1,182 research outputs found
Evaluation of automated decisionmaking methodologies and development of an integrated robotic system simulation
A generic computer simulation for manipulator systems (ROBSIM) was implemented and the specific technologies necessary to increase the role of automation in various missions were developed. The specific items developed are: (1) capability for definition of a manipulator system consisting of multiple arms, load objects, and an environment; (2) capability for kinematic analysis, requirements analysis, and response simulation of manipulator motion; (3) postprocessing options such as graphic replay of simulated motion and manipulator parameter plotting; (4) investigation and simulation of various control methods including manual force/torque and active compliances control; (5) evaluation and implementation of three obstacle avoidance methods; (6) video simulation and edge detection; and (7) software simulation validation
Nonlinear robust H∞ control.
A new theory is proposed for the full-information finite and infinite horizontime
robust H∞ control that is equivalently effective for the regulation and/or tracking
problems of the general class of time-varying nonlinear systems under the presence of
exogenous disturbance inputs. The theory employs the sequence of linear-quadratic and
time-varying approximations, that were recently introduced in the optimal control
framework, to transform the nonlinear H∞ control problem into a sequence of linearquadratic
robust H∞ control problems by using well-known results from the existing
Riccati-based theory of the maturing classical linear robust control. The proposed
method, as in the optimal control case, requires solving an approximating sequence of
Riccati equations (ASRE), to find linear time-varying feedback controllers for such
disturbed nonlinear systems while employing classical methods. Under very mild
conditions of local Lipschitz continuity, these iterative sequences of solutions are
known to converge to the unique viscosity solution of the Hamilton-lacobi-Bellman
partial differential equation of the original nonlinear optimal control problem in the
weak form (Cimen, 2003); and should hold for the robust control problems herein. The
theory is analytically illustrated by directly applying it to some sophisticated nonlinear
dynamical models of practical real-world applications. Under a r -iteration sense, such
a theory gives the control engineer and designer more transparent control requirements
to be incorporated a priori to fine-tune between robustness and optimality needs. It is
believed, however, that the automatic state-regulation robust ASRE feedback control
systems and techniques provided in this thesis yield very effective control actions in
theory, in view of its computational simplicity and its validation by means of classical
numerical techniques, and can straightforwardly be implemented in practice as the
feedback controller is constrained to be linear with respect to its inputs
Environmental Sensor Placement with Convolutional Gaussian Neural Processes
Environmental sensors are crucial for monitoring weather conditions and the
impacts of climate change. However, it is challenging to maximise measurement
informativeness and place sensors efficiently, particularly in remote regions
like Antarctica. Probabilistic machine learning models can evaluate placement
informativeness by predicting the uncertainty reduction provided by a new
sensor. Gaussian process (GP) models are widely used for this purpose, but they
struggle with capturing complex non-stationary behaviour and scaling to large
datasets. This paper proposes using a convolutional Gaussian neural process
(ConvGNP) to address these issues. A ConvGNP uses neural networks to
parameterise a joint Gaussian distribution at arbitrary target locations,
enabling flexibility and scalability. Using simulated surface air temperature
anomaly over Antarctica as ground truth, the ConvGNP learns spatial and
seasonal non-stationarities, outperforming a non-stationary GP baseline. In a
simulated sensor placement experiment, the ConvGNP better predicts the
performance boost obtained from new observations than GP baselines, leading to
more informative sensor placements. We contrast our approach with physics-based
sensor placement methods and propose future work towards an operational sensor
placement recommendation system. This system could help to realise
environmental digital twins that actively direct measurement sampling to
improve the digital representation of reality.Comment: In review for the Climate Informatics 2023 special issue of
Environmental Data Scienc
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