Information-Constrained Model Predictive Control with Application to Vehicle Platooning

Causevic, V.; Fanger, Yunis; Brüdigam, Tim; Hirche, Sandra

doi:10.1016/j.ifacol.2020.12.1047

Cited by 3 publications

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is a large body of work focusing on SMPC for autonomous driving applications, e.g., autonomous lane change [6], cruise control [7], and platooning [8]. A typical SMPC algorithm involves solving a chanceconstrained finite horizon optimal control problem in a 1 E.g., features for a longitudinal control driver model may consist of safety distance from its lead car and/or the distance to a stop sign.…”

Section: Related Workmentioning

confidence: 99%

Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions

Nair¹,

Govindarajan²,

Lin³

et al. 2022

Preprint

View full text Add to dashboard Cite

We propose a Stochastic MPC (SMPC) approach for autonomous driving which incorporates multimodal, interaction-aware predictions of surrounding vehicles. For each mode, vehicle motion predictions are obtained by a control model described using a basis of fixed features with unknown weights. The proposed SMPC formulation finds optimal controls which serves two purposes: 1) reducing conservatism of the SMPC by optimizing over parameterized control laws and 2) prediction and estimation of feature weights used in interaction-aware modeling using Kalman filtering. The proposed approach is demonstrated on a longitudinal control example, with uncertainties in predictions of the autonomous and surrounding vehicles.

show abstract

Section: Related Workmentioning

confidence: 99%

Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions

Nair¹,

Govindarajan²,

Lin³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…This (later proven), together with assumptions on one-step delay between neigboring decision makers, implies the optimality of linear control policies for the information-constrained problem addressed here. An interesting example [20] that illustrates the role of state power constraints is a vehicle platoon that suddenly increases its velocity due to an increased speed limit on the road. As the target speed of the platoon increases, it is crucial to limit the deviation of distances from desired ones (as the failure of one vehicle can have bigger consequences when the platoon is moving at higher velocity).…”

Section: Problem Settingmentioning

confidence: 99%

“…The physical interconnections between subsystems are given via an undirected line graph (as shown in Figure 2). An example of system whose dynamics is described via a line graph is a vehicle platoon [20]. Referring to (2), we define the following global dynamics matrices:…”

Section: Simulationsmentioning

confidence: 99%

“…The physical interconnections between subsystems are given via an undirected line graph (as shown in Figure 2). An example of system whose dynamics is described via a line graph is a vehicle platoon [20]. Referring to (), we define the following global dynamics matrices:

\begin{matrix} A = ([], \begin{matrix} center center center center \\ array 1 & array 10 & array 0 & array 0 \\ array 1 & array 0.1 & array 1 & array 0 \\ array 0 & array 0.1 & array 1 & array 1 \\ array 0 & array 0 & array 0.1 & array 1 \end{matrix}), B = ([], \begin{matrix} center center center center \\ array 1 & array 0 & array 0 & array 0 \\ array 0 & array 1 & array 0 & array 0 \\ array 0 & array 0 & array 1 & array 0 \\ array 0 & array 0 & array 0 & array 1 \end{matrix}) . \end{matrix}

The initial state is drawn from zero‐mean distribution with covariance

{normalΣ}_{x} = 1 0^{- 2} \times 𝕀^{4 x 4}

, and the system noise is drawn from the zero‐mean distribution with covariance

25 \times 1 0^{- 4} \times 𝕀^{4 x 4}

, where

𝕀^{4 x 4}

denotes identity matrix with corresponding dimensions.…”

Section: Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

Optimal power‐constrained control of distributed systems with information constraints

Causevic

Abara

Hirche

2021

Asian Journal of Control

View full text Add to dashboard Cite

This paper is concerned with a special case of stochastic distributed optimal control, where the objective is to design a structurally constrained controller for a system subject to state and input power constraints. The structural constraints are induced by the directed communication between local controllers over a strongly connected graph. Based on the information structure present, that is, who knows what and when, we provide a control synthesis with the optimal control law consisting of two parts: one that is based on the common information between the subsystems and one that uses more localized information. The developed method is applicable to an arbitrary number of physically interconnected subsystems.

show abstract

Stochastic MPC with Multi-modal Predictions for Traffic Intersections

Nair

Govindarajan

Lin

et al. 2022

2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

View full text Add to dashboard Cite

Information-Constrained Model Predictive Control with Application to Vehicle Platooning

Cited by 3 publications

References 11 publications

Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions

Stochastic MPC with Dual Control for Autonomous Driving with Multi-Modal Interaction-Aware Predictions

Optimal power‐constrained control of distributed systems with information constraints

Stochastic MPC with Multi-modal Predictions for Traffic Intersections

Contact Info

Product

Resources

About