17,439 research outputs found
Yet Another Tutorial of Disturbance Observer: Robust Stabilization and Recovery of Nominal Performance
This paper presents a tutorial-style review on the recent results about the
disturbance observer (DOB) in view of robust stabilization and recovery of the
nominal performance. The analysis is based on the case when the bandwidth of
Q-filter is large, and it is explained in a pedagogical manner that, even in
the presence of plant uncertainties and disturbances, the behavior of real
uncertain plant can be made almost similar to that of disturbance-free nominal
system both in the transient and in the steady-state. The conventional DOB is
interpreted in a new perspective, and its restrictions and extensions are
discussed
The passivity of adaptive output regulation of nonlinear exosystem with application of aircraft motions
This paper deals with passivity of adaptive output regulation of nonlinear exosystem. It is shown that factorisable low-high frequency gains and harmonic uncertainties are estimated to the exogenous signals with adaptive nonlinear system. The design methodology guarantees asymptotic regulation in the case where the dimension of the regulator is sufficiently large in relation, which affects the number of harmonics acting on the system. On the other hand, harmonics of uncertain amplitude, phase, and frequency are the major sources, and the bounded steady-state regulation error ensures that adaptive nonlinear system is globally asymptotically stable via passivity theory. Kalman–Yacubovitch–Popov property provides that the uncertain adaptive nonlinear system is passive. Finally, specific examples are shown in order to demonstrate the applicability of the result
Robust Asymptotic Stabilization of Nonlinear Systems with Non-Hyperbolic Zero Dynamics
In this paper we present a general tool to handle the presence of zero
dynamics which are asymptotically but not locally exponentially stable in
problems of robust nonlinear stabilization by output feedback. We show how it
is possible to design locally Lipschitz stabilizers under conditions which only
rely upon a partial detectability assumption on the controlled plant, by
obtaining a robust stabilizing paradigm which is not based on design of
observers and separation principles. The main design idea comes from recent
achievements in the field of output regulation and specifically in the design
of nonlinear internal models.Comment: 30 pages. Preliminary versions accepted at the 47th IEEE Conference
on Decision and Control, 200
"Class-Type" identification-based internal models in multivariable nonlinear output regulation
The paper deals with the problem of output regulation in a “non-equilibrium” context for a special class of multivariable nonlinear systems stabilizable by high-gain feedback. A post-processing internal model design suitable for the multivariable nature of the system, which might have more inputs than regulation errors, is proposed. Uncertainties in the system and exosystem are dealt with by assuming that the ideal steady state input belongs to a certain “class of signals" by which an appropriate model set for the internal model can be derived. The adaptation mechanism for the internal model is then cast as an identification problem and a least square solution is specifically developed. In line with recent developments in the field, the vision that emerges from the paper is that approximate, possibly asymptotic, regulation is the appropriate way of approaching the problem in a multivariable and uncertain context. New insights about the use of identification tools in the design of adaptive internal models are also presented
A nonparametric learning framework for nonlinear robust output regulation
This paper proposes a nonparametric learning solution framework for a generic
internal model design of nonlinear robust output regulation. The global robust
output regulation problem for a class of nonlinear systems with output feedback
subject to a nonlinear exosystem can be tackled by constructing a linear
generic internal model, provided that a continuous nonlinear mapping exists. An
explicit continuous nonlinear mapping was constructed recently in [1] under the
assumption that the steady-state generator is linear in the exogenous signal.
We further relax such an assumption to a relaxed assumption that the
steady-state generator is polynomial in the exogenous signal. A nonparametric
learning framework is proposed to solve a linear time-varying equation to make
the nonlinear continuous mapping always exist. With the help of the proposed
framework, the nonlinear robust output regulation problem can be converted into
a robust non-adaptive stabilization problem for the augmented system with
integral Input-to-State Stable (iISS) inverse dynamics. Moreover, a dynamic
gain approach can adaptively raise the gain to a sufficiently large constant to
achieve stabilization without requiring any a priori knowledge of the
uncertainties appearing in the dynamics of the exosystem and the system. We
further apply the nonparametric learning framework to globally reconstruct and
estimate multiple sinusoidal signals with unknown frequencies without using
adaptive techniques. An explicit nonlinear mapping can directly provide the
estimated parameters, which will exponentially converge to the unknown
frequencies. As a result, a feedforward control design is proposed to solve the
output regulation using our nonparametric learning framework.Comment: 15 pages; Nonlinear control; iISS stability; output regulation;
parameter estimation; Non-adaptive contro
Robust Asymptotic Stabilization of Nonlinear Systems With Non-Hyperbolic Zero Dynamics
International audienceWe present a general tool to handle the presence of zero dynamics which are asymptotically but not locally exponentially stable in problems of robust nonlinear stabilization by output feedback. We show how it is possible to design locally Lipschitz stabilizers under conditions which only rely upon a partial detectability assumption on the controlled plant, by obtaining a robust stabilizing paradigm which is not based on design of observers and separation principles. The main design idea comes from recent achievements in the field of output regulation and specifically in the design of nonlinear internal models
- …