Distributed Model Predictive Control Research Articles

Feasible cooperative linked-optimization based distributed model predictive control for networked Linear large-scale constrained systems: application to power systems

Feasible cooperative linked-optimization based distributed model predictive control for networked Linear large-scale constrained systems: application to power systems

Parallel distributed collision avoidance with intention consensus based on ADMM

This paper presents an approach to the problem of collaborative collision avoidance of autonomous inland ships. We propose a distributed model predictive control algorithm that allows ships to negotiate their intention to collaboratively avoid collisions directly. Furthermore, a new method is introduced to better shape ships’ behavior in order to follow Traffic regulations. The simulation results illustrate that the proposed algorithm could significantly increase ship safety navigation. Moreover, it also shows that the solution proposed by our algorithm complies with waterway Traffic regulations except in complex situations when other safe solutions are negotiated.

Ecological Electric Vehicle Platooning: An Adaptive Tube-based Distributed Model Predictive Control Approach

Ecological Electric Vehicle Platooning: An Adaptive Tube-based Distributed Model Predictive Control Approach

Distributed Model Predictive Control for Virtually Coupled Heterogeneous Trains: Comparison and Assessment

Distributed Model Predictive Control for Virtually Coupled Heterogeneous Trains: Comparison and Assessment

Cooperative object transportation with differential-drive mobile robots: Control and experimentation

Non-prehensile cooperative object transportation is a challenging model problem for distributed control and organization methods but also has practical applications. Therefore, it is widely studied in distributed robotics research. This paper describes and evaluates a novel transportation scheme for differential-drive mobile robots that is, to the authors’ best knowledge, the most versatile scheme of its kind successfully evaluated with real-world hardware. The proposed scheme can conceptually deal with any number of robots and arbitrary polygonal objects, including non-convex ones, without having to retune, retrain, or reconfigure any of the control parameters between different scenarios. This is achieved by splitting the task into a formation control and a formation finding task, both of which are tackled with model-based approaches using distributed optimization. Formation control and formation finding are complicated by the robots’ non-holonomic kinematic constraints. Therefore, a tailored distributed model predictive controller is used for formation control. Finding formations relies on a multibody-dynamics representation of the robots-object system to properly account for contact and non-holonomic constraints. Due to these measures, the transportation scheme achieves a very satisfactory performance and dexterity in real-world hardware experiments utilizing network communication and distributed computation.

DMPC-based load frequency control of multi-area power systems with heterogeneous energy storage system considering SoC consensus

The energy storage system (ESS) has been widely used for the load frequency control (LFC) of power systems. The heterogeneous ESS (HESS) consisting of various types of energy storage units (ESUs) with different regulation characteristics creates many difficulties in designing a high-performance LFC strategy for a multi-area power system. To obtain the satisfied dynamic response of the LFC scheme against load disturbances and keep the state of charge (SoC) balance of the HESS, this paper proposes a distributed model predictive control (DMPC) method to design the LFC strategy. Firstly, the dynamic model of the power system with HESS is established. Then, the distributed predictive model for LFC design is formulated by considering the LFC objective, the SoC balance requirement, and the operational constraints of the generators and the HESS. Subsequently, the optimal LFC strategy is obtained by solving the distributed predictive model based on the linear quadratic programming (LQP) method. Finally, we make case studies to validate the effectiveness of the proposed method. Compared with the PI-based LFC, the proposed DMPC-based LFC performs better in stabilizing the system frequency, narrowing the area control error (ACE), and keeping the SoC of all the ESUs consistent.

Distributed Formation Control with Obstacle and Collision Avoidance for Hypersonic Gliding Vehicles Subject to Multiple Constraints

Multiple hypersonic gliding vehicles’ (HGVs’) formation control problems with obstacle and collision avoidance are investigated in this paper, which is addressed in the stage of entry gliding. The originality of this paper stems from the formation control algorithm where constraints of dynamic pressure, heating rate, total aerodynamic load, control inputs, collision avoidance, obstacle avoidance, and the terminal states are considered simultaneously. The algorithm implements a control framework designed to be of two terms: distributed virtual controller and actual control input solver. The distributed virtual controller is based on distributed model predictive control with synchronous update strategy, where the virtual control signals are derived by the optimization simultaneously at each time step for each HGV under directed communication topology. Subsequently, according to the virtual control signals obtained, a coupled nonlinear equation set is solved to get actual control signals: each HGV’s bank angle together with the angle of attack. The actual control input solver adopts a feasible solution process to calculate the actual control signals while dealing with constraints. Finally, extensive numerical simulations are implemented to unveil the proposed algorithm’s performance and superiority.

