Search

A method for derivation of closed-form solutions for the differential Riccati matrix equation for specific time-varying systems is presented. It allows more insight into the nature of the solution. It reduces the on-line computation requirements, since it does not require on-line solution of a differential equation. Sufficient conditions for the existence of the closed-form solution are given. The method is applied to a target tracking problem.

A control algorithm for control of linear uncertain systems is presented. This algorithm is applied to the control of an unstable nonminimum phase uncertain dynamic vehicle. The algorithm is based on the state and parameters observability canonical (SPOC) form and a certainty equivalence pole placement controller. The algorithm provides single controller for the problem of the uncertain dynamic vehicle that approaches the performance of a controller designed for perfectly known parameters within the whole range of the plant's parameters uncertainty. The algorithm is globally bounded input bounded output stable and needs only the knowledge of the order of the plant. The algorithm is presented and its performance is demonstrated by simulations.

The effectof instabilities when the fast Fourier transform is used for spectral estimation of nonuniformly sampled harmonic signals is studied. Both numerical and simulation results are compared for the independent and accumulated jitter cases of random sampling. Explicit expressions for the jitter-caused degradation are given. They indicate the severe degradation due to accumulated jitter as compared to the moderate degradation caused by independent jitter.

The problem of optimal control of stochastic discrete parameter-varying uncertain linear systems is formulated and partially solved. The solution is achieved by a generalization of the State and Parameters Observability Canonical form - SPOC to parameter varying systems. This representation of linear time-variant systems enables the application of tools from the LQR-LQG theory of control and estimation of discrete linear parameter-varying systems. The optimal solution is exact and noncausal. A causal suboptimal controller, using certainty equivalence, is proposed as an adhoc solution. This controller needs only the knowledge of the order of the system. The scheme is BIBO stable for sufficiently low noises and sufficiently slowly varying parameters. As an example, the proposed algorithm is applied to a difficult problem: control of an unstable nonminimum phase parameter-varying model of a dynamic launch vehicle with very large uncertainty, and unmodelled dynamics. Care is devoted to analysis of the robustness of the control algorithm. It is demonstrated via simulations that as long as the assumptions are valid the algorithm remains stable and preserves performance. [ABSTRACT FROM AUTHOR]

The existing optimal guidance laws assume that the time-to-go is known exactly. The time-to-go is usually estimated and thus is a random variable. The issue of optimal guidance with uncertain time-to-go is dealt with here. A problem of control of linear discrete systems with unknown time-to-go is formulated and solved. The solution is applied to derive guidance laws. The solution depends on the probability density function of the time-of-flight. This guidance law has the structure of a rendezvous guidance law where the guidance gains are time-dependent and depend on the distribution of the time-to-go. Examples that demonstrate these dependencies are presented.