Distributed Model Predictive Control Research Articles

Distributed state estimation and fault diagnosis using reduced sensitivity to neighbor estimates with application to building control

This paper proposes solutions that reduce the inaccuracy of distributed state estimation and consequent performance deterioration of distributed model predictive control caused by faults and inaccurate models. A distributed state estimation method for large-scale systems is introduced. A local state estimation approach considers the uncertainty of neighbor estimates, which can improve the state estimation accuracy, whereas it keeps a low network communication burden. The method also incorporates the uncertainty of model parameters which improves the performance when using simplified models. The proposed method is extended with multiple models and estimates the probability of nominal and fault behavior models, which creates a distributed fault detection and diagnosis method. An example with application to the building heating control demonstrates that the proposed algorithm provides accurate state estimates to a controller and detects local or global faults while using simplified models.

Robust self-triggered DMPC for linear discrete-time systems with local and global constraints

In this paper, a novel robust distributed model predictive control with a self-triggered mechanism is proposed for a family of discrete-time linear systems with additive disturbances, local and global constraints. To handle the additive disturbances, a tube method is applied to achieve the robust satisfaction of local constraints. Meanwhile, a sequential constraints tightening method is proposed to guarantee the satisfaction of global coupled constraints. The optimization problem is constructed as a consensus problem of an augmented Lagrange function and is solved through a modified distributed alternative direction multiplier method. Furthermore, a self-triggered mechanism is adopted to help reduce the computation burden and accelerate system stabilization by skipping insignificant iteration steps in parallel ways. By clarifying the upper bound on tolerable bounded disturbances, two sufficient conditions about recursive feasibility of optimization problem and input-to-state stability of the closed-loop system are given, respectively. Finally, performances of the proposed scheme are illustrated by simulation examples.

Guest editorial: Decision making and control for connected and automated vehicles

Guest editorial: Decision making and control for connected and automated vehicles

Cooperative braking of urban rail vehicles with Koopman model predictive control

AbstractBraking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%.

Real‐time active power dispatch of virtual power plant based on distributed model predictive control

With the increasing penetration of renewable energy, virtual power plants (VPP) reduce the impact on the power grid by integrating massive distributed resources for unified management. However, the optimal scheduling of a large number of distributed resources in VPP has become a new problem in recent years. Therefore, aiming at the real-time optimal scheduling problem in the optimal scheduling of virtual power plant, this letter regards the virtual power plant as a multi-agent system and proposes a novel real-time active power dispatch scheme of virtual power plant based on distributed model predictive control (DMPC), so that each agent can not only calculate its own optimization function relatively independently, but also fully refer to the neighbour information. Simulation results show the feasibility and effectiveness of the proposed method.

Distributed model predictive control strategy based on block‐wised alternating direction multiplier method for microgird clusters

Microgrid clusters (MGC) can improve the consumption of renewable energy and the system reliability. The control of microgrid cluster with large-scale microgrids is the focus of microgrid cluster research. In order to better realize the coordination control and energy scheduling among microgrids, this paper proposes a distributed optimal strategy for complex MGC based on model predictive control (MPC) and block-wised alternating direction multiplier method (BADMM). Firstly, an optimization model is established based on a honeycomb active distribution network (HADN); then the network is decomposed into several regions according to BADMM. The global optimization goal of the cluster is achieved by iterating the optimization results of each region in a time domain. Finally, the case study results verify the effectiveness and convergence of the proposed distributed optimization strategy, and provide a reference for energy management of MGC.

Distributed Model Predictive Control for Two-Dimensional Electric Vehicle Platoon Based on QMIX Algorithm

In this paper, an improved distributed model predictive control (IDMPC) method for the platoon consisting of electric vehicles is put forward. And the motion of the platoon is performed in two dimensions, which contains longitudinal motion and lateral motion. Firstly, a platoon model is built based on the car-following model for a single following vehicle. Then, the IDMPC strategy is designed with the consideration of multiple objectives. The symmetrical weight matrices in the IDMPC are important for the final control effect. To control each following vehicle in the platoon coordinately, the weights for the IDMPC are optimized based on the QMIX algorithm in multi-agent reinforcement learning. The QMIX can fully consider the global information in the multi-vehicle following process; therefore, the IDMPC can get optimal control variables. Finally, the simulation and experimental results verify the IDMPC. Compared to the comparison strategies, the IDMPC has the better lane tracking, stability in lateral direction and economic performance.

