The generation of dual wavelength pulse fiber laser using fiber bragg grating

A stable simple generation of dual wavelength pulse fiber laser on experimental method is proposed and demonstrated by using Figure eight circuit diagram. The generation of dual wavelength pulse fiber laser was proposed using fiber Bragg gratings (FBGs) with two different central wavelengths which are 1550 nm and 1560 nm. At 600 mA (27.78 dBm) of laser diode, the stability of dual wavelength pulse fiber laser appears on 1550 nm and 1560 nm with the respective peak powers of -54.03 dBm and -58.00 dBm. The wavelength spacing of the spectrum is about 10 nm while the signal noise to ratio (SNR) for both peaks are about 8.23 dBm and 9.67 dBm. In addition, the repetition rate is 2.878 MHz with corresponding pulse spacing of about 0.5 μs, is recorded

Predictive pole-placement control with linear models

The predictive pole-placement control method introduced in this paper embeds the classical pole-placement state feedback design into a quadratic optimisation-based model-predictive formulation. This provides an alternative to model-predictive controllers which are based on linear–quadratic control. The theoretical properties of the controller in a linear continuous-time setting are presented and a number of illustrative examples are given. These results provide the foundation for novel linear and nonlinear constrained predictive control methods based on continuous-time models

Control bifurcations

A parametrized nonlinear differential equation can have multiple equilibria as the parameter is varied. A local bifurcation of a parametrized differential equation occurs at an equilibrium where there is a change in the topological character of the nearby solution curves. This typically happens because some eigenvalues of the parametrized linear approximating differential equation cross the imaginary axis and there is a change in stability of the equilibrium. The topological nature of the solutions is unchanged by smooth changes of state coordinates so these may be used to bring the differential equation into Poincare/spl acute/ normal form. From this normal form, the type of the bifurcation can be determined. For differential equations depending on a single parameter, the typical ways that the system can bifurcate are fully understood, e.g., the fold (or saddle node), the transcritical and the Hopf bifurcation. A nonlinear control system has multiple equilibria typically parametrized by the set value of the control. A control bifurcation of a nonlinear system typically occurs when its linear approximation loses stabilizability. The ways in which this can happen are understood through the appropriate normal forms. We present the quadratic and cubic normal forms of a scalar input nonlinear control system around an equilibrium point. These are the normal forms under quadratic and cubic change of state coordinates and invertible state feedback. The system need not be linearly controllable. We study some important control bifurcations, the analogues of the classical fold, transcritical and Hopf bifurcations

A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control

This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control. We generalize the well-known iLQR algorithm to different multiple-shooting variants, combining advantages like straight-forward initialization and a closed-loop forward integration. All algorithms have similar computational complexity, i.e. linear complexity in the time horizon, and can be derived in the same computational framework. We compare the full-step variants of our algorithms and present several simulation examples, including a high-dimensional underactuated robot subject to contact switches. Simulation results show that our multiple-shooting algorithms can achieve faster convergence, better local contraction rates and much shorter runtimes than classical iLQR, which makes them a superior choice for nonlinear model predictive control applications.Comment: 8 page

Multiobjective control of a four-link flexible manipulator: A robust H∞ approach

Copyright [2002] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper presents an approach to robust H∞ control of a real multilink flexible manipulator via regional pole assignment. We first show that the manipulator system can be approximated by a linear continuous uncertain model with exogenous disturbance input. The uncertainty occurring in an operating space is assumed to be norm-bounded and enter into both the system and control matrices. Then, a multiobjective simultaneous realization problem is studied. The purpose of this problem is to design a state feedback controller such that, for all admissible parameter uncertainties, the closed-loop system simultaneously satisfies both the prespecified H∞ norm constraint on the transfer function from the disturbance input to the system output and the prespecified circular pole constraint on the closed-loop system matrix. An algebraic parameterized approach is developed to characterize the existence conditions as well as the analytical expression of the desired controllers. Third, by comparing with the traditional linear quadratic regulator control method in the sense of robustness and tracking precision, we provide both the simulation and experimental results to demonstrate the effectiveness and advantages of the proposed approach

Hidden attractors in fundamental problems and engineering models

Recently a concept of self-excited and hidden attractors was suggested: an attractor is called a self-excited attractor if its basin of attraction overlaps with neighborhood of an equilibrium, otherwise it is called a hidden attractor. For example, hidden attractors are attractors in systems with no equilibria or with only one stable equilibrium (a special case of multistability and coexistence of attractors). While coexisting self-excited attractors can be found using the standard computational procedure, there is no standard way of predicting the existence or coexistence of hidden attractors in a system. In this plenary survey lecture the concept of self-excited and hidden attractors is discussed, and various corresponding examples of self-excited and hidden attractors are considered