Search

This paper presents a novel multivariable two-time scale control method for flexible joint robots. The fundamental limitations of the conventional two-time scale control are clearly explained based on a singular perturbation analysis with a linearized dynamic model. Unlike the conventional methods, the bandwidth of the slow control can be made high and the derivatives of the rigid control are not required. The closed-form solution of a slow model that is more accurate than the rigid model is presented. It is shown that the modeling error of the proposed two-time scale control can be made arbitrarily small by the control gain only. A real-world experiment with a six degrees-of-freedom commercial industrial robot was performed to validate the presented approach.

This paper presents a two-time-scale neurodynamic approach to constrained minimax optimization using two coupled neural networks. One of the recurrent neural networks is used for minimizing the objective function and another is used for maximization. It is shown that the coupled neurodynamic systems operating in two different time scales work well for minimax optimization. The effectiveness and characteristics of the proposed approach are illustrated using several examples. Furthermore, the proposed approach is applied for $H_\infty $ model predictive control.

In transient stability analysis, the fifth-order generator model neglects the stator transients and may result in different simulation results as compared with the seventh-order model. Based on the two-time scale property of generators, a dynamic correction method is proposed to offset the difference. Using the singular perturbation theory, fast components in the stator flux linkage is captured to restore stator dynamics and correct the electromagnetic torque. Simulations on a single-machine-infinite-bus (SMIB) system and a 3-machine system show that, the difference between the two generator models can be remarkably reduced by the proposed method.

This note is concerned with the robust stability problem of uncertain two-time-scale systems, where the only information available for system uncertainties is their norm upper bounds. The stability bound /spl epsiv//sup */ is derived via a state transformation and the constructive use of a Lyapunov function. Finally, examples are given to illustrate the applications of the proposed results.

This comment points out several mathematical errors in the proof of Therorem 3, and gives the correct expression of $B_{3}$ .

This paper describes a fundamental study into modeling the effects of frequency variation on network and shunt impedances in long term power system stability simulations. The focus is on extreme post-fault situations where significant generation-load mismatch causes wide excursions in power system frequency. A novel application of singular perturbations is presented. This method is compared with the "constant reactance" technique, the "variable reactance" method in which the network reactances are varied with system frequency, and a method in which the network transients are fully simulated. Stability simulations for an interconnected two-area power system employing the different modeling techniques show that a two time scale analysis using the singular perturbation method is a feasible technique for accurate prediction of long term post fault system behaviour.

Output feedback control of linear time-invariant singularly perturbed systems is studied. The set of all compensators that stabilize a singularly perturbed system while preserving its two-time-scale structure is parameterized. The parameterization is used to show that any two-frequency-scale stabilizing compensator can be asymptotically approximated by a compensator designed via a sequential procedure. In this procedure, a fast (high-frequency) compensator is designed first to stabilize the fast model of the system. Then, a strictly proper slow (low-frequency) compensator is designed to stabilize a modified slow model. The parallel connection of the two compensators forms a two-frequency-scale stabilizing compensator for the singularly perturbed system.

The block processing technique is applied to a class of two-time-scale linear discrete-time systems. Block state-space structures for slow and fast subsystems have been obtained. Based upon these models, blocked slow and fast controls are designed. In addition, an extra control is designed to exactly compensate for the effects of the parasitics.

The design of stabilizing feedback control of singularly perturbed diserete-time systems is decomposed into the design of slow and fast controllers which are combined to form the composite control. Composite control strategies are developed for the case of single rate measurements (all variables are measured at the same rate) as well as for the case of multirate measurements (slow variables are measured at a rate slower than that of fast variables).

For a class of nonlinear singularly perturbed systems, feedback stabilizing and near-optimal controls are designed using two separate lower order subsystems. The two-time-scale properties greatly simplify the stability analysis and the nonlinear controller design. Two electric machine examples (a dc motor and a synchronous generator) illustrate the proposed design procedure.

Two-time-scale decoupling is adopted to obtain slow and fast subsystems. Based on these subsystems, a multirate composite control strategy is derived. It is shown how a recently presented multirate scheme can be modified to handle slow, nonzero mean disturbances, by introducing time incremental models. The improvements with the given multirate control law are illustrated by an example. >