Distributed Model Predictive Control Research Articles

Coordinated distributed model predictive control for multi energy carrier systems.

Introducing Energy hubs (EHs) is a beneficial strategy for incorporating quickly expanding renewable energies. However, the stochastic nature of renewable energy sources (RESs) and fluctuating energy demand have produced a number of difficulties, including unstable voltage/frequency, challenging energy management, and difficult network interaction. Additionally, the changing in response time of electrical and heat demands will make control challenging. This paper proposes a distributed control system for use with dynamic EHs. The RESs and loads present in the multi-carrier system cause the dynamics considering here. In order to optimise system performance, this research suggests a distributed model predictive control strategy that considers expected behaviour and operational restrictions. The strategy's potential is demonstrated via simulations in which the proposed scheme is applied to a benchmark system.

Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperatively intercepting a maneuvering target, this paper proposes a cooperative guidance law with less energy consumption and a newly accurate time-to-go estimation algorithm in the two-dimensional (2D) plane. Firstly, based on the relative motion equations between UAVs and the target on the 2D plane, the line-of-sight (LOS) direction and the LOS normal direction models are established. Then, based on the distributed model predictive control (DMPC) theory, DMPC cooperative guidance laws are designed in two directions. This guidance law can ensure that all UAVs intercept the maneuvering target at the expected LOS angle at the same time and reduce the energy consumption during the guidance process. Then, a new time-to-go estimation algorithm is designed, which can reduce the time-to-go estimation error and improve the cooperative accuracy. Finally, the simulation results show that the DMPC cooperative guidance law reduces energy consumption by more than 50% compared to other guidance laws and the proposed time-to-go estimation algorithm improves the accuracy by 200% compared to traditional methods.

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

Distributed model predictive control for consensus of nonlinear systems via parametric sensitivity

To handle the nonlinear consensus problem, a distributed model predictive control (DMPC) scheme is developed via parametric sensitivity. A two-stage input computation strategy is adopted for enhancing optimization efficiency. In the background stage, each agent first establishes its next-step optimization problem based on communication topology, and then performs distributed optimization to calculate the future inputs. In the online stage, all the agents build their sensitivity equations based on new information. Three variants of sensitivity equation are developed based on the level of communication load capacity, and the corresponding computation strategies are proposed. After solution, the background inputs are corrected and implemented. The optimality and robustness of the proposed algorithm are rigorously derived. Finally, the superiority of this DMPC scheme is demonstrated in the multi-vehicle system with two different topologies.

Multi-agent distributed control of integrated process networks using an adaptive community detection approach

This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.

Obstacle avoidance control of UAV formation based on distributed model prediction

Aiming at the formation and maintenance problem of UAVs in an obstacle environment, a distributed model predictive control (DMPC) algorithm with no reference trajectory considering system constraints is proposed. In order to deal with the constraint coupling and cost coupling existing in model predictive control (MPC), the assumed trajectory is introduced to design a low conservative compatibility constraint and a cost function of no reference trajectory, so that the algorithm can be executed in a distributed and synchronous manner. Secondly, the terminal constraint is designed based on the speed barrier method to ensure the safety of the terminal domain, and a feasible terminal control input is given. The cost function is taken as a Lyapunov function, combined with the constructed stability constraint, and the iterative feasibility and system stability of the algorithm are analyzed. In addition, in order to take into account the real-time performance, a non-strictly stable DMPC algorithm that can better meet the requirements of formation obstacle avoidance is given on the basis of the proposed algorithm. The effectiveness and superiority of the proposed algorithm are verified by numerical simulation.