Robust distributed model predictive control for satellite cluster reconfiguration with collision avoidance

This article investigates a reconfiguration problem of the satellite cluster with collision avoidance. Focusing on the uncertain disturbances with upper bound, a tube-based auxiliary controller is designed, which can improve the robustness of actual satellite cluster system. Then, a distributed model predictive controller (DMPC) is proposed for the nominal system with thrust constraints. In DMPC, the terminal constraints and terminal control law based on velocity obstacle are designed to satisfy the collision avoidance. Furthermore, the stability and feasibility can be ensured by using optimized slack variable. In addition, to reduce fuel consumption and the actions of collision, an optimal allocation strategy for target points based on Hungarian algorithm is adopted. Finally, the numerical simulations are presented to verify the effectiveness and advantage of the proposed scheme.

Self-Triggered Min–Max DMPC for Asynchronous Multiagent Systems With Communication Delays

This article studies the formation stabilization problem of asynchronous nonlinear multiagent systems (MAS) subject to parametric uncertainties, external disturbances, and bounded time-varying communication delays. A self-triggered min–max distributed model predictive control (DMPC) approach is proposed to address this problem. At triggering instants, each agent solves a local min–max optimization problem based on local system states and predicted states of neighbors, determines its next triggering instant, and broadcasts its predicted state trajectory to the neighbors. As a result, the communication load is greatly alleviated while retaining robustness and comparable control performance compared to periodic DMPC algorithms. In order to handle time-varying delays, a novel consistency constraint is incorporated into each local optimization problem to restrict the deviation between the newest predicted states and previously broadcasted predicted states. Consequently, each agent can utilize previously predicted states of its neighbors to achieve cooperation in the presence of the asynchronous communication and time-varying delays. The proposed algorithm’s recursive feasibility and MAS’s closed-loop stability at triggering instants are proven. Finally, numerical simulations are conducted to verify the theoretical results.

Distributed Model Predictive Control for Vehicle Platoon With Mixed Disturbances and Model Uncertainties

To successfully implement the platoon control of connected and automated vehicles (CAVs), the model uncertainties, external disturbances, and time delays must be addressed. In this study, we propose a distributed Model Predictive Control scheme to achieve offset-free tracking for CAVs with model uncertainties and mixed disturbances which contains both stochastic and deterministic noises. A simple Vehicle-to-Vehicle communication flow with the short-distance and low-volume information exchange is adopted to reduce the effect of the communication delay and dropout. First, the deterministic and stochastic perturbances are addressed separately in constraints handling with an integrated control law based on the mismatched prediction model. Additional constraints on coupling input and output are imposed to guarantee the assumption about forecast trajectory. Then an observer gives an unbiased estimate of the state and integrated disturbance. Based on the estimation, the target calculator eliminates the steady tracking offset by designing a new target in real-time and the Min-Max Model Predictive Control steers the uncertain systems to the new target under the worst case within the domain of disturbances. Lastly, the Optimal Control Problem is reformulated and can be efficiently solved by existing solvers. Simulation results indicate that the proposed method shows superiority in interference rejection and offset-free tracking.

Multiobjective Platooning of Connected and Automated Vehicles Using Distributed Economic Model Predictive Control

This paper considers the multi-objective platooning control problem of a group of connected and automated vehicles (CAVs) with bidirectional topologies. A new distributed economic model predictive control (DEMPC) algorithm is presented to reconcile the conflict of the control objectives of tracking, safety, stability and fuel economy of the heterogeneous vehicle platoon with guaranteed string stability. Using transient engine energy efficiency indices and cooperative performance of tracking and string stability, two distributed receding horizon optimal control problems are orderly formulated by a Lyapunov-based coupling constraint. Moreover, a new concept of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> -string stability is defined for the platoon with bidirectional topologies. Some distributed terminal conditions are then derived to guarantee the recursive feasibility and asymptotic stability of the DEMPC as well as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> -string stability of the platoon in the presence of constraints. Compared to traditional platooning control, the new DEMPC has a 4.2% energy-saving of vehicles while achieving the cooperative tasks of the platoon in several simulation scenarios.

