Distributed Model Predictive Control Research Articles

Partitional Collaborative Mitigation Strategy of Distribution Network Harmonics Based on Distributed Model Predictive Control

Dispersed harmonic control makes a critical guarantee for safe and stable operation of distribution networks. However, conventional point-to-point local control method has limitations. In addition, harmonic current injection has volatility and time-variability, and the residual capacity (RC) of multi-functional grid-connected inverter (MFGCI) employed for auxiliary harmonic compensation is uncertain, which challenges mitigation decision-making. In this study, a grid-side distributed mitigation strategy based on distributed model predictive control (DMPC) is proposed. Firstly, the voltage distortion observation node is selected based on node clustering, and sensitivity analysis of harmonic mitigation for observation nodes is conducted to divide the whole network into multiple control areas. Secondly, for global optimal of voltage distortion, the long time scale distributed optimization model for mitigation parameters orienting partitional coordination is established. Finally, a rolling optimization model based on DMPC for correction of conductance parameters is developed from regional autonomy perspective. The results show that: (1) Distributed mitigation scheme can improve the total harmonic distortion of voltage (THDV) of the whole network node; (2) Compared with control references, the corrected parameters can reduce the decision deviation and further lower the THDV by about 2%; (3) In multi-uncertain scenarios, multi-time-scale optimization can alleviate the uncertainty in harmonic control.

Hierarchical and Distributed Eco-Driving Approach for Mixed Vehicle Clusters at Unsignalized Intersections

To improve the driving efficiency and energy-saving characteristics for large-scale mixed traffic flows under different market penetration rates (MPRs) of intelligent and connected vehicles (ICVs) at unsignalized intersections, considering the cooperative eco-driving performance between ICVs and human-driven vehicles (HDVs) with time-varying speed characteristics, the hierarchical and distributed cooperative eco-driving architecture is first established in this paper, consisting of a cloud decision layer and a vehicle control layer. For the cloud decision layer, the multivehicle model-free adaptive predictive cooperative driving (MFAPCD) method is designed by using only the driving data of the HDVs and ICVs formation based on compact form dynamic linearization (CFDL) technique, thereby improving traffic efficiency. Furthermore, the CFDL integral terminal sliding mode predictive control (CFDL-ITSMPC) scheme is utilized to predict the time-varying driving speed of HDVs, and then, the CFDL predictive control (CFDL-PC) scheme is utilized to predict the expected control variables of ICVs formation. For the vehicle control layer, based on the anticipated driving speed obtained from the cloud decision layer, the nonlinear distributed model predictive control (NDMPC) method is utilized for distributed optimal control of each vehicle formation, to achieve optimization in terms of energy saving. Simulation results show that, compared with the signal time assignment strategy, the method can increase the average velocity by about 15.22% and decrease the average fuel consumption by about 36.43% under different MPRs and traffic volumes.

Testing distributed trajectory planning in the cyber-physical mobility lab

Abstract This article presents the testing of distributed trajectory planning algorithms using our rapid prototyping platform, the Cyber-Physical Mobility Lab (CPM Lab). We propose two algorithms for distributed trajectory planning which plan trajectories at intersections, highway on- and off-ramps, and lane changes for networked and autonomous vehicles. The algorithms avoid collisions between vehicles using a synchronization-based and a prioritized Distributed Model Predictive Control (DMPC) strategy. We test two algorithms in the CPM Lab which is able to handle parallel, sequential, and hybrid computations. The CPM Lab achieves reproducible experiments under non-deterministic computation times and stochastic communication times. Our evaluation shows that different algorithms for distributed trajectory planning can be efficiently tested in different in-the-loop tests.

