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Full-Text Articles in Engineering

Tech Report: Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam Mar 2016

Tech Report: Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its ...


Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam Jan 2016

Robust Model Predictive Control For Non-Linear Systems With Input And State Constraints Via Feedback Linearization, Yash Vardhan Pant, Houssam Abbas, Rahul Mangharam

Real-Time and Embedded Systems Lab (mLAB)

Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constraints. In this paper, we solve this problem of robust control of a non-linear system under bounded state estimation errors and input and state constraints using feedback linearization. We do so by developing robust constraints on the feedback linearized system such that the non-linear system respects its ...


Representation Of Confidence In Assurance Cases Using The Beta Distribution, Lian Duan, Sanjai Rayadurgam, Mats Heimdahl, Oleg Sokolsky, Insup Lee Jan 2016

Representation Of Confidence In Assurance Cases Using The Beta Distribution, Lian Duan, Sanjai Rayadurgam, Mats Heimdahl, Oleg Sokolsky, Insup Lee

Departmental Papers (CIS)

Assurance cases are used to document an argument that a system—such as a critical software system—satisfies some desirable property (e.g., safety, security, or reliability). Demonstrating high confidence that the claims made based on an assurance case can be trusted is crucial to the success of the case. Researchers have proposed quantification of confidence as a Baconian probability ratio of eliminated concerns about the assurance case to the total number of identified concerns. In this paper, we extend their work by mapping this discrete ratio to a continuous probability distribution—a beta distribution— enabling different visualizations of the ...