Interactive strategy of shared control vehicle and automated vehicles based on adaptive non-cooperative game

Considering the complex traffic environment in the future, an interactive strategy between human-machine shared control vehicle (HSCV) and automated vehicles (AVs) based on the adaptive non-cooperative game (NCG) theory is proposed to ensure the safety of vehicles interaction control involving drivers with different characteristics. In HSCV, first, considering the environmental risk potential field (ERPF) and the human-machine conflict coefficient, an adaptive weight distribution method is designed to realize the weight distribution between a driver and an automation system. Subsequently, the steering angle command imposed by a driver is introduced into the game solving process between HSCV and AVs, enabling adaptive dynamic game through weight adjustment. During V2V interaction, based on NCG and distributed model predictive control, a motion planning controller (MOPC) is designed to integrate the optimal solution problem of path planning and tracking control and solve the interaction problem with constraints. Two typical interactive scenarios are used to verify the feasibility of the strategy. The results demonstrate that the interaction strategy considering the different drivers’ operating commands not only realizes the interaction between a driver and an ego vehicle but also realizes the interaction between an ego vehicle and nearby vehicles to ensure the safety of vehicles.

Data-based distributed model predictive control for large-scale systems

Data-based distributed model predictive control for large-scale systems

STdi4DMPC: Distributed Model Predictive Control for Connected and Automated Truck Platoon With Mixed Traffic Flow Based on Spatiotemporal Trajectory Prediction

STdi4DMPC: Distributed Model Predictive Control for Connected and Automated Truck Platoon With Mixed Traffic Flow Based on Spatiotemporal Trajectory Prediction

Motion planning of multiple unmanned vehicles using sequential convex programming‐based distributed model predictive control

AbstractThis article tackles the complex challenge of multi‐vehicle motion planning by presenting the novel Sequential Convex Programming‐based Distributed Model Predictive Control (SCP‐DMPC) algorithm. Existing methods often struggle with the nonconvex nature of optimization in multi‐vehicle motion planning, frequently compromising on computational efficiency. SCP‐DMPC innovatively combines the predictive control of DMPC with the optimization prowess of SCP to address these issues efficiently, while adhering to both physical and operational constraints. To ensure precise attainment of target poses, a three‐stage control strategy is introduced, with theoretical analysis on its recursive feasibility and asymptotic stability, enhancing the reliability of the algorithm. Moreover, a heuristic‐based deadlock resolution scheme is devised to prevent vehicle stalling, a common issue in cooperative motion. Validated through simulations in challenging scenarios, including symmetric position swapping and obstacle‐laden formation transition, SCP‐DMPC demonstrates superior adaptability, precision, and efficiency. These results underscore its potential for robust, real‐time unmanned vehicle applications, suggesting new directions for future research and development in the field.

Consensus formation control of wheeled mobile robots with mixed disturbances under input constraints

This paper addresses the problem of distributed consensus-based formation control for wheeled mobile robots (WMRs) under the influence of mixed disturbances, including both random noise and non-random disturbances. A consensus formation auxiliary subsystem is constructed based on the leader’s position estimated by the distributed estimator. A formation tracking subsystem for each robot is constructed based on the trajectory tracking error method. The above two subsystems are constructed into an extended formation modeling system. Further, a distributed model predictive control (DMPC) is designed to control this system without disturbance, and the controller is solved by means of a general-purpose neural network. A combination of Kalman filter (KF) and extended state observer (ESO) is intended to reduce the effect of both non-random disturbances and random noise, hence increasing the controller’s resilience to disturbances. Moreover, a composite control law is designed to ensure the controller’s effectiveness. Finally, simulation results demonstrate that the proposed control strategy is well-suited to addressing the problem, as it not only achieves accurate formation control but also effectively regulates the robot’s physical constraints while suppressing both non-random disturbances and random noise.

Dynamic Optimal Obstacle Avoidance Control of AUV Formation Based on MLoTFWA Algorithm

In addressing the optimal formation obstacle avoidance control problem for Autonomous Underwater Vehicles (AUVs) in environments with unknown and moving obstacles, this paper employs the Modified Fireworks Algorithm based on a Loser Elimination Mechanism (MLoTFWA) and constructs a Distributed Model Predictive Control (DMPC) framework to achieve obstacle avoidance for AUV formations. Initially, a prediction model is established, followed by feedback compensation to mitigate the effects of unknown perturbations. An appropriate fitness function is then formulated, and enhancements such as the loser elimination rule are introduced to optimize the fireworks algorithm. Additionally, the concept of an adaptive DMPC prediction window is proposed to conserve resources. The local and global stability of the DMPC formation control framework is theoretically proven. Simulations verify that the control system based on the DMPC framework ensures safe obstacle avoidance for the formation, maintains formation consistency, and achieves the shortest and smoothest path. The improved fireworks algorithm demonstrates superior performance compared with the original fireworks algorithm and other optimization algorithms. In testing, the improved fireworks algorithm exhibits better adaptability, higher average fitness, and best fitness, along with a significantly faster convergence speed. Compared with the ordinary fireworks algorithm, the convergence speed is reduced by 30%.

