2,648 research outputs found
Disturbance Observer-based Robust Control and Its Applications: 35th Anniversary Overview
Disturbance Observer has been one of the most widely used robust control
tools since it was proposed in 1983. This paper introduces the origins of
Disturbance Observer and presents a survey of the major results on Disturbance
Observer-based robust control in the last thirty-five years. Furthermore, it
explains the analysis and synthesis techniques of Disturbance Observer-based
robust control for linear and nonlinear systems by using a unified framework.
In the last section, this paper presents concluding remarks on Disturbance
Observer-based robust control and its engineering applications.Comment: 12 pages, 4 figure
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning
One major challenge for autonomous attitude takeover control for on-orbit
servicing of spacecraft is that an accurate dynamic motion model of the
combined vehicles is highly nonlinear, complex and often costly to identify
online, which makes traditional model-based control impractical for this task.
To address this issue, a recursive online sparse Gaussian Process (GP)-based
learning strategy for attitude takeover control of noncooperative targets with
maneuverability is proposed, where the unknown dynamics are online compensated
based on the learnt GP model in a semi-feedforward manner. The method enables
the continuous use of on-orbit data to successively improve the learnt model
during online operation and has reduced computational load compared to standard
GP regression. Next to the GP-based feedforward, a feedback controller is
proposed that varies its gains based on the predicted model confidence,
ensuring robustness of the overall scheme. Moreover, rigorous theoretical
proofs of Lyapunov stability and boundedness guarantees of the proposed
method-driven closed-loop system are provided in the probabilistic sense. A
simulation study based on a high-fidelity simulator is used to show the
effectiveness of the proposed strategy and demonstrate its high performance.Comment: 17 pages, 14 figures. Submitted to in IEEE Transactions on Aerospace
and Electronic System
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