Robustified Distributed Model Predictive Control for Coherence and Energy Efficiency-Aware Platooning

IEEE Control Syst. Lett.

2021

In this paper a coupled PDE-ODE model describing the interaction between the bulk traffic flow and a platoon of connected vehicles is adopted to develop a control action aiming at reducing the fuel consumption of the overall traffic flow. The platoon is modeled as a capacity restriction acting on the surrounding traffic. The trajectory of the initial and final points of the platoon are optimized by means of a model predictive control strategy, acting on the speeds of the front-end and backend of the platoon, thus resulting in controlling both the speed and the length of the platoon. The approach is assessed in simulations.

Section: Introductionmentioning

confidence: 99%

Traffic Control Via Platoons of Intelligent Vehicles for Saving Fuel Consumption in Freeway Systems

Piacentini

Goatin

IEEE Control Syst. Lett.

2021

“…Further, just because of the unavoidable inaccuracy in the CAV model, it is advisable that robust control approaches are adopted to control the vehicles forming the platoons. In [37], for instance, a hierarchical MPC with sliding mode control (SMC) has been proposed to keep a safe distance between the vehicles of the platoon in spite of the modelling uncertainties affecting the vehicles description. A genuine sliding mode platoon control has been instead proposed in [38].…”

Section: A Brief Overview Of Traffic Controlmentioning

confidence: 99%

“…Inspired by [29] and [37], in this paper a novel hierarchical multi-level control scheme is proposed to reduce traffic congestion and fuel consumption in freeway traffic systems. The proposal consists of four key elements: a high-level MPC relying on the nominal model of the freeway traffic with a platoon of electric CAVs; an event-triggered logic to enable the high-level optimization only when it is actually needed; medium-level distributed MPCs, one for each CAV in the platoon, in order to track the reference values provided by the high-level control in an energy efficient way; finally, low-level integral sliding mode controllers, acting locally at the level of any single CAV, to enhance the control robustness.…”

Section: B Contributionsmentioning

confidence: 99%

Multi-Scale Model-Based Hierarchical Control of Freeway Traffic via Platoons of Connected and Automated Vehicles

IEEE Open J. Intell. Transp. Syst.

Incremona

Birliba

et al. 2022

In this paper a novel hierarchical multi-level control scheme is proposed for freeway traffic systems. Relying on a coupled PDE-ODE nominal model, capturing the interaction between the macroscopic traffic flow and a platoon of connected and automated electric vehicles (CAVs) which acts as a moving bottleneck, a high-level model predictive controller (MPC) is adopted to reduce traffic congestion and vehicle fuel consumption. This controller generates, only when necessary, i.e., according to an eventtriggered control logic, the most appropriate reference values for the platoon length and velocity. The platoon is in turn controlled, in an energy efficient way, by a distributed medium-level MPC, so as to track the reference speed values for its downstream and upstream end-points provided by the high-level MPC. The mismatch between the dynamics of the CAVs forming the platoon and their nominal dynamics is tackled via the design of local low-level robust integral sliding mode controllers, which have the capability of compensating for the mismatch. In the paper, the controlled platoon of CAVs is assumed to be immersed into a realistic traffic system with traffic demand not known in advance, which differs from the nominal prediction model used by the high-level MPC.INDEX TERMS Electric vehicles, event-triggered control, optimal control, platoon control, sliding mode control, traffic control.

“…The control goal is to make the vehicle follow the speed reference v ⋆ generated by the planner in spite of uncertainties injected in the vehicle dynamics in order to simulate possible mismatches due to modelling approximations or external forces from actuators. More precisely, the considered disturbance is a shifted low-frequency sinusoid with a random noise ϵ(t) superimposed (see e.g., [18]), i.e.,…”

Section: A Settingsmentioning

confidence: 99%

“…Let v(0) = 13.5 m s −1 be the initial velocity condition. The thresholds for the ET block are chosen as ε v = 0.2 m s −1 for condition T 1 in ( 16), ε τ = 2 s for condition T 2 in (17), and ε p = 10 m for condition T 3 in (18). As for the SMC, the control gain is selected as K = 60.…”

Section: A Settingsmentioning

confidence: 99%

Event-Triggered Eco-Driving With Sliding Mode Control for an Electric Vehicle in Urban Traffic Networks

Incremona

2022 European Control Conference (ECC)

2022

Self Cite

This paper deals with an eco-driving control problem for autonomous electric vehicles in urban traffic networks with signalized intersections. Specifically, a novel control scheme is proposed to make the vehicle travel through a sequence of signalized intersections while always catching green lights with minimum energy consumption. The proposal includes a speed reference generator, a sliding mode local controller and an event-triggered decision maker whose intelligence is provided by a pre-specified condition. This mechanism enables to determine when it is necessary to re-plan a new optimal velocity profile by the speed planner, which solves a sub-optimal version of the non-convex eco-driving optimal control problem due to the traffic lights constraints. The sliding mode controller instead enables a finite time tracking of the optimized speed reference and plays the role of compensator of the uncertainties affecting the vehicle dynamics. Such a robustness property in turn allows to limit the triggering events when a new optimization is solved to update the speed reference profile. The performance of the whole control scheme are finally assessed in simulation.