A Model Predictive Control scheme for freeway traffic systems based on the Classification And Regression Trees methodology

Oleari, Alberto Nai; Frejo, José Ramón D.; Camacho, Eduardo F.; Ferrara, Antonella

doi:10.1109/ecc.2015.7331069

Cited by 9 publications

(3 citation statements)

References 21 publications

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“…Over the years, a multitude of traffic control systems have been proposed (see [9], [25]). Those models can be classified on the basis of their level of abstraction (macroscopic, as in [17], [18], microscopic as in [6], [7] or mesoscopic, a microscopic model that takes into account macroscopic parameters) or the adopted control strategy (centralized, as in [3], or decentralized as in [4], [2]) and have been utilized with different methodologies for managing a large variety of problems ( [24], [26], [39], [1], [21], [22]).…”

Section: Introductionmentioning

confidence: 99%

Safe human-inspired mesoscopic hybrid automaton for autonomous vehicles

Iovine

Valentini

Santis

et al. 2017

Nonlinear Analysis: Hybrid Systems

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In this paper a mesoscopic hybrid model, i.e. a microscopic hybrid model that takes into account macroscopic parameters, is introduced for designing a humaninspired Adaptive Cruise Control. A control law is proposed with the design goal of replacing and imitating the behaviour of a human driver in a car-following situation where lane changes are possible. First, a microscopic hybrid automaton model is presented, based on human psycho-physical behavior, for both longitudinal and lateral vehicle control. Then a rule for changing time headway on the basis of macroscopic quantities is used to describe the interaction among next vehicles and their impact on driver performance. Simulation results show the advantages of the mesoscopic model. A feasibility analysis of the needed communication network is also presented.

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Section: Introductionmentioning

confidence: 99%

Safe human-inspired mesoscopic hybrid automaton for autonomous vehicles

Iovine

Valentini

Santis

et al. 2017

Nonlinear Analysis: Hybrid Systems

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show abstract

“…As for the implementation point of view, one could enhance the computational aspects related to the MPC components of the control scheme, by using methods which are particularly suitable for the on-line implementation in traffic systems (e.g., fast MPC [58], plug-and-play MPC [59], or regression-tree based MPC [60]). Finally, it is certainly worth studying how a multi-level approach of the type proposed in this paper can be combined with the more traditional traffic control methods based on ramp metering, variable speed limits and routing, giving rise to a coordinated traffic control system where CAVs suitably cooperate with more conventional control actuators.…”

Section: Limitations and Extensionsmentioning

confidence: 99%

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

Ferrara

Incremona

Birliba

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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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.

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“…The resulting models can then be used for explicit MPC, as in [4]. This approach has been applied to problems of stabilization [6] and freeway traffic systems using regression trees [21]. Another class of methods solve nonlinear optimization directly on the trained models to do receding horizon control.…”

Section: Related Workmentioning

confidence: 99%

Data Predictive Control for Peak Power Reduction

Jain

Mangharam

Behl

2016

Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments

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Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making controloriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/ grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs. mb2kg@virginia.edu ABSTRACTDecisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs.

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A Model Predictive Control scheme for freeway traffic systems based on the Classification And Regression Trees methodology

Cited by 9 publications

References 21 publications

Safe human-inspired mesoscopic hybrid automaton for autonomous vehicles

Safe human-inspired mesoscopic hybrid automaton for autonomous vehicles

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

Data Predictive Control for Peak Power Reduction

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