Compensation control of commercial vehicle platoon considering communication delay and response lag

The real-time accurate control of vehicle is of great significance for the stability of platoon driving. To address the impact of random time-varying communication delays, communication packet loss, data disorder, and significant response lag of commercial vehicle on platoon control performance in non-ideal communication environment, a novel compensation control method for commercial vehicle platoon is proposed to overcome the abovementioned issues. The proposed method significantly improves the impact of communication issues and vehicle response lag on platoon control, ensuring platoon safety while reducing inter-vehicle spacing and spacing error, and maintaining platoon stability. In addition, this method combines the comprehensive compensation mechanism with the Distributed Model Predictive Controller (DMPC), avoiding the establishment of additional compensation control modules and effectively reducing the computational burden. The results under emergency braking condition show that, compared with the method without compensation, the proposed method significantly reduces inter-vehicle spacing and spacing error, reducing the average maximum spacing error from 29.25 m to 1.44 m, shortening the error convergence time of the platoon by 28 %, and ensuring platoon stability. Additionally, compared to the compensation method based on Kalman Filtering (KF) and Smith predictor, the proposed method can reduce the average maximum spacing error by 60 % while maintaining similar convergence time and platoon stability.

Adaptive-DMPC based energy management and pre-installation techno-economic analysis of a grid-tied cyber-physical community microgrid

The trends for creating smart cities is increasing as it gives assurance of high-quality living standards, however, converting an existing city into a smart one needs more time, investments and is highly tedious. Another concept of community based sustainable living that can be installed in and around outskirts of any city. All the features of smart cities can be brought in by most economical, sustainable, and environmental friendly way. A cyber-physical Community Microgrid (CMG) can have various loads such as heating, cooling, security and lighting purposes. The dependency on grid supply can be reduced by using renewable sources of electricity. Solar water heaters and biogas plant can take care of heating loads. Handling uncertainties in generation, time-varying load scenarios, and dynamics of supply grid are crucial for smooth operation of cyber-physical CMG. In this work, an Energy Management Strategy (EMS) for CMG is proposed using an adaptive Distributed Model Predictive Control (DMPC) that improves scalability with modular architecture, allow enhanced flexibility and adaptability, robustness and reliability, enhanced optimization and predictive capabilities. All possible intermittencies types due to renewables, time-variable demand profiles, smooth charging/discharging cycles and longevity of storage batteries, curtailing of noncritical loads, and dynamic price of grid consumption are applied. Further, use of EMS outcomes is done to develop pre-installation techno-economic analysis for establishing a CMG with optimal component sizing. The given DMPC scheme for CMG is verified by conducting comprehensive test case studies over 24 hrs interval on the Matlab/Simulink and hardware-in-loop testing.

A DMPC-based three-dimensional cooperative guidance scheme with impact time and impact angle constraints

In this paper, a distributed model predictive control (DMPC)-based three-dimensional (3D) cooperative guidance scheme is proposed for multiple interceptors with controllable thrust and constrained acceleration, to simultaneously attack a maneuvering target at desired terminal angles. The cooperative guidance scheme consists of a fractional power function-based extended state observer (FPESO) for disturbance estimation, and a DMPC controller to generate the control actions. By introducing the assumed state trajectory, the DMPC controller (of each interceptor) can simultaneously employ the zebra optimization algorithm (ZOA) to solve the constrained optimization problem, and generate the real-time guidance commands with no need of any global information. For restricting the uncertain deviation between the assumed state trajectory and the actual one, a compatibility constraint is designed, and the terminal ingredients are developed for coupling and decoupling stage costs, respectively. By applying the above designed constraints and the terminal ingredients, the recursive feasibility of the resulting optimization problem and the closed-loop stability of multi-interceptor system are both ensured. Through numerical simulations, it is verified that the proposed cooperative guidance scheme can achieve cooperative interception with impact time and angle constraints in various scenarios, and has advantageous performance.

