75 research outputs found
An Optimal Coordination Framework for Connected and Automated Vehicles in two Interconnected Intersections
In this paper, we provide a decentralized optimal control framework for
coordinating connected and automated vehicles (CAVs) in two interconnected
intersections. We formulate a control problem and provide a solution that can
be implemented in real time. The solution yields the optimal
acceleration/deceleration of each CAV under the safety constraint at "conflict
zones," where there is a chance of potential collision. Our objective is to
minimize travel time for each CAV. If no such solution exists, then each CAV
solves an energy-optimal control problem. We evaluate the effectiveness of the
efficiency of the proposed framework through simulation.Comment: 8 pages, 5 figures, IEEE CONFERENCE ON CONTROL TECHNOLOGY AND
APPLICATIONS 201
Beyond Reynolds: A Constraint-Driven Approach to Cluster Flocking
In this paper, we present an original set of flocking rules using an
ecologically-inspired paradigm for control of multi-robot systems. We translate
these rules into a constraint-driven optimal control problem where the agents
minimize energy consumption subject to safety and task constraints. We prove
several properties about the feasible space of the optimal control problem and
show that velocity consensus is an optimal solution. We also motivate the
inclusion of slack variables in constraint-driven problems when the global
state is only partially observable by each agent. Finally, we analyze the case
where the communication topology is fixed and connected, and prove that our
proposed flocking rules achieve velocity consensus.Comment: 6 page
On Team Decision Problems with Nonclassical Information Structures
In this paper, we consider sequential dynamic team decision problems with
nonclassical information structures. First, we address the problem from the
point of view of a "manager" who seeks to derive the optimal strategy of the
team in a centralized process. We derive structural results that yield an
information state for the team which does not depend on the control strategy,
and thus it can lead to a dynamic programming decomposition where the
optimization problem is over the space of the team's decisions. We, then,
derive structural results for each team member that yield an information state
which does not depend on their control strategy, and thus it can lead to a
dynamic programming decomposition where the optimization problem for each team
member is over the space of their decisions. Finally, we show that the control
strategy of each team member is the same as the one derived by the manager. We
present an illustrative example of a dynamic team with a delayed sharing
information structure.Comment: 16 page
Combining Learning and Control in Linear Systems
In this paper, we provide a theoretical framework that separates the control
and learning tasks in a linear system. This separation allows us to combine
offline model-based control with online learning approaches and thus circumvent
current challenges in deriving optimal control strategies in applications where
a large volume of data is added to the system gradually in real time and not
altogether in advance. We provide an analytical example to illustrate the
framework.Comment: 6 pages, 1 figure. arXiv admin note: text overlap with
arXiv:2211.1497
On Separation Between Learning and Control in Partially Observed Markov Decision Processes
Cyber-physical systems (CPS) encounter a large volume of data which is added
to the system gradually in real time and not altogether in advance. As the
volume of data increases, the domain of the control strategies also increases,
and thus it becomes challenging to search for an optimal strategy. Even if an
optimal control strategy is found, implementing such strategies with increasing
domains is burdensome. To derive an optimal control strategy in CPS, we
typically assume an ideal model of the system. Such model-based control
approaches cannot effectively facilitate optimal solutions with performance
guarantees due to the discrepancy between the model and the actual CPS.
Alternatively, traditional supervised learning approaches cannot always
facilitate robust solutions using data derived offline. Similarly, applying
reinforcement learning approaches directly to the actual CPS might impose
significant implications on safety and robust operation of the system. The goal
of this chapter is to provide a theoretical framework that aims at separating
the control and learning tasks which allows us to combine offline model-based
control with online learning approaches, and thus circumvent the challenges in
deriving optimal control strategies for CPS.Comment: 18 pages, 5 figures. arXiv admin note: text overlap with
arXiv:2101.1099
Conditions for State and Control Constraint Activation in Coordination of Connected and Automated Vehicles
Connected and automated vehicles (CAVs) provide the most intriguing
opportunity to reduce pollution, energy consumption, and travel delays. In
earlier work, we addressed the optimal coordination of CAVs using Hamiltonian
analysis. In this paper, we investigate the nature of the unconstrained problem
and provide conditions under which the state and control constraints become
active. We derive a closed-form analytical solution of the constrained
optimization problem and evaluate the solution using numerical simulation
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