Distributed Model Predictive Control Approach Research Articles

Model predictive coordinated cooperative control mechanism for multiagent systems based on priority negotiation

Aiming at the control requirements in complex industrial processes and the problem of poor decision-making flexibility of agents in state/output difference-based consensus strategies, a novel model predictive coordinated cooperative control mechanism is proposed using multiagent systems as the computational paradigm. The proposed mechanism considers the global state of the system, the interaction of control actions among agents, the constraints of the process, and energy consumption. According to the proposed coordinated cooperative control mechanism, each agent must accomplish three tasks at each time step. First, each agent proposes its own “winner-takes-all” control scheme and calculates its payoff. Then, adjacent agents determine their own priorities at the current time step according to the payoffs of the “winner-takes-all” scheme by using a designed pairwise game mechanism. Next, each agent, in order of priority, calculates its sequence of actual optimal control input increments by applying the dynamic predictive control approach based on the dynamic model of the system. Finally, the effectiveness of the proposed coordinated cooperative control mechanism is verified by comparing it with the networked coordination-based distributed model predictive control approach and the centralized model predictive control approach based on the temperature control process of heating an alumina ceramic block in a high-power microwave reactor with six microwave sources.

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

Model Predictive Control Strategies for Turbine Electrified Energy Management

Abstract The increasing electrification of aircraft propulsion systems is leading to new control architectures being developed to address integration between electric machines and gas-based turbine engines. For hybrid-electric propulsion systems, current conceptual architectures often couple electric machines with the shafts of gas turbine engines and introduce energy storage. Leveraging the electrical power system of hybridized engines, Turbine Electrified Energy Management (TEEM) is a recent control approach that improves transient operability in an effort to enable more efficient and lighter weight turbomachinery. This study seeks to expand TEEM's application beyond traditional proportional-integral (PI) control by presenting linear model predictive control (MPC) schemes to execute the TEEM concept. Through constraint selection and cost function design, transient operability goals for TEEM are considered with no external logic or saturation. Unique to the designs are the use of a washout filter, which simplifies transient detection and motor activation logic. The proposed architectures are implemented with both centralized MPC and distributed MPC approaches, and comparisons are drawn to a benchmark PI controller simulated on a nonlinear turbofan engine model at one ground condition and one cruise condition. Performance is evaluated using compressor maps, stall margin performance, and two novel metrics: transient stack usage and transient excursion integral. Results reveal the linear MPC scheme performs comparably to the baseline controller and can be implemented in at least two distinct configurations with potential for further modifications, thus establishing the groundwork for future investigations.

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.

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.

Distributed Model Predictive Consensus Control of Unmanned Surface Vehicles with Post-Verification

In this paper, the consensus control of unmanned surface vehicles (USVs) is investigated by employing a distributed model predictive control approach. A hierarchical control structure is considered during the controller design, where the upper layer determines the reference signals of USV velocities while the lower layer optimizes the control inputs of each USV. The main feature of this work is that a post-verification procedure is proposed to address the failure states caused by local errors or cyberattacks. Each USV compares the actual state and the predicted one obtained at the previous moment. This allows the estimation of local perturbations. In addition, the failure state of the USV can also be determined if a preset condition is satisfied, thus forcing a change in the communication topology and avoiding further impact. Simulations show that the proposed method is effective in USV formation control. Compared with the method without post-verification, the proposed approach is more robust when failure states occur.

Self-triggered distributed model predictive control for cooperative diving of multi-AUV system

This paper investigates the issue of cooperative diving of a multi-autonomous underwater vehicle (AUV) system using self-triggered distributed model predictive control (DMPC). Firstly, a basic DMPC approach is proposed to realize cooperative diving, where a stability condition is derived to facilitate control design. Then, the self-triggering mechanism is introduced into the basic DMPC approach to further save communication and computation resources. The triggering interval is determined with the maximum time instant satisfying the terminal constraint after solving the optimization problem, and this helps reduce the computation burden at each control loop. The feasibility and Lyapunov stability of the self-triggered DMPC approach are analyzed. Numerical simulation is provided to verify the effectiveness of the proposed method.

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.

Adaptive event-triggered distributed model predictive control for tracking consensus of multiagent systems

<p indent="0mm">The tracking consensus of multiagent systems based on distributed model predictive control (DMPC) is a current research hotspot. However, as a research difficulty, effective solutions to deal with external disturbances and reduce computational resource consumption are lacking. This paper proposes a new constraint tightening scheme and establishes a new distributed optimization problem. Moreover, an adaptive event-triggering mechanism is designed using the idea of variable prediction horizon, and an adaptive event-triggered DMPC approach for the tracking consensus of multiagent systems is proposed, effectively solving the problems of the unsatisfaction of constraints and computational resource consumption in the tracking consensus of multiagent systems. The effectiveness of the proposed approach is validated by a multivehicle system simulation.