Distributed model predictive control based on the alternating directions method of multipliers applied to voltage and frequency control in power systems

AbstractWe present a straightforward way to solve a model predictive control problem for a power network system given as a nonlinear differential‐algebraic equation (DAE) in a distributed way using the consensus alternating directions method of multipliers (consensus ADMM) algorithm. While no convergence‐ or stability results are available for fully nonlinear DAE models, this gives unprecedented experimental evidence that power network systems of the presented structure allow to be controlled in this way, unlocking the numerous combined advantages of distributed and predictive control schemes in the context of energy distribution networks, as well as broadening the field of use for the consensus ADMM algorithm to nonlinear DAE models.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks: A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles (ICVs) against F-local Byzantine attacks. We introduce a resilient distributed model-predictive platooning control framework for such ICVs. This framework seamlessly integrates the predesigned optimal control with distributed model predictive control (DMPC) optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles. Notably, our strategy uses previously broadcasted information and a specialized convex set, termed the “resilience set”, to identify unreliable data. This approach significantly eases graph robustness prerequisites, requiring only an (F + 1)-robust graph, in contrast to the established mean sequence reduced algorithms, which require a minimum (2F + 1)-robust graph. Additionally, we introduce a verification algorithm to restore trust in vehicles under minor attacks, further reducing communication network robustness. Our analysis demonstrates the recursive feasibility of the DMPC optimization. Furthermore, the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs, while ensuring constraint compliance and cybersecurity. Simulation results verify the effectiveness of our theoretical findings.

Rolling self-triggered distributed MPC for dynamically coupled nonlinear systems

The mutual influences caused by dynamic couplings in large-scale systems increase the difficulty in the design and analysis of distributed model predictive control (DMPC), and require information exchange among subsystems which calls for a scheduling strategy to save communication resources in communication-limited environments. To circumvent the two problems, we design a rolling self-triggered DMPC strategy for large-scale dynamically coupled systems with state and control input constraints. First, the optimal control problem where the cost is subject to the coupled dynamic and the constraints are subject to the uncoupled counterpart is proposed, forming the dual-model DMPC that is simple in design and analysis but yields good control performance. Second, the information exchange only occurs at some specified triggering instants determined by a rolling self-triggered mechanism, saving communication resources more significantly. The effectiveness of the designed strategy is verified by numerical simulations.

Distributed Sensor-Tolerant MPC for Formation Tracking of Networked Multicopters With Input/State Constraints

This brief presents a novel model predictive control (MPC) algorithm to achieve formation tracking of networked multicopters (nodes) with sensor faults and physical constraints. The algorithm can be used to asymptotically form and maintain a user-defined three-dimensional pattern for the multicopters formation. Another concern lies on how to deal with the usual sensor faults caused by the failure of the Global Positioning System (GPS) or the Inertial Navigation System (INS), in the above-mentioned control process. To this end, we embed the residual generators and auxiliary variable observers into the algorithm for the detection and estimation of the multicopters faults. Multicopters only accept the information related to their neighbors for real-time online rolling optimization. By using the sum of local cost functions to construct a Lyapunov candidate, it is proved that the asymptotic stability of such a distributed MPC (DMPC) is guaranteed under the condition that the weight requirements are satisfied. Simulation results demonstrate the rationality and effectiveness of the proposed algorithm.

Resilient and constrained consensus against adversarial attacks: A distributed MPC framework

This paper proposes a distributed resilient consensus framework consisting of a distributed model predictive control (DMPC)-based consensus protocol and a new distributed attack detection mechanism, aiming to effectively handle the general linear constrained MAS under adversarial attacks. Based on the historical information from neighbors and a resilience set, we can evaluate the reliability of communication links via the attack detection mechanism. The proposed detection mechanism can significantly reduce the requirement on the network robustness compared with the well-known mean-subsequence-reduced (MSR) algorithms. The resilient consensus of general linear constrained MAS with F-locally adversarial attacks is achieved when the communication network is (F+1)-robust. Furthermore, the DMPC-based resilient consensus framework exhibits the following key feature: the constrained consensus performance is optimized by minimizing a set of control variables. We demonstrate that the recursive feasibility of the associated DMPC optimization problem and the resilient consensus convergence of the constrained MAS can be guaranteed. Finally, the effectiveness of the proposed framework is illustrated through simulation experiments.

