3 research outputs found
Data-Driven Convex Approach to Off-road Navigation via Linear Transfer Operators
We consider the problem of optimal navigation control design for navigation
on off-road terrain. We use traversability measure to characterize the degree
of difficulty of navigation on the off-road terrain. The traversability measure
captures the property of terrain essential for navigation, such as elevation
map, terrain roughness, slope, and terrain texture. The terrain with the
presence or absence of obstacles becomes a particular case of the proposed
traversability measure. We provide a convex formulation to the off-road
navigation problem by lifting the problem to the density space using the linear
Perron-Frobenius (P-F) operator. The convex formulation leads to an
infinite-dimensional optimal navigation problem for control synthesis. The
finite-dimensional approximation of the infinite-dimensional convex problem is
constructed using data. We use a computational framework involving the Koopman
operator and the duality between the Koopman and P-F operator for the
data-driven approximation. This makes our proposed approach data-driven and can
be applied in cases where an explicit system model is unavailable. Finally, we
demonstrate the application of the developed framework for the navigation of
vehicle dynamics with Dubin's car model
Data-driven optimal control under safety constraints using sparse Koopman approximation
In this work we approach the dual optimal reach-safe control problem using
sparse approximations of Koopman operator. Matrix approximation of Koopman
operator needs to solve a least-squares (LS) problem in the lifted function
space, which is computationally intractable for fine discretizations and high
dimensions. The state transitional physical meaning of the Koopman operator
leads to a sparse LS problem in this space. Leveraging this sparsity, we
propose an efficient method to solve the sparse LS problem where we reduce the
problem dimension dramatically by formulating the problem using only the
non-zero elements in the approximation matrix with known sparsity pattern. The
obtained matrix approximation of the operators is then used in a dual optimal
reach-safe problem formulation where a linear program with sparse linear
constraints naturally appears. We validate our proposed method on various
dynamical systems and show that the computation time for operator approximation
is greatly reduced with high precision in the solutions
Vehicle-to-Grid Integration for Enhancement of Grid: A Distributed Resource Allocation Approach
In the future grids, to reduce greenhouse gas emissions Electric Vehicles (EVs) seems to be an important means of transportation. One of the major disadvantages of the future grid is the demand-supply mismatch which can be mitigated by incorporating the EVs into the grid. The paper introduces the concept of the Distributed Resource Allocation (DRA) approach for incorporating a large number of Plug-in EV (PEVs) with the power grid utilizing the concept of achieving output consensus. The charging/discharging time of all the participating PEVs are separated with respect to time slots and are considered as strategies. The major aim of the paper is to obtain a favorable charging strategy for each grid-connected PEVs in such a way that it satisfies both grid objectives in terms of load profile smoothening and minimizing of load shifting as well as economic and social interests of vehicle owners i.e. a fair share of the rate of charging for all connected PEVs. The three-fold contribution of the paper in smoothening of load profile, load shifting minimization, and fair charging rate is validated using a representative case study. The results confirm improvement in load profile and also highlight a fair deal in the charging rate for each PEV