Cooperative predictive control for arbitrarily mixed vehicle platoons with guaranteed global optimality

AbstractThis paper studies the cooperative control problem of a mixed vehicle platoon, which is composed of connected autonomous vehicles (CAVs) and human‐driven vehicles (HDVs) in an arbitrary order. An alternating direction method of multipliers (ADMM) based distributed model predictive control (DMPC) algorithm is proposed for CAVs to lead the mixed vehicle platoon travelling in a string with anticipated inter‐vehicle spacing and a desired velocity. First, the mixed vehicle platoon is divided into multiple interrelated sub‐platoons with any two adjacent sub‐platoons having a common CAV, and then a generic model is constructed for each sub‐platoon based on the intelligent driver model for HDV and the kinematic model for CAV, respectively. Second, a local MPC controller is designed for each sub‐platoon to optimize the control inputs of CAVs with the objective of minimizing the position and velocity errors of all vehicles in the sub‐platoon, and then the ADMM is utilized to obtain the global optimal solution among local MPC controllers of all sub‐platoons. Finally, numerical simulations and experiments are carried out to verify the effectiveness of the proposed DMPC algorithm, the results of which reveal that it can reduce the computation cost significantly and ensure the control performance comparable to the centralized MPC algorithm.

Coordinated Optimal Control of AFS and DYC for Four-Wheel Independent Drive Electric Vehicles Based on MAS Model.

The problem that it is difficult to balance vehicle stability and economy at the same time under the starting steering condition of a four-wheel independent drive electric vehicle (4WIDEV) is addressed. In this paper, we propose a coordinated optimal control method of AFS and DYC for a four-wheel independent drive electric vehicle based on the MAS model. Firstly, the angular velocity of the transverse pendulum at the center of mass and the lateral deflection angle of the center of mass are decoupled by vector transformation, and the two-degree-of-freedom eight-input model of the vehicle is transformed into four two-degree-of-freedom two-input models, and the reduced-dimensional system is regarded as four agents. Based on the hardware connection structure and communication topology of the four-wheel independent drive electric vehicle, the reduced-dimensional model of 4WIDEV AFS and DYC coordinated optimal control is established based on graph theory. Secondly, the deviation of the vehicle transverse swing angular velocity and mass lateral deflection angle from their ideal values is oriented by combining sliding mode variable structure control (SMC) with distributed model predictive control (DMPC). A discrete dynamic sliding mode surface function is proposed for the ith agent to improve the robustness of the system in response to parameter variations and disturbances. Considering the stability and economy of the ith agent, an active front wheel steering and drive torque optimization control method based on SMC and DMPC is proposed for engineering applications. Finally, a hardware-in-the-loop (HIL) test bench is built for experimental verification, and the results show that the steering angle is in the range of 0-5°, and the proposed method effectively weighs the system dynamic performance, computational efficiency, and the economy of the whole vehicle. Compared with the conventional centralized control method, the torque-solving speed is improved by 32.33 times, and the electrical consumption of the wheel motor is reduced by 16.6%.

Fast Distributed Model Predictive Control Method for Active Suspension Systems.

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

Distributed Model Predictive Control with Particle Swarm Optimizer for Collision-Free Trajectory Tracking of MWMR Formation

The distributed model predictive control (DMPC) strategy with particle swarm optimization (PSO) is applied to solve the collision-free trajectory tracking problem for the mecanum-wheeled mobile robot (MWMR) formation. Under the leader–follower framework, the predictive model is established considering the kinematics and dynamics of the MWMR with the uncertainties and external disturbances. Based on the information from itself and its neighbors, each MWMR is assigned its own finite-horizon optimal control problem, of which the objective/cost function consists of formation maintenance, trajectory tracking, and collision avoidance terms, and the control inputs of each MWMR are computed synchronously in a distributed manner. PSO serves as the fast and effective optimizer to find feasible solutions to these finite-horizon optimal control problems. Further, the feedback emendation is implemented using a double closed-loop compensator to efficiently inhibit the influence of unknown dynamics in real time. The stability of the proposed distributed formation control approach is strictly analyzed. Numerical simulations confirmed the robustness and effectiveness of the control approach in obstacle environments.

