Decentralized Merging Control in Traffic Networks with Noisy Vehicle Dynamics: a Joint Optimal Control and Barrier Function Approach

Xiao, Wei; Cassandras, Christos G.; Belta, Călin

doi:10.1109/itsc.2019.8917473

Cited by 32 publications

(42 citation statements)

References 16 publications

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“…However, we may need to approximate the minimum braking distance with this method when we have non-linear dynamics and a cooperative optimization control problem [23]. This conflict is hard to address for high-dimensional systems.…”

Section: ) Conflict Between Control Input Limitation and Hocbf Constmentioning

confidence: 99%

Control Barrier Functions for Systems with High Relative Degree

Xiao

Belta

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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216

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This paper extends control barrier functions (CBFs) to high order control barrier functions (HOCBFs) that can be used for high relative degree constraints. The proposed HOCBFs are more general than recently proposed (exponential) HOCBFs. We introduce high order barrier functions (HOBF), and show that their satisfaction of Lyapunov-like conditions implies the forward invariance of the intersection of a series of sets. We then introduce HOCBF, and show that any control input that satisfies the HOCBF constraints renders the intersection of a series of sets forward invariant. We formulate optimal control problems with constraints given by HOCBF and control Lyapunov functions (CLF) and analyze the influence of the choice of the class K functions used in the definition of the HOCBF on the size of the feasible control region. We also provide a promising method to address the conflict between HOCBF constraints and control limitations by penalizing the class K functions. We illustrate the proposed method on an adaptive cruise control problem.

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Section: ) Conflict Between Control Input Limitation and Hocbf Constmentioning

confidence: 99%

Control Barrier Functions for Systems with High Relative Degree

Xiao

Belta

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

Self Cite

216

222

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“…The only complication here is that the safe merging constraints in (4) have to be converted to continuously differentiable forms so as to be used in the CBF method. Thus, we use the same technique as in [14] to convert (4) into:…”

Section: Joint Optimal and Barrier Function Controllermentioning

confidence: 99%

Decentralized Optimal Control in Multi-lane Merging for Connected and Automated Vehicles

Xiao

Cassandras

Belta

2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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We address the problem of optimally controlling Connected and Automated Vehicles (CAVs) arriving from two multi-lane roads and merging at multiple points where the objective is to jointly minimize the travel time and energy consumption of each CAV subject to speed-dependent safety constraints, as well as speed and acceleration constraints. This problem was solved in prior work for two single-lane roads. A direct extension to multi-lane roads is limited by the computational complexity required to obtain an explicit optimal control solution. Instead, we propose a general framework that converts a multi-lane merging problem into a decentralized optimal control problem for each CAV in a less-conservative way. To accomplish this, we employ a joint optimal control and barrier function method to efficiently get an optimal control for each CAV with guaranteed satisfaction of all constraints. Simulation examples are included to compare the performance of the proposed framework to a baseline provided by humandriven vehicles with results showing significant improvements in both time and energy metrics.

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“…Barrier-based methods have shown promise in this direction since they can ensure forward invariance of a safe set without computation of reachable sets. They have been used in a variety of automotive applications such as adaptive cruise control [3], traffic control [22] and obstacle avoidance [5]. They have also seen application in robotics [9], [1], [8].…”

Section: Introductionmentioning

confidence: 99%

Risk-Bounded Control Using Stochastic Barrier Functions

Yaghoubi¹,

Majd

Fainekos

et al. 2021

IEEE Control Syst. Lett.

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In this paper, we study Stochastic Control Barrier Functions (SCBFs) to enable the design of probabilistic safe real-time controllers in presence of uncertainties and based on noisy measurements. Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value. To that end, we first estimate the system states from the noisy measurements using an Extended Kalman filter, and compute confidence intervals on the filtering errors. Then, we account for filtering errors and derive sufficient conditions on the control input based on the estimated states to bound the probability that the real states of the system enter an unsafe region within a finite time interval. We show that these sufficient conditions are linear constraints on the control input, and, hence, they can be used in tractable optimization problems to achieve safety, in addition to other properties like reachability, and stability. Our approach is evaluated using a simulation of a lane-changing scenario on a highway with dense traffic.

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Decentralized Merging Control in Traffic Networks with Noisy Vehicle Dynamics: a Joint Optimal Control and Barrier Function Approach

Cited by 32 publications

References 16 publications

Control Barrier Functions for Systems with High Relative Degree

Control Barrier Functions for Systems with High Relative Degree

Decentralized Optimal Control in Multi-lane Merging for Connected and Automated Vehicles

Risk-Bounded Control Using Stochastic Barrier Functions

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