Synchronization tracking control of networked multi-axis motion systems: A cooperative distributed model predictive control approach

Synchronization tracking control is an essential technique in networked multi-axis motion systems (NMAMSs). However, the uncertainties induced by network, such as delay and packet disordering, have great influence on control performance of the motion systems. To improve the influence of the uncertainties induced by network on synchronization tracking control performance in NMAMSs, a cooperative distributed model predictive control (CDMPC) strategy for NMAMSs is proposed. Firstly, the synchronization tracking control of the NMAMS is designed in a networked control framework. By introducing the synchronization error into performance index as a coupling term, a local controller adopting a CDMPC strategy is designed for each subsystem. Then, a Nash optimization iteration strategy is presented to deal with the CDMPC optimization problem, and the convergence and stability of the system are also analyzed. Finally, experiments are carried out to demonstrate the practicability and effectiveness of the proposed control strategy.

Formation control of multiple underwater robots based on ADMM distributed model predictive control

In this paper, we propose a distributed model predictive control approach based on the alternating direction multiplier method (ADMM) to solve the formation control problem of multiple autonomous underwater vehicles (AUV). First, a kinematic model with six degrees of freedom is established for underwater vehicles. This is a linear error model which can accelerate the calculation speed of the controller while still ensuring accuracy. This model also incorporates the design of a local distributed controller for each underwater robot. The formation factor is transformed into the state coupling constraint between robots, and ADMM is used for iterative calculations, thus solving the problem of robot formation control. Considering external interference, a feedforward compensation control link is added to ensure stable tracking of the reference trajectory by multiple robots under certain disturbances. Finally, results from simulations verify the effectiveness of the proposed algorithm.

A Decentralized Cooperative Control Framework for Active Steering and Active Suspension: Multi-Agent Approach

A modular electric vehicle (EV) platform enables ability to integrate different control systems in a flexible way. In this article, a decentralized cooperative control framework is proposed to achieve the integration of active front steering (AFS) system and active-suspension system (ASS) by applying a multi-constrained distributed model predictive control (MDMPC) approach, which aims to improve the vehicle lateral stability, ride comfort, and roll safety during path tracking. First, a partly decoupled 6-degree-of-freedom (DoF) half-vehicle dynamics model is constructed. Then, a multi-agent-system (MAS)-based framework is introduced to coordinate AFS and ASS, which allows for the multi-constraints within the information exchange between agents. Through minimizing linear convex combination of objective functions, the cooperative control strategy is ultimately solved by the Pareto-optimality theory. Moreover, vehicle lateral and roll motion stability region described by the phase plane is employed to bound the controllable limits and achieve the dynamic cooperation between AFS and ASS. The simulation and hardware-in-the-loop (HIL) test results show that the proposed framework is effective for coordinating AFS and ASS, thereby enhancing the vehicle lateral and vertical stability during path tracking.

A two-layer distributed MPC approach to thermal control of Multiprocessor Systems-on-Chip

Next-generation Multiprocessor, or Multicore, Systems-on-Chip offer very high computing performance at the expense of a very high power density unevenly distributed on the chip. The hot spots thus generated represent a significant source of performance and reliability degradation, as well as power consumption increase. In recent years, run-time thermal control strategies have been developed to deal with this issue by acting on some “computational knobs” (e.g., clock frequencies and supply voltages). In this context, schemes based on Model Predictive Control (MPC) are particularly suitable due to their capability to deal with constraints explicitly. In this paper, we first discuss relevant properties for the design of predictive controllers for thermal systems. Starting from the Partial Differential Equation (PDE) describing heat diffusion in a solid, we prove meaningful feasibility properties that can be leveraged for constraint reduction. We then present a procedure to derive approximated but effective modular thermal models intended to build an efficient distributed MPC. Finally, a two-layer control solution is proposed to maximize performance while preserving feasibility despite model approximations. The effectiveness of this approach is validated through extensive and realistic numerical simulations.