DMPC based distributed voltage control for unbalanced distribution networks with single-/three-phase DGs

This paper proposes a distributed voltage control scheme for unbalanced distribution networks (DNs) with single-/three-phase distributed generations (S/T-DGs) based on distributed model predictive control (DMPC). The active and reactive power outputs of each phase of DG units are optimally coordinated to regulate three-phase voltages of all buses to be close to the nominal value and mitigate voltage fluctuations. According to the operation conditions of three-phase DNs, two control modes are designed: preventive and corrective modes. In the preventive mode, the measured voltages of each phase are within the predefined limits, only the reactive power outputs of DG units are optimally adjusted to minimize the voltage deviations of each phase of all critical buses from the nominal value and mitigate the reactive power variations; while in the corrective mode, both the active and reactive power outputs of DG units are optimized to minimize the voltage deviations and the curtailed active power of DGs. The relationship between voltage variation of each phase and power injection is determined using an efficient analytical sensitivity calculation method. The DG controller only exchanges information with adjacent controllers and solves the local DMPC problem. A modified Finnish distribution network with 6 SDGs and 2 TDGs was used to verify the control performance of the proposed distributed voltage control scheme.© 2017 Elsevier Inc. All rights reserved.

Neurodynamics-based distributed model predictive control of a low-speed two-stroke marine main engine power system

This article studies a steady operation optimization problem of a low-speed two-stroke marine main engine (LTMME) power system including a cooling water subsystem, a fuel oil subsystem and a main engine subsystem with input and state constraints. Firstly, a distributed model with coupling inputs and states is established for the LTMME power system according to laws of thermodynamics and kinetics. Further, an optimization problem of the LTMME power system is formulated to ensure the system to operate steadily, subjected to constraint conditions of the distributed model and the input and state bounds. Moreover, the optimization problem is rewritten as a quadratic programming problem, and an iterative distributed model predictive control (DMPC) scheme based on a primal–dual neural network (PDNN) method is used to obtain the optimal inputs within the constrained range. Finally, based on the actual data from an underway ocean vessel named Mingzhou 501 with an LTMME power system, a group of simulations are carried out to verify the effectiveness of the proposed approach.

Event‐triggered distributed model predictive control scheme for temperature regulation in multi‐zone air conditioning systems with improved indoor thermal preference indicator

SummaryDriven by the increasing needs in multi‐zone buildings for reducing the air conditioning (A/C) system related energy consumption and increasing the occupants' thermal comfort, an event‐triggered distributed model predictive control (DMPC) scheme for building temperature regulation with improved indoor thermal preference indicator is proposed. To this end, a temperature‐energy model of the A/C system in a zone which considers multiple types of heat transfer, and the system model of the multi‐zone A/C system are derived first. On this basis, an improved indoor thermal preference indicator that trades off the A/C systems related energy consumption with the aggregate thermal comfort of the occupants is proposed for the multi‐zone A/C system, then the optimal indoor temperature set‐points for each zone are determined by using the distributed consensus algorithm. It is followed by an event‐triggered DMPC scheme by adjusting the A/C systems to arrive at the optimal indoor temperature set‐points for all the zones with lower communication and computation load. The performance and effectiveness of the proposed methods are demonstrated through a case study on an actual multi‐zone A/C system.

A cooperative game approach to synthesizing a sequential distributed model predictive controller

A cooperative game approach to synthesizing a sequential distributed model predictive controller

Cooperative distributed model predictive control for robot in-hand manipulation

PurposeThis study aims to process multi-agent system with kinds of limitations and constraints, and consider the robot in-hand manipulation as a problem of coordination and cooperation of multi-fingered hand.Design/methodology/approachA cooperative distributed model predictive control (MPC) algorithm is proposed to perform robot in-hand manipulation.FindingsA cooperative distributed MPC approach is formulated for robot in-hand manipulation problem, which enables address complex limitation and constraint conditions in object motion planning, and realizes tracking trajectory of the object more than tracking position of the object.Originality/valueThis method to implement the moving object task uses the kinematic parameters without the knowledge of dynamic properties of the object. The cooperative distributed MPC scheme is designed to guarantee the movement of the object to a desired position and trajectory at algorithmic level.