SVD-Based Robust Distributed MPC for Tracking Systems Coupled in Dynamics With Global Constraints.

This article presents a novel singular value decomposition (SVD)-based robust distributed model predictive control (SVD-RDMPC) strategy for linear systems with additive uncertainties. The system is globally constrained and consists of multiple interrelated subsystems with bounded disturbances, each of whom has local constraints on states and inputs. First, we integrate the steady-state target optimizer into the MPC problem through the offset cost function to formulate a modified single optimization problem for tracking changing targets from real-time optimization. Then, the concept of constraint tightening is utilized to enhance the robustness and ensure robust constraint satisfaction in the presence of interferences. On this basis, the SVD method is introduced to decompose the new optimization problem into several independent subsystems on the orthogonal projection space, and a distributed dual gradient algorithm with convergence proved is implemented to obtain the control of each nominal subsystem. The recursive feasibility is then ensured and the tracking ability of the strategy is analyzed. It is verified that for a target, the system can be steered to a neighborhood of the closest possible steady setpoint. At last, the effectiveness of the raised SVD-RDMPC strategy is established in two simulations on building temperature control and load frequency control.

Multi-priority HVAC temperature control with resource constrained based on coordinated distributed zone MPC

The conventional HVAC system for multi-area buildings includes both centralized and in-situ equipment to collectively regulate indoor air temperature to meet the preferences of its occupants. However, due to the limited capacity of the total supply air mass rate in centralized equipment, maintaining comfortable indoor air temperature for all areas can face conflicts. The first conflict arises between satisfying diverse preferences and limited global resources, while the second conflict is between centralized air supply and distributed regulation. In this paper, a distributed zone model predictive control (DZMPC) with priority conflict resolution is proposed for the multi-area HVAC systems. This strategy is composed of a zone parameter optimization with dynamic priority as a coordinated upper layer and a lower DZMPC layer. In the upper layer, a zone parameter optimization with priority is presented to coordinate sub-controllers by adjusting variable references’ bounds for DZMPC, to solve the resource competition when it occurs. In the lower layer, model predictive control with zone control is formed to mitigate the tension of the shared resource. By integrating dual decomposition and Augmented Lagrange function to address global constraints, a distributed solvable control structure is formed. The resulting optimization can be solved using a distributed primal–dual algorithm. The effectiveness of the proposed scheme is proved by simulation examples.

Distributed Control of an Ill-Conditioned Non-Linear Process Using Control Relevant Excitation Signals

Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we found that a summation of correlated and uncorrelated signals can yield better excitation of the plant. Our proposed distributed model predictive control (MPC) scheme uses a shifted input sequence to address loop interactions and reduce the computational load. This approach deviates from traditional schemes that use iteration, which can increase complexity and computational load. We initially tested the proposed method on the linear model of a highly coupled 2 × 2 process and compared its performance with decentralized proportional-integral-derivative (PID) controllers and centralized MPC. Our results show improved performance over PID controllers and similar results to centralized MPC. Furthermore, we compared the performance of the proposed approach with a centralized MPC on a nonlinear model of a distillation column. The results for the second study also demonstrated comparable performance between the two controllers with the decentralised control slightly outperforming the centralised MPC in some cases. These findings are promising and may be of interest to practitioners that are more comfortable with tuning decentralised loops.

Trajectory Tracking Control of Transformer Inspection Robot Using Distributed Model Predictive Control.

To overcome the difficulty in tracking the trajectory of an inspection robot inside a transformer, this paper proposes a distributed model predictive control method. First, the kinematics and dynamics models of a robot in transformer oil are established based on the Lagrange equation. Then, by using the nonlinear model predictive control method and following the distributed control theory, the motion of a robot in transformer oil is decoupled into five independent subsystems. Based on this, a distributed model predictive control (DMPC) method is then developed. Finally, the simulation results indicate that a robot motion control system based on DMPC achieves high tracking accuracy and robustness with reduced computing complexity, and it provides an effective solution for the motion control of robots in narrow environments.