A scenario-based distributed model predictive control approach for freeway networks

In this paper a scenario-based Distributed Model Predictive Control (DMPC) approach based on a reduced scenario tree is developed for large-scale freeway networks. In the new scenario-based DMPC approach, uncertainties in a large-scale freeway network are distinguished into two categories: global uncertainties for the overall network and local uncertainties applicable to subnetworks only. We propose to use a reduced scenario tree instead of using a complete scenario tree. A complete scenario tree is defined as a scenario tree consisting of global scenarios and all the combinations of the local scenarios for all subnetworks, while a reduced scenario tree is defined as a scenario tree consisting of global scenarios and a reduced local scenario tree in which local scenarios are combined within each subnetwork, not among subnetworks. Moreover, an expected-value setting and a min–max setting are considered for handling uncertainties in scenario-based DMPC. In the expected-value setting, the expected-value of the cost function values for all considered uncertainty scenarios is optimized by scenario-based DMPC. However, in the min–max setting, the worst-case of the cost function values for all considered uncertainty scenarios is optimized by scenario-based DMPC. The results for a numerical experiment show that the new scenario-based DMPC approach is effective in improving the control performance while at the same time satisfying the queue constraints in the presence of uncertainties. Additionally, the proposed approach results in a relatively low computational burden compared to the case with the complete scenario tree.

Distributed Model Predictive Control Strategy for Constrained High-Speed Virtually Coupled Train Set

Virtual Coupling (VC) is regarded as a breakthrough to the traditional train operation and control for improving the capability and flexibility in railways. It brings benefits as trains under VC are allowed to operate much closer to one another, forming a virtually coupled train set (VCTS). However, the safe and stable spacing between trains in the VCTS is a problem since there are no rigid couplers to connect them into a fixed formation, especially in high-speed scenarios. Due to the close spacing, the interference between trains becomes non-negligible as various maneuvers of the preceding train can significantly affect driving behaviors of the following train; this results in fluctuating spacing and therefore an unstable VCTS. Aiming at minimizing the interference and maintaining constantly safe spacing between trains in the VCTS, this paper presents a distributed model predictive control (DMPC) approach for solving the high-speed VCTS control problem. Particularly, the proposed control method focuses on the feasibility and stability of this problem, with considerations of the coupled constraint of safety braking distance and the individual constraints of speed limit variations and restricted traction/braking performance. To guarantee feasibility and stability, the terminal controller and invariant set of the DMPC are designed. For rigor, sufficient conditions of feasibility and stability are mathematically proved and derived. Based on the data of the Beijing-Shanghai high-speed railway line, numerical experiments are conducted to verify the correctness of derived sufficient conditions and the effectiveness of the proposed control method under interference and disturbances.

A distributed model predictive control approach to cooperative adaptive cruise control of the heterogeneous platoon

This paper presents a distributed model predictive control algorithm to solve the cruise control problem of a heterogeneous platoon. Each following vehicle in the platoon can use the communication equipment to receive the information of the leading vehicle and its preceding adjacent one. The vehicles in the platoon are dynamically decoupled and have different dynamic parameters. Each vehicle solves a local optimal control problem independently. The cost function of each vehicle’s local optimal control algorithm is designed with traceability as the control objective, and its asymptotic stability is guaranteed by using the terminal constraint method. In addition, the timestamps of all vehicles in the platoon are synchronous, which means that in each sampling period, a specific vehicle in the platoon cannot obtain the solution results of other vehicles’ local optimal control problems at the current sampling moment. Under this restriction, the constraints that each vehicle needs to meet to realize the platoon’s string stability are also designed. Finally, the simulation results show the effectiveness of the algorithm.

Ramp metering control under stochastic capacity in a connected environment: A dynamic bargaining game theory approach

This paper presents a dynamic predictive and cooperative ramp metering approach that considers stochastic breakdowns at merging bottlenecks. A stochastic microscopic model is used to estimate traffic state parameters based on speed, location, and travel time information from connected vehicles. Traffic state predictions are obtained on a lane by lane basis using an adaptive Kalman filter (AKF) that fuses fixed detector measurements with the model; the AKF then produces multiple step ahead predictions. The ramp metering problem in this paper is modeled as a stochastic distributed model predictive control (SDMPC) approach. The SDMPC problem is solved based on a bargaining game approach where each controller, a player in the game, receives traffic state and control decision information from other controllers to solve the local optimization problem based on expected local costs and constraints. The performance of the proposed model is evaluated for three aspects of efficiency: short-term and long-term equity and effectiveness compared to multiple control scenarios. The outcomes indicate that the proposed cooperative model with stochastic capacity considerations outperforms the deterministic capacity-based models in regard to effectiveness and equity properties. However, the centralized approach performs slightly better in respect to system-wide efficiency.

Hierarchical distributed model predictive control based on fuzzy negotiation

This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eight-coupled tank plant via simulation.