16,569 research outputs found
Sufficient conditions for robust performance of adaptive controllers with general uncertainty structure
Sufficient conditions are given under which an adaptive control system is robustly stable and achieves a guaranteed robust asymptotic performance level equal to that of the robust controller given perfect parameter information. The conditions are general in several respects. For example, structured non-parametric uncertainty (e.g. block diagonal) is allowed, as well as exogenous noise inputs. In addition, the structure of the parametric uncertainty is very general, and even allows for parameters which scale the uncertainty magnitudes. This allows one to identify the size of the non-parametric uncertainty and to schedule the controller based on this size. Finally, the robust gain scheduled controller is largely unrestricted. Identification mechanisms which are proven to satisfy the sufficient conditions are not given here and, for the general problem, have not yet been developed. However, an example of such a mechanism for a subclass of systems does exist and is referenced. For the general problem, this paper provides properties to be sought in the development of robust identification laws for robust adaptive control.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30184/1/0000569.pd
Adaptive ââ-control for nonlinear systems: a dissipation theoretical approach
The adaptive ââ-control problem for parameter-dependent nonlinear systems with full information feedback is considered. The techniques from dissipation theory as well as the vector and parameter projection methods are used to derive the adaptive ââ-control laws. Both of the projection techniques are rigorously treated. The adaptive robust stabilization for nonlinear systems with â2-gain hounded uncertainties is investigated
Process operating mode monitoring : switching online the right controller
This paper presents a structure which deals with
process operating mode monitoring and allows the control law reconfiguration
by switching online the right controller. After a short
review of the advances in switching based control systems during
the last decade, we introduce our approach based on the definition
of operating modes of a plant. The control reconfiguration
strategy is achieved by online selection of an adequate controller,
in a case of active accommodation. The main contribution lies
in settling up the design steps of the multicontroller structure
and its accurate integration in the operating mode detection and
accommodation loop. Simulation results show the effectiveness
of the operating mode detection and accommodation (OMDA)
structure for which the design steps propose a method to study the
asymptotic stability, switching performances improvement, and
the tuning of the multimodel based detector
Sparse Wide-Area Control of Power Systems using Data-driven Reinforcement Learning
In this paper we present an online wide-area oscillation damping control
(WAC) design for uncertain models of power systems using ideas from
reinforcement learning. We assume that the exact small-signal model of the
power system at the onset of a contingency is not known to the operator and use
the nominal model and online measurements of the generator states and control
inputs to rapidly converge to a state-feedback controller that minimizes a
given quadratic energy cost. However, unlike conventional linear quadratic
regulators (LQR), we intend our controller to be sparse, so its implementation
reduces the communication costs. We, therefore, employ the gradient support
pursuit (GraSP) optimization algorithm to impose sparsity constraints on the
control gain matrix during learning. The sparse controller is thereafter
implemented using distributed communication. Using the IEEE 39-bus power system
model with 1149 unknown parameters, it is demonstrated that the proposed
learning method provides reliable LQR performance while the controller matched
to the nominal model becomes unstable for severely uncertain systems.Comment: Submitted to IEEE ACC 2019. 8 pages, 4 figure
Robust nonlinear control of vectored thrust aircraft
An interdisciplinary program in robust control for nonlinear systems with applications to a variety of engineering problems is outlined. Major emphasis will be placed on flight control, with both experimental and analytical studies. This program builds on recent new results in control theory for stability, stabilization, robust stability, robust performance, synthesis, and model reduction in a unified framework using Linear Fractional Transformations (LFT's), Linear Matrix Inequalities (LMI's), and the structured singular value micron. Most of these new advances have been accomplished by the Caltech controls group independently or in collaboration with researchers in other institutions. These recent results offer a new and remarkably unified framework for all aspects of robust control, but what is particularly important for this program is that they also have important implications for system identification and control of nonlinear systems. This combines well with Caltech's expertise in nonlinear control theory, both in geometric methods and methods for systems with constraints and saturations
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