Optimal virtual tube planning and control for swarm robotics

This paper presents a novel method for efficiently solving a trajectory planning problem for swarm robotics in cluttered environments. Recent research has demonstrated high success rates in real-time local trajectory planning for swarm robotics in cluttered environments, but optimizing trajectories for each robot is still computationally expensive, with a computational complexity from O ( k ( n t , ε ) n t 2 ) to O ( k ( n t , ε ) n t 3 ) where n t is the number of parameters in the parameterized trajectory, ε is precision, and k ( n t , ε ) is the number of iterations with respect to n t and ε . Furthermore, the swarm is difficult to move as a group. To address this issue, we define and then construct the optimal virtual tube, which includes infinite optimal trajectories. Under certain conditions, any optimal trajectory in the optimal virtual tube can be expressed as a convex combination of a finite number of optimal trajectories, with a computational complexity of O ( n t ) . Afterward, a hierarchical approach including a planning method of the optimal virtual tube with minimizing energy and distributed model predictive control is proposed. In simulations and experiments, the proposed approach is validated and its effectiveness over other methods is demonstrated through comparison.

Encrypted distributed model predictive control with state estimation for nonlinear processes

This research focuses on encrypted distributed control architectures, aimed at enhancing the operational safety, cybersecurity and computational efficiency of large-scale nonlinear systems, where only partial state measurements are available. In this setup, a distributed model predictive controller (DMPC) is utilized to partition the process into multiple subsystems, each controlled by a distinct Lyapunov-based MPC (LMPC). To consider the interactions among different subsystems, each controller receives and shares with the other controllers control inputs computed for its particular subsystem. As full state feedback is not available, we integrate an extended Luenberger observer with each LMPC, initializing the LMPC model with complete state estimate information provided by the observer. Furthermore, to enhance cybersecurity, wireless signals received and transmitted by the controllers are encrypted. Guidelines are established to implement this proposed control structure in any large-scale nonlinear chemical process network. Simulation results, conducted on a specific nonlinear chemical process network, demonstrate the effective closed-loop performance of the encrypted DMPC with state estimation, utilizing partial state feedback with sensor noise. This is followed by a comprehensive comparison of the closed-loop performance, control input computational time, and suitability of encrypted centralized, decentralized, and distributed MPC frameworks.

Distributed Model Predictive Formation Control for a Group of UAVs With Spatial Kinematics and Unidirectional Data Transmissions

This paper proposes a distributed model predictive control (DMPC) algorithm for the formation flight of a group of UAVs modeled by six degree-of-freedom (6-DoF) spatial kinematics. The formation system has a leader-follower structure with the leader cruising at a constant linear velocity during the flight, and the UAVs transmit data in a unidirectional order. The DMPC algorithm utilizes the unidirectional structure through the reception and transmission of an assumed output sequence as the reference trajectory, which is constructed from the solutions to the local DMPC problem and the prediction model. Based on the interactions between UAVs, convergence and stability of the multi-UAV formation are ensured by the terminal constraints and recursive feasibility of the DMPC optimization. Simulations involving time-delays, packet drops and turbulence effect are conducted to verify the effectiveness of the proposed algorithm. Comparisons with non-unidirectional data transmissions further demonstrate that the unidirectional structure plays a critical role in the convergence performance

Periodic Event-Triggered Robust Distributed Model Predictive Control for Multiagent Systems With Input and Communication Delays

This paper investigates the problem of event-triggered distributed model predictive control (DMPC) for continuous-time nonlinear multi-agent systems (MASs) subject to bounded disturbances, input and communication delays simultaneously. The compensation schemes for input and communication delays are proposed, respectively. A new optimal control problem (OCP) to achieve consensus among all agents is formulated under the proposed delay compensation schemes. To alleviate the communication burden and sensing cost, a novel input delay-related periodic event-triggering condition is proposed based on the state error to determine when to calculate the new control input. Sufficient condition guaranteeing the feasibility of the OCP is achieved with the proposed event-triggered control scheme. In stability analysis, a novel time-varying Lyapunov function is constructed, and the input-to-state practical stability (ISpS) of the MASs is derived by using the quasi-Lipschitz continuous property of the Lyapunov function. Numerical results related to vehicle platooning demonstrate the effectiveness of the proposed event-triggered DMPC.