BOOK-CHAPTER

Robust Control Design for Uncertain Nonlinear Dynamic Systems

Abstract

Robustness to parametric uncertainty is fundamental to successful control system design and as such it has been at the core of many design methods developed over the decades. Despite its prominence, most of the work on robust control design has focused on linear models and uncertainties that are non-probabilistic in nature. Recently, researchers have acknowledged this disparity and have been developing theory to address a broader class of uncertainties. This paper presents an experimental application of robust control design for a hybrid class of probabilistic and non-probabilistic parametric uncertainties. The experimental apparatus is based upon the classic inverted pendulum on a cart. The physical uncertainty is realized by a known additional lumped mass at an unknown location on the pendulum. This unknown location has the effect of substantially altering the nominal frequency and controllability of the nonlinear system, and in the limit has the capability to make the system neutrally stable and uncontrollable. Another uncertainty to be considered is a direct current motor parameter. The control design objective is to design a controller that satisfies stability, tracking error, control power, and transient behavior requirements for the largest range of parametric uncertainties. This paper presents an overview of the theory behind the robust control design methodology and the experimental results.

Keywords:
Control theory (sociology) Parametric statistics Robust control Probabilistic logic Robustness (evolution) Controllability Nonlinear system Control engineering Computer science Pendulum Controller (irrigation) Probabilistic design Inverted pendulum Engineering Control system Engineering design process Control (management) Mathematics Artificial intelligence

Metrics

1
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1.67
FWCI (Field Weighted Citation Impact)
5
Refs
0.77
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Probabilistic and Robust Engineering Design
Social Sciences →  Decision Sciences →  Statistics, Probability and Uncertainty
Control Systems and Identification
Physical Sciences →  Engineering →  Control and Systems Engineering
Numerical Methods and Algorithms
Physical Sciences →  Computer Science →  Computational Theory and Mathematics

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