Distributed MPC-Based String Stable Platoon Control of Networked Vehicle Systems

In this paper, the string stable platoon control problem of discrete-time networked vehicle systems is considered by using distributed model predictive control (MPC) based method. An optimization problem is established to minimize the cost function associated to the system trajectories. The last-step shifting method is applied to set the local optimal solution as the assumed solution and send it to the neighbor vehicles. By using the sum of the cost function as Lyapunov function, the stability of the closed-loop platoon system is studied. Comparing with existing results, the string stability, which is the unique characteristics of the platoon system, is guaranteed under the bidirectional-based structure as well as the predecessor-follower-based information flow structure. Finally, several simulations are presented to demonstrate the effectiveness of the proposed algorithms.

Robust Longitudinal Distributed Model Predictive Control of Connected and Automated Vehicles With Coupled Safety Constraints

This paper deals with robust cooperative control of connected and automated vehicles (CAVs) with special consideration of the coupled safety inter-vehicle distance constraints. To address the hard constraints caused by the vehicle physical limitations and the coupled safety constraints among adjacent vehicles explicitly, the distributed model predictive control (DMPC) problem for CAVs is formulated. By virtue of the Lagrangian multiplier method and the dual decomposition technique, the DMPC problem subject to coupled constraints is recast into a distributed dual variable consensus optimization problem comprised of a series of subsystems with local copies of the dual variables, which is then cooperatively solved in parallel based on the alternating direction method of multiplier (ADMM) under the distributed information exchange mechanism. In addition, the disturbance is dynamically mitigated by the feedback control, and its influence on the vehicle status is quantified by the associated robust positively invariant set. Robust cooperative control protocols are proposed to regulate the cooperative operations of CAVs, and the recursive feasibility and the closed-loop stability are theoretically proved. Numerical experiments are carried out to illustrate the effectiveness of the proposed method.

Distributed Model Predictive Control for Heterogeneous Vehicle Platoon With Inter-Vehicular Spacing Constraints

This paper proposes a distributed control scheme for a platoon of heterogeneous vehicles based on the mechanism of model predictive control (MPC). The platoon composes of a group of vehicles interacting with each other via inter-vehicular spacing constraints, to avoid collision and reduce communication latency, and aims to make multiple vehicles driving on the same lane safely with a close range and the same velocity. Each vehicle is subject to both state constraints and input constraints, communicates only with neighboring vehicles, and may not know a priori desired setpoint. We divide the computation of control inputs into several local optimization problems based on each vehicle’s local information. To compute the control input of each vehicle based on local information, a distributed computing method must be adopted and thus the coupled constraint is required to be decoupled. This is achieved by introducing the reference state trajectories from neighboring vehicles for each vehicle and by employing the interactive structure of computing local problems of vehicles with odd indices and even indices. It is shown that the feasibility of MPC optimization problems is achieved at all time steps based on tailored terminal inequality constraints, and the asymptotic stability of each vehicle to the desired trajectory is guaranteed even under a single iteration between vehicles at each time. Finally, a comparison simulation is conducted to demonstrate the effectiveness of the proposed distributed MPC method for heterogeneous vehicle control with respect to normal and extreme scenarios.

Multi-time scale optimization scheduling of microgrid considering source and load uncertainty

With the increase of renewable energy penetration in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power of contact lines and system economy when achieving optimal scheduling. In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed. In the day-ahead scheduling stage, a two-stage distributionally robust optimal scheduling model is established with the objective of minimizing the comprehensive day-ahead scheduling cost of the microgrid, and the optimal day-ahead scheduling solution is found under the probability distribution of the worst scenario. In the intra-day scheduling stage, based on the prediction value of source and load with higher accuracy, a distributed model predictive control method is used to build an intra-day rolling optimization and real-time adjustment optimal scheduling model, which ensures the effective implementation of the day-ahead plan and fully suppresses power fluctuation of the contact line. The simulation results show that the proposed multi-time scale optimal scheduling method can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid.

