within a broad class. For a particular application, however, certain monomials may be quite superfluous. This paper examines the possibility of removing monomials from the model in accordance with the level of sensitivity displayed by the residuals to their absence. Critical in these studies is the effect of system input excitation, and the effect of discarding monomial terms, upon the model parameter set. Therefore, model reduction is approached iteratively, with inputs redesigned at each iteration to ensure sufficient excitation of remaining monomials for parameter approximation. Examples are reported to illustrate the performance of such model reduction approaches.