Fully Distributed Model Predictive Control of Connected Automated Vehicles in Intersections: Theory and Vehicle Experiments

We propose a fully distributed control system architecture, amenable to in-vehicle implementation, that aims to safely coordinate connected and automated vehicles (CAVs) at road intersections. For control purposes, we build upon a fully distributed model predictive control approach, in which the agents solve a nonconvex optimal control problem (OCP) locally and synchronously, and exchange their optimized trajectories via vehicle-to-vehicle (V2V) communication. To accommodate a fast solution of the nonconvex OCPs, we apply the penalty convex-concave procedure which solves a convexified version of the original OCP. For experimental evaluation, we complement the predictive controller with a localization layer, being in charge of self-localization, and an estimator, which determines joint collision points with other agents. Experimental tests reveal the efficacy of the proposed control system architecture.

UAV Formation Control under Communication Constraints Based on Distributed Model Predictive Control

A distributed model predictive control method is used to transform the formation and maintenance problem of fixed-wing UAV formation during flight into an online rolling optimization problem to solve in this paper. Firstly, the state estimation model of the neighborhood UAV is established according to the relative information of the UAV. Secondly, the error state model in the three-dimensional inertial coordinate frame of the UAV is established without considering the time delay, sensor error, and external interference. Thirdly, a cost function is designed by introducing the error state of the UAV in the neighborhood. Finally, four UAVs are used to verify that under the action of the controller, the UAVs can quickly form and maintain the desired formation while tracking the reference line.

A distributed model predictive control method combined with delay compensator for multiple vehicle platoons

With an increasing number of vehicles on the road, multiple-vehicle platoons have become a new solution under communication constraints. However, the existing studies seldom consider the effect of the tail vehicle on the information transfer and platoon control over long distances, and thus the control performance under time-varying delays is unacceptable. In this study, a hierarchical multi-platoon control architecture considering the backbone platoon and sub-platoons is proposed under non-ideal communication conditions, where the backbone platoon layer communication topology is designed to achieve backbone platoon control and appropriate coordination among sub-platoons. Distributed model predictive controllers (DMPCs) for backbone- and sub-platoons that integrate delay compensation have been proposed. Buffers and delay compensators in the proposed DMPC are designed to reduce the interference caused by non-ideal communications. The asymptotic stability is analyzed at the sub- and multi-platoon levels. The results show that compared with traditional DMPC without a compensator, the proposed DMPC can eliminate the velocity lag and steady-state spacing errors and maintain the maximum velocity and spacing errors within 0.11 m/s and 0.17 m, respectively, under certain operating conditions. Thus, the proposed method guarantees the stability of multiple platoons under both the constant and time-varying delay conditions.

Enhancing topological information of the Lyapunov‐based distributed model predictive control design for large‐scale nonlinear systems

SummaryIn this paper, a distributed model predictive control architecture based on graph theory for integrated large‐scale nonlinear process systems is proposed. This architecture is first agglomerated using popular community detection techniques and then organized on account of the relative master–slave relationship according to the ample information of interactions among separate subsystems. Both sequential and iterative distributed nonlinear model predictive coordination forms are considered for the reduction of the communication and computational burden within an acceptable loss of performance. Furthermore, the control performance of the large‐scale integrated system could be improved to some extent under the architecture and the communication strategy we propose, whereby a brave exploration is made on the relationship between the control structure and the control performance, ulteriorly obtaining some notable results towards the untapped territory. The effectiveness of the proposed coordination method is evaluated by a standard reactor‐separator process system.

Distributed optimal energy scheduling for grid connected multi-microgrids with architecturized load characteristics

This paper proposes load characteristics-based multiple microgrids (Ch-MMGs) architecture and utilizes distributed model predictive control (DMPC) based energy management for its day-ahead scheduling in grid-connected mode. Different from existing work, which consider MMG with composite load architecture, the MMG architecture is re-organized by categorizing consumer’s load into different groups according to their operational characteristics. By doing so, each characteristics microgrid (Ch-MG) would have specific load pattern, which can significantly improve power utilization more economically and optimally. Demand Response Program (DRP) is incorporated for reducing the peak load demand of categorized groups in accordance to Time-of-Use (ToU) prices. In order to meet the supply and demand of each Ch-MG more economically, a DMPC based two level optimal energy scheduling scheme has been developed, where distributed network operator (DNO) acts as coordinating entity at upper level. It facilitates economic energy exchange among Ch-MMGs through providing optimal reference signal to Ch-MG at lower level. The Ch-MG performs local optimization in according to reference signal from upper level DNO. The uncertainty in renewable power generation are handled with probabilistic scenarios using Monte-Carlo simulation. Comparative case studies are conducted on conventional and proposed MMG architecture. The results indicate that the operation of individual MGs with proposed scheme can effectively reduce system operation cost and achieve system-wide and local supply–demand balance more economically.

