Distributed Model Predictive Control for Heterogeneous Vehicle Platoons Under Unidirectional Topologies
TL;DR: It is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions, by using the sum of local cost functions as a Lyapunov candidate.
Abstract: This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between the predicted and assumed trajectories. Together with this penalization, an equality-based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of the proposed DMPC.
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TL;DR: This paper introduces a decomposition framework to model, analyze, and design the platoon system, and the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributing sliding mode control, and distributed model predictive control.
Abstract: The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control.
304 citations
TL;DR: This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature.
Abstract: Connected and automated vehicles (CAV) are marketed for their increased safety, driving comfort, and time saving potential. With much easier access to information, increased processing power, and precision control, they also offer unprecedented opportunities for energy efficient driving. This paper is an attempt to highlight the energy saving potential of connected and automated vehicles based on first principles of motion, optimal control theory, and a review of the vast but scattered eco-driving literature. We explain that connectivity to other vehicles and infrastructure allows better anticipation of upcoming events, such as hills, curves, slow traffic, state of traffic signals, and movement of neighboring vehicles. Automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and save energy. Opportunities for cooperative driving could further increase energy efficiency of a group of vehicles by allowing them to move in a coordinated manner. Energy efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles.
297 citations
TL;DR: The architecture of various cooperative CAV systems is reviewed to answer how cooperative longitudinal motion control can work with the help of multiple system modules and what the critical design issues are.
Abstract: Connected and automated vehicles (CAVs) have the potential to address a number of safety, mobility, and sustainability issues of our current transportation systems. Cooperative longitudinal motion control is one of the key CAV technologies that allows vehicles to be driven in a cooperative manner to achieve system-wide benefits. In this paper, we provide a literature survey on the progress accomplished by researchers worldwide regarding cooperative longitudinal motion control systems of multiple CAVs. Specifically, the architecture of various cooperative CAV systems is reviewed to answer how cooperative longitudinal motion control can work with the help of multiple system modules. Next, different operational concepts of cooperative longitudinal motion control applications are reviewed to answer where they can be implemented in today's transportation systems . Different cooperative longitudinal motion control methodologies and their major characteristics are then described to answer what the critical design issues are . This paper concludes by describing an overall landscape of cooperative longitudinal motion control of CAVs, as well as pointing out opportunities and challenges in the future research and experimental implementations.
194 citations
Cites background from "Distributed Model Predictive Contro..."
...This study was further extended to the case of discrete-time vehicle dynamics and general unidirectional topology in [112]....
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TL;DR: A distributed algorithm to solve the MPCs according to the properties of the optimizers, such as solution uniqueness, sequentially feasibility, and nonempty interior point of the solution space is developed and it is proved that the distributed algorithm can solve the One-step and P-step MPCs efficiently.
Abstract: This study seeks to develop a cooperative platoon control for a platoon mixed with connected and autonomous vehicles (CAVs) and human-drive vehicles (HDVs), aiming to ensure system level traffic flow smoothness and stability as well as individual vehicles’ mobility and safety. Specifically, our study integrated/contributed the following technical approaches. First, the car-following behavior of human-drive vehicles is modeled by well-accepted Newell car-following models. Accordingly, an online curve matching algorithm is integrated to anticipate the aggregated response delay of the human-drive vehicles using real-time trajectory data. Built upon that, constrained One- or P-step MPC models are developed to control the movement of the CAV platoon upstream or downstream of the HDV platoon so that we can ensure both transient traffic smoothness and asymptotic stability of this sample mixed flow platoon, leveraging the communication and computation technologies equipped on CAVs. Considering the lack of the centralized computation facilities and severe changes of the platoon topology, this study develops a distributed algorithm to solve the MPCs according to the properties of the optimizers, such as solution uniqueness, sequentially feasibility, and nonempty interior point of the solution space. The convergence of the distributed algorithm as well as the stability of the MPC control is proved by both the theoretical analysis and the experimental study. Extensive numerical experiments based on the field data indicate that the distributed algorithm can solve the One-step and P-step MPCs efficiently. The cooperative MPC can dampen traffic oscillation propagation and stabilize the traffic flow more efficiently for the entire mixed flow platoon. Moreover, the cooperative platoon control scheme outperforms the other three control strategies, including the non-cooperative control strategy and a latest CACC strategy in literature.
182 citations
TL;DR: In this article, the authors considered the robustness analysis and distributed H-infinity controller synthesis for a platoon of connected vehicles with undirected topologies and provided an explicit scaling trend of robustness measure.
Abstract: This paper considers the robustness analysis and distributed $\mathcal {H}_{\infty }$ (H-infinity) controller synthesis for a platoon of connected vehicles with undirected topologies. We first formulate a unified model to describe the collective behavior of homogeneous platoons with external disturbances using graph theory. By exploiting the spectral decomposition of a symmetric matrix, the collective dynamics of a platoon is equivalently decomposed into a set of subsystems sharing the same size with one single vehicle. Then, we provide an explicit scaling trend of robustness measure $\gamma $ -gain, and introduce a scalable multi-step procedure to synthesize a distributed $\mathcal {H}_{\infty }$ controller for large-scale platoons. It is shown that communication topology, especially the leader’s information, exerts great influence on both robustness performance and controller synthesis. Furthermore, an intuitive optimization problem is formulated to optimize an undirected topology for a platoon system, and the upper and lower bounds of the objective are explicitly analyzed, which hints us that coordination of multiple mini-platoons is one reasonable architecture to control large-scale platoons. Numerical simulations are conducted to illustrate our findings.
