11 research outputs found
Fast Reachable Set Approximations via State Decoupling Disturbances
With the recent surge of interest in using robotics and automation for civil
purposes, providing safety and performance guarantees has become extremely
important. In the past, differential games have been successfully used for the
analysis of safety-critical systems. In particular, the Hamilton-Jacobi (HJ)
formulation of differential games provides a flexible way to compute the
reachable set, which can characterize the set of states which lead to either
desirable or undesirable configurations, depending on the application. While HJ
reachability is applicable to many small practical systems, the curse of
dimensionality prevents the direct application of HJ reachability to many
larger systems. To address computation complexity issues, various efficient
computation methods in the literature have been developed for approximating or
exactly computing the solution to HJ partial differential equations, but only
when the system dynamics are of specific forms. In this paper, we propose a
flexible method to trade off optimality with computation complexity in HJ
reachability analysis. To achieve this, we propose to simplify system dynamics
by treating state variables as disturbances. We prove that the resulting
approximation is conservative in the desired direction, and demonstrate our
method using a four-dimensional plane model.Comment: in Proceedings of the IEE Conference on Decision and Control, 201
A Classification-based Approach for Approximate Reachability
Hamilton-Jacobi (HJ) reachability analysis has been developed over the past
decades into a widely-applicable tool for determining goal satisfaction and
safety verification in nonlinear systems. While HJ reachability can be
formulated very generally, computational complexity can be a serious impediment
for many systems of practical interest. Much prior work has been devoted to
computing approximate solutions to large reachability problems, yet many of
these methods may only apply to very restrictive problem classes, do not
generate controllers, and/or can be extremely conservative. In this paper, we
present a new method for approximating the optimal controller of the HJ
reachability problem for control-affine systems. While also a specific problem
class, many dynamical systems of interest are, or can be well approximated, by
control-affine models. We explicitly avoid storing a representation of the
reachability value function, and instead learn a controller as a sequence of
simple binary classifiers. We compare our approach to existing grid-based
methodologies in HJ reachability and demonstrate its utility on several
examples, including a physical quadrotor navigation task
Synthesis of an Intelligent UAV Control System Based on Fuzzy Logic in External Disturbance Conditions
To ensure reliable execution of flight tasks in the presence of both external perturbations and internal parametric perturbations, deterioration of the characteristics of the sensors, a control system structure based on intelligent technologies is proposed. The process of forming a “knowledge base” of a fuzzy controller is considered. The results of mathematical modeling of the longitudinal UAV control channel with a PID-controller and a fuzzy controller in the control loop are presented
Planning of Truck Platoons: a Literature Review and Directions for Future Research
A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient utilization of road capacity. To fully reap these benefits in the initial phases requires careful planning of platoons based on trucks’ itineraries and time schedules. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research