A Platoon Control Method Based on DMPC for Connected Energy-Saving Electric Vehicles

This article presents a distributed model predictive control (DMPC) algorithm with energy management for the electric vehicle platoon. In addition, a braking force distribution strategy is proposed for the following vehicles (FVs) in the platoon, and the optimal energy recovery braking force distribution is adopted within the limits permitted by braking regulations. The cost function of DMPC includes the following aspects: drive/brake power deviation, vehicle distance deviation, trajectory deviation, and energy consumption. Through the simulation analysis, it is demonstrated that the proposed algorithm has satisfactory speed and trajectory tracking performance, platoon stability, and driving comfort. Two platoon control methods [DMPC without energy management, proportion integration differentiation (PID)] and two single vehicle control methods (single vehicle with or without recovery braking) are designed to verify the proposed method. A semiphysical experimental platform is established to verify the authenticity, accuracy, and effectiveness of the model and the proposed control methods.

A distributed model predictive control with machine learning for automated shot peening machine in remanufacturing processes

In practical peening operation, the values of inlet air pressure and media flow rate are manually preset to acquire desired intensity requirements. The operator often needs to perform intensive experimental trials to determine a set of operational inputs for actual production. Obtaining these operational parameters is often time-consuming and labor-intensive. Thus, in this study, we propose an optimal distributed model predictive control for the multiple input/multiple output system to address the issues. In the newly developed system, control actions of inlet air pressure and voltage are optimally obtained with the anticipation of the predictive future states of the plant models, while reference values of air pressure at the nozzle and media flowrate are determined using a proxy model. The dynamical plant models include an air pressure model and a media flowrate model, which are developed based on measurement data and physics-based knowledge using the sparse identification of nonlinear dynamics algorithm. The proxy model is developed from the measurement data of the intensity, pressure, and media flowrate using a deep machine-learning algorithm. The control performance is demonstrated using on-site controls at the physical machine for different operational scenarios. The obtained measurement results exhibit a favorable control performance in stability, robustness, and accuracy. The measurement intensity is consistent with the target setting value; the difference is smaller than the industrial threshold of ± 0.01mmA for all random tests. In another word, all target setting intensity can be achieved without the need of performing trials to determine the operational parameters. It also suggests that the developed control system can be deployed to the physical machine for actual production.

A Swarm-Based Distributed Model Predictive Control Scheme for Autonomous Vehicle Formations in Uncertain Environments.

In this article, a novel distributed model predictive control architecture is proposed for the coordination and control of multivehicle formations moving within uncertain environments. As one of its main merits, multiagent swarm modeling and leader-follower configurations are jointly exploited within an ad hoc model predictive control framework to reduce as much as possible the use of onboard sensors that is essential in long-range missions. Moreover, the feasibility and asymptotic closed-loop stability of the resulting scheme are formally proved. Finally, a laboratory experiment is used to show the effectiveness of the proposed algorithm with particular attention to the follower capability of operating in a so-called blind fashion.

Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon

Human-leading truck platooning systems have been proposed to leverage the benefits of both human supervision and vehicle autonomy. Equipped with human guidance and autonomous technology, human-leading truck platooning systems are more versatile to handle uncertain traffic conditions than fully automated platooning systems. This paper presents a novel distributed stochastic model predictive control (DSMPC) design for a human-leading heavy-duty truck platoon. The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon. The driver behavior of the human-driven leader truck is learned by a stochastic inverse reinforcement learning (SIRL) approach. The proposed stochastic driver behavior model aims to learn a distribution of cost function, which represents the richness and uniqueness of human driver behaviors, with a given set of driver-specific demonstrations. The distributed formation control includes a serial DSMPC with guaranteed recursive feasibility, closed-loop chance constraint satisfaction, and string stability. Simulation studies are conducted to investigate the efficacy of the proposed design under several realistic traffic scenarios. Compared to the baseline platoon control strategy (deterministic distributed model predictive control), the proposed DSMPC achieves superior controller performance in constraint violations and spacing errors.