Distributed safety flight control for LAV swarm in a hostile environment under multi-constraints and sudden appearing risks

PurposeLoitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.Design/methodology/approachTo address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).FindingsThe proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.Originality/valueThe proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

A two-timescale neurodynamic approach to robust distributed model predictive control for nonlinear systems

In this paper, a robust distributed model predictive control strategy is proposed for nonlinear dynamical systems affected by bounded disturbances. Grounded on sequential quadratic programming, a two-timescale recurrent neural network paradigm is deployed for solving minimax optimization problems. Sufficient criteria for the two-timescale recurrent neural network are delineated, which specifically guarantee its stability and optimality. Under these conditions, it is demonstrated that the recurrent neural network converges to optimal solutions, thereby enhancing the robustness of the control strategy. The simulation results exhibit significantly enhanced convergence in comparison to the mono-timescale recurrent neural network.

Distributed control for DC microgrids with cyber attacks and constraints: A fault-tolerant model predictive controller

Communication security issues pose severe challenges to stability of microgrid system. This paper designs a cooperative control method based on fault-tolerant distributed model predictive control (DMPC) for DC microgrid against possible false information injection (FDI) attacks and communication constraints problems. First, a state variable is defined based on the voltage and current information, thereby deriving a system model of the microgrid under the cyber-physical framework. Then, since a combined uncertainty term consisting of attacks and disturbances exists in the constructed system model, an expanded state observer (ESO) is proposed to observe the uncertainty term. Further, based on the constructed system model and the designed ESO, a fault-tolerant distributed model predictive controller is designed. In the controller, DMPC is utilized to constrain the communication information and ensure the boundedness of the communication state and physical state. Simulation results indicate that the proposed DMPC-based distributed control method can achieve stable voltage regulation and accurate current sharing, and maintain smooth operation even under FDI attack, which verifies the feasibility for the proposed method.

Distributed Model Predictive Control Based on Bus Voltage Derivative and SoC Dynamic Model for Shipboard DC Microgrids

State-of-charge (SoC) consistency and bus voltage regulation are two major control objectives of shipboard DC microgrids. To achieve these objectives, this paper presents a novel distributed model predictive control (DMPC) strategy with multiple cost functions. Firstly, based on the bus voltage derivative and SoC dynamic model, the voltage and SoC control equations in discrete form are established. Subsequently, considering the safe operation of battery energy storage systems (BESSs), a DMPC taking the energy storage current as the control set is designed. Finally, the average voltage compensation is taken to achieve precise control of the average voltage. The proposed method can avoid the complex process of adjusting weight coefficients, thereby simplifying controller design. Furthermore, the robustness and practicality of the proposed DMPC method are verified through MATLAB/Simulink 2021a simulations and hardware-in-the-loop (HiL) experiments.

Event‐triggered asynchronous distributed MPC for multi‐quadrotor systems with communication delays

AbstractThis article investigates the formation composition and keeping problem for multi‐quadrotor systems under time‐varying communication delays. First, a distributed model predictive control (MPC) approach is employed to transform the formation composition and keeping of multi‐quadrotor systems into an online rolling optimization issue, accompanied by the event‐triggered mechanism to reduce solution frequency and communication load. Second, to address the asynchrony and time‐varying communication delays introduced by event‐triggered distributed MPC, a set of constraints is designed to restrict deviations between the current predictive state and previously broadcasted states. Consequently, based on the previously predicted states of neighbors, the cooperation of the multi‐quadrotor systems is achieved under asynchronous communication and time‐varying delays. This approach guarantees robust asymptotic stability and satisfactory formation performance for multi‐quadrotor systems under various delay scenarios. Finally, the numerical and software‐in‐the‐loop simulations validate the effectiveness of the proposed algorithm of multi‐quadrotors under communication delays.