162 citations
References
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TL;DR: This review focuses on model predictive control of constrained systems, both linear and nonlinear, and distill from an extensive literature essential principles that ensure stability to present a concise characterization of most of the model predictive controllers that have been proposed in the literature.
8,064 citations
"Distributed Model Predictive Contro..." refers methods in this paper
...Traditionally, MPC is used for a single-agent system, where the control input is obtained by numerically optimizing a finite horizon optimal control problem, where both nonlinearity and constraints can be explicitly handled [23]....
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TL;DR: A collection of methods for improving the speed of MPC, using online optimization, which can compute the control action on the order of 100 times faster than a method that uses a generic optimizer.
Abstract: A widely recognized shortcoming of model predictive control (MPC) is that it can usually only be used in applications with slow dynamics, where the sample time is measured in seconds or minutes. A well-known technique for implementing fast MPC is to compute the entire control law offline, in which case the online controller can be implemented as a lookup table. This method works well for systems with small state and input dimensions (say, no more than five), few constraints, and short time horizons. In this paper, we describe a collection of methods for improving the speed of MPC, using online optimization. These custom methods, which exploit the particular structure of the MPC problem, can compute the control action on the order of 100 times faster than a method that uses a generic optimizer. As an example, our method computes the control actions for a problem with 12 states, 3 controls, and horizon of 30 time steps (which entails solving a quadratic program with 450 variables and 1284 constraints) in around 5 ms, allowing MPC to be carried out at 200 Hz.
1,369 citations
"Distributed Model Predictive Contro..." refers methods in this paper
...In this aspect, several efficient computing techniques, such as utilizing particular structure [40], using explicit MPC via a lookup table [41], and reducing the dimension via the parameterization method and “move blocking” method [42], might be employed to solve each single MPC problem for real-time implementations, which would be extremely interesting for further research....
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TL;DR: A survey on the development of D2ITS is provided, discussing the functionality of its key components and some deployment issues associated with D2 ITS Future research directions for the developed system are presented.
Abstract: For the last two decades, intelligent transportation systems (ITS) have emerged as an efficient way of improving the performance of transportation systems, enhancing travel security, and providing more choices to travelers. A significant change in ITS in recent years is that much more data are collected from a variety of sources and can be processed into various forms for different stakeholders. The availability of a large amount of data can potentially lead to a revolution in ITS development, changing an ITS from a conventional technology-driven system into a more powerful multifunctional data-driven intelligent transportation system (D2ITS) : a system that is vision, multisource, and learning algorithm driven to optimize its performance. Furthermore, D2ITS is trending to become a privacy-aware people-centric more intelligent system. In this paper, we provide a survey on the development of D2ITS, discussing the functionality of its key components and some deployment issues associated with D2ITS Future research directions for the development of D2ITS is also presented.
1,336 citations
"Distributed Model Predictive Contro..." refers background in this paper
...Most of this attention is due to its potential to significantly benefit road transportation, including improving traffic efficiency, enhancing road safety, and reducing fuel consumption [1], [2]....
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...THE platooning of autonomous vehicles has received considerable attention in recent years [1]–[7]....
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TL;DR: Implementation of the CACC system, the string-stability characteristics of the practical setup, and experimental results are discussed, indicating the advantages of the design over standard adaptive-cruise-control functionality.
Abstract: The design of a cooperative adaptive cruise-control (CACC) system and its practical validation are presented. Focusing on the feasibility of implementation, a decentralized controller design with a limited communication structure is proposed (in this case, a wireless communication link with the nearest preceding vehicle only). A necessary and sufficient frequency-domain condition for string stability is derived, taking into account heterogeneous traffic, i.e., vehicles with possibly different characteristics. For a velocity-dependent intervehicle spacing policy, it is shown that the wireless communication link enables driving at small intervehicle distances, whereas string stability is guaranteed. For a constant velocity-independent intervehicle spacing, string stability cannot be guaranteed. To validate the theoretical results, experiments are performed with two CACC-equipped vehicles. Implementation of the CACC system, the string-stability characteristics of the practical setup, and experimental results are discussed, indicating the advantages of the design over standard adaptive-cruise-control functionality.
779 citations
"Distributed Model Predictive Contro..." refers background in this paper
...THE platooning of autonomous vehicles has received considerable attention in recent years [1]–[7]....
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...The main objective of the platoon control is to ensure all the vehicles in a group move at the same speed while maintaining a prespecified distance between any consecutive followers [5]–[7]....
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...Since then, many other issues on the platoon control have been discussed, such as the selection of spacing policies [6], [7], the influence of communication topology [8]–[10], and the impact of dynamic heterogeneity [11], [12]....
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TL;DR: The accomplishments to date on the development of automatic vehicle control technology in the Program on Advanced Technology for the Highway (PATH) at the University of California, Berkeley are summarized in this article.
Abstract: The accomplishments to date on the development of automatic vehicle control technology in the Program on Advanced Technology for the Highway (PATH) at the University of California, Berkeley, are summarized. The basic principles and assumptions underlying the PATH work are identified, and the work on automating vehicle lateral (steering) and longitudinal (spacing and speed) control is explained. For both lateral and longitudinal control, the modeling of plant dynamics is described, and the development of the additional subsystems needed (communications, reference/sensor systems) and the derivation of the control laws are presented. Plans for testing on vehicles in both near and long term are discussed. >
774 citations
"Distributed Model Predictive Contro..." refers background in this paper
...2594588 The earliest practices on the platoon control could date back to the PATH program in the 1980s, in which many well-known topics were introduced in terms of sensors and actuators, control architecture, decentralized control, and string stability [5]....
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...The main objective of the platoon control is to ensure all the vehicles in a group move at the same speed while maintaining a prespecified distance between any consecutive followers [5]–[7]....
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