Decoupled Dynamics for Control and Estimation

Decoupling of the dynamical equations in polar coordinates is used to develop a control scheme for use in target-intercept problems with passive measurements. By defining a pseudo control variable in the radial coordinate, the radial dynamics is made independent of the transverse dynamics. After solving for the radial control, the transverse control is determined through solutions to a two-point boundary value problem. Numerical results from a six degree-of-freedom simulation which used the decoupled control indicate that it is better than the completely Cartesian coordinate control for most of the cases considered. Decoupled control, though, is obtained iteratively through a two-point boundary value problem and, hence, is more computationally intensiv

Reproductive biology of Parapenaeopsis stylifera

Controversy exists regarding the incidence and reproductive habits of Parapenaeopsis stylifera along the coast of Kerala, India. Results are reported of a detailed study on the different aspects of the reproductive biology based on the trawl catches landed at Neendakara Coast for a period of one year. This species breeds throughout the year with peak breeding activity in May and November. The monsoon catches consist of juveniles. Life span of the species is 2.5 years and total length at first maturity is 75 mm. Measures for the effective management of the species in the area are discussed

Fuzzy Logic in Restructurable Flight Control Systems

In this paper, a fuzzy logic controller (FLC) is proposed for restructurable flight control systems. The role of the FLC is to stabilize the aircraft upon a fault occurrence. The FLC derives pitch/roll/yaw controls from a generic knowledge base characterized by 49 if-then rules. A linearized model representative of a modern jet fighter provides the basis for the numerical simulation. Simulated faults include various degrees of surface loss at the right stabilator, combined with reduced ailerons and rudder control power. The FLC accomplishes the stabilization task under test conditions without any knowledge of the system parameters. The numerical results demonstrate the potential of the FLC as a suitable control algorithm that bridges the critical gap between the fault occurrence and the full implementation of the new control la

Rate coefficients for rovibrational transitions in H_2 due to collisions with He

We present quantum mechanical and quasiclassical trajectory calculations of cross sections for rovibrational transitions in ortho- and para-H_2 induced by collisions with He atoms. Cross sections were obtained for kinetic energies between 10^-4 and 3 eV, and the corresponding rate coefficients were calculated for the temperature range 100<T<4000 K. Comparisons are made with previous calculations.Comment: 21 pages, 2 figures, AAS, eps

Novel magnetic phases in a Gd2Ti2O7 pyrochlore for a field applied along the [100] axis

We report on longitudinal and transverse magnetisation measurements performed on single crystal samples of Gd2Ti2O7 for a magnetic field applied along the [100] direction. The measurements reveal the presence of previously unreported phases in fields below 10 kOe in an addition to the higher-field-induced phases that are also seen for H//[111], [110], and [112]. The proposed H-T phase diagram for the [100] direction looks distinctly different from all the other directions studied previously.Comment: 4 pages, 5 figure

Online Identification and Control of Aerospace Vehicles Using Recurrent Networks

Methods for estimating the aerospace system parameters and controlling them through two neural networks are presented in this study. We equate the energy function of Hopfield neural network to integral square of errors in the system dynamics and extract the parameters of a system. Parameter convergence is proved. For control, we equate the equilibrium status of a modified Hopfield neural network to the steady state Riccati solution with the system parameters as inputs. Through these two networks, we present the online identification and control of an aircraft using its nonlinear dynamics

Adaptive Critic Based Neural Networks for Control (Low Order System Applications)

Dynamic programming is an exact method of determining optimal control for a discretized system. Unfortunately, for nonlinear systems the computations necessary with this method become prohibitive. This study investigates the use of adaptive neural networks that utilize dynamic programming methodology to develop near optimal control laws. First, a one dimensional infinite horizon problem is examined. Problems involving cost functions with final state constraints are considered for one dimensional linear and nonlinear systems. A two dimensional linear problem is also investigated. In addition to these examples, an example of the corrective capabilities of critics is shown. Synthesis of the networks in this study needs no external training; they do not need any apriori knowledge of the functional form of control. Comparison with specific optimal control techniques show that the networks yield optimal control over the entire range of trainin

Robust Adaptive Critic Based Neurocontrollers for Systems with Input Uncertainties

A two-neural network approach to solving optimal control problems is described in this study. This approach called the adaptive critic method consists of two neural networks: one is called the supervisor or critic, and the other is called an action network or controller. The inputs to both these networks are the current states of the system to be controlled. Each network is trained through an output of the other network and the conditions for optimal control. When their outputs are mutually consistent, the controller network output is optimal. The optimality is limited to the underlying model. Hence, we develop a Lyapunov based theory for robust stability of these controllers when there is input uncertainty. We illustrate this approach through a longitudinal autopilot of a nonlinear missile problem

Optimal Management of Beaver Population using a Reduced-Order Distributed Parameter Model and Single Network Adaptive Critics

Beavers are often found to be in conflict with human interests by creating nuisances like building dams on flowing water (leading to flooding), blocking irrigation canals, cutting down timbers, etc. At the same time they contribute to raising water tables, increased vegetation, etc. Consequently, maintaining an optimal beaver population is beneficial. Because of their diffusion externality (due to migratory nature), strategies based on lumped parameter models are often ineffective. Using a distributed parameter model for beaver population that accounts for their spatial and temporal behavior, an optimal control (trapping) strategy is presented in this paper that leads to a desired distribution of the animal density in a region in the long run. The optimal control solution presented, imbeds the solution for a large number of initial conditions (i.e., it has a feedback form), which is otherwise nontrivial to obtain. The solution obtained can be used in real-time by a nonexpert in control theory since it involves only using the neural networks trained offline. Proper orthogonal decomposition-based basis function design followed by their use in a Galerkin projection has been incorporated in the solution process as a model reduction technique. Optimal solutions are obtained through a single network adaptive critic (SNAC) neural-network architecture

Use of Energy in Data-Track Association and State Estimation in Multitarget-Multisensor Problems

A coarse test and a fine test have been formulated for use in multiple sensor-multiple target data-track association. Various forms of energies are used in these tests to pick the proper data to be associated with any target. Numerical experiments which involve the use of a decentralized Kalman filter scheme are presented. The experiments show the effectiveness of the two test