Formation Trajectory Tracking Control of UTVs: A Coupling Multi-Objective Iterative Distributed Model Predictive Control Approach

Formation Trajectory Tracking Control of UTVs: A Coupling Multi-Objective Iterative Distributed Model Predictive Control Approach

Distributed Scheme for Main Engine Power Control Systems of Small and Medium Sized Container Ships Based on Nash Equilibrium

As a main force of ocean shipping, the energy consumption of container ships is considerably high due to the frequent load shifts of main engine and other disturbances. In this study, for engine room power systems in intelligent container ships, including cooling water subsystems, fuel oil subsystems, and main engine subsystems, a coordinated control scheme based on a correlation model and a distributed model predictive control approach was proposed to reduce continual fluctuations of control and output values through optimisation in a negotiated manner. The correlation model considers the interconnections among the subsystems, and the control approach achieves Nash equilibrium solutions. A comparative simulation was carried out between the proposed method and the decentralized model predictive control. The result shows that the proposed method works better than the decentralized one with less fluctuations. This study provides a theoretical basis for integrated designs of power control systems in intelligent container ships.

Cooperation and Coordination Transportation for Nonholonomic Mobile Manipulators: A Distributed Model Predictive Control Approach

This article addresses the problem of cooperation and coordination transportation for decoupling nonholonomic mobile manipulators (NMMs) in a workspace with obstacles. We propose a distributed model predictive control (MPC) approach for a team of NMMs to transport a target object while satisfying significant constraints and limitations, such as the feasible state and control input constraints, parameter synchronization constraints, and obstacles within the workspace. First, under the framework of the decoupling dynamics, an auxiliary dynamics model for task-space end-effectors and null-space mobile bases is obtained by the nonlinear feedback technique based on the Euler–Lagrange description of the NMMs. Using the modified virtual structure method, the cooperation and coordination transportation problem for NMMs is simplified as two independent synchronization tracking control problems for task-space end-effectors and null-space mobile bases. A distributed constrained optimization problem is established by taking the parameter synchronization and system constraints into the cost function. A general projection neural network (GPNN) approach is employed to solve the optimization problem and obtain the optimal control input. Moreover, a sufficient condition that guarantees the stability of the closed-loop system is further developed. Simulation results show that the proposed cooperation and coordination transportation strategy is feasible and effective.

A MAS-Based Hierarchical Architecture for the Cooperation Control of Connected and Automated Vehicles

Complex traffic scenarios pose a challenge to the cooperation control of connected and automated vehicles (CAVs). Conventional platoon control or advanced driver-assistance systems only provide an isolated solution due to their inadequacies in systematic perspective. To this end, a multi-agent system (MAS) based distributed control architecture together with a hierarchical controller is proposed for the CAVs cooperation control system. The upper layer considers the potential states of a CAV in the platoon evolution (i.e., vehicle-cruising maneuver and vehicle-following maneuver) and elaborates the transition rules for discrete maneuvering behaviors. The lower layer includes the corresponding motion controller for each CAV. Artificial potential field (APF) combined with the distributed model predictive control algorithm is adopted to realize the integrated longitudinal, lateral and yaw motion control of CAVs. Next, inspired by the Pareto-optimal method for tackling the MAS-cooperation system, an optimal solution strategy is introduced to solve the CAVs cooperation problem from the viewpoint of the control theory. Furthermore, multi-constraints are designed to ensure a safety inter-vehicle spacing between CAVs, while the vehicle stability can also be guaranteed. The simulation results demonstrate that the MAS-based hierarchical control architecture can effectively handle the cooperation control of CAVs in the typical traffic scenarios. The real-time implementation of the proposed controller also proves the feasibility.