Centralized Model Predictive Control 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.

Semi-physical simulation and coordinated control of SOFC-PV/T-HP system

Due to the differences in the dynamic characteristics of various energy processes, the operation and control of multi-energy coupled integrated energy systems (IES) pose significant challenges. This paper focuses on the semi-physical simulation and coordinated control of SOFC-PV/T-HP thermoelectric system, which includes solid oxide fuel cells, a solar photovoltaic/thermal panel and a heat pump. In order to enhance the experimental safety and reliability of the research findings, a semi-physical simulation platform is developed by integrating physical equipment and dynamic models. The operational characteristics of the cogeneration system are investigated and controlled using an extended state-space based model predictive algorithm. The experimental results demonstrate that the designed centralized model predictive controller effectively stabilizes the demand power of the battery. The standard deviation of the battery demand power is reduced from 2.39 W to 0.66 W within 500 s, which alleviates the regulation burden of the battery. At the same time, the hot water temperature is also well controlled. The fluctuation of hot water supply temperature does not exceed 0.11℃ after stability. The system can maintain stable operation under external disturbance, and maintain the balance between electricity/heat supply and demand.

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.

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 model predictive control for Wave Energy Converter arrays

It is well known that commercial Wave Energy Converters (WECs) are likely to be deployed in arrays, which gives the possibility to enhance the energy harvesting properties of the wave farm as a whole. Advanced control strategies are necessary to exploit the potential and capture as much energy as possible in a limited sea space. Centralized control is a natural benchmark, as it sees the array as a large-scale system, making energy maximization a large plantwide problem. However, centralized control has a high requirement for computational efficiency. In this paper, a cooperative model predictive control (MPC) is proposed to achieve energy maximization while reducing computational cost. A modified array model is constructed to capture the dynamics of interactions in arrays, explicitly showing the linear couplings in the system state, control input, and excitation force of each subsystem. Sensitivity analyses are performed to evaluate the performance for different array layouts, separation distances, and sea states. The results show that cooperative MPC can achieve an energy capture performance that approximates centralized MPC, outperforming decentralized MPC significantly, and is well adapted to sea states. Furthermore, the proposed method has the potential to improve computational efficiency by 14.86%–51.14% for simulations in the study.

Centralized Model Predictive Control With Human-Driver Interaction for Platooning

Cooperative adaptive cruise control presents an opportunity to improve road transportation through increase in road capacity and reduction in energy use and accidents. Clever design of control algorithms and communication systems is required to ensure that the vehicle platoon is stable and meets desired safety requirements. In this paper, we propose a centralized model predictive controller for a heterogeneous platoon of vehicles to reach a desired platoon velocity and individual inter-vehicle distances with driver-selected headway time. As a novel concept, we allow for interruption from a human driver in the platoon that temporarily takes control of their vehicle with the assumption that the driver will, at minimum, obey legal velocity limits and the physical performance constraints of their vehicle. The finite horizon cost function of our proposed platoon controller is inspired from the infinite horizon design. To the best of our knowledge, this is the first platoon controller that integrates human-driven vehicles. We illustrate the performance of our proposed design with a numerical study, demonstrating that the safety distance, velocity, and actuation constraints are obeyed. Additionally, in simulation we illustrate a key property of string stability where the impact of a disturbance is reduced through the platoon.

Optimal design for efficiency enhanced hierarchical cell-to-cell equalization systems based on centralized model predictive control

Effective topology is particularly significant for enabling high-efficiency hierarchical cell-to-cell (CC) equalization systems for large-scale batteries. This paper proposes an efficiency-enhanced hierarchical battery equalization systems (BESs) by formulating a topology graph-balancing connectivity evaluation method. As a performance evaluation index, connectivity takes both balance time and energy loss into consideration, which can be measured by the second small eigenvalue of the Laplacian matrix of the topology graph. Then, a centralized model predictive control (MPC) balancing strategy is formulated and integrated with hierarchical topologies to tackle optimal residual available energy (RAE) balancing problems. Making use of simulation tests of hierarchical BESs with different configurations, results show that the balancing performance and measured connectivity are consistent, and the balancing connectivity gradually rises and then decreases with an increasing number of module groupings. The optimal hierarchical BES with 96-series cells can achieve a low balancing time of 229 s and an energy loss of 0.67%. The balancing time and energy loss of centralized MPC can be reduced by 80.28% and 34.02%, respectively, relative to decentralized MPC balancing. For practical implementation consideration, dynamic balancing simulation and hardware-in-the-loop (HIL) experiments are conducted, and the experimental results are well matched with the simulation.

Distributed model predictive control based on the alternating direction method of multipliers for branching open canal irrigation systems

Most studies of open canal automation concentrated on the control of single canal pool or cascaded multiple canal pools, neglecting the hydraulic coupling effects between the main canal and lateral canals. Low transmission efficiency and poor equity of agriculture water delivery may be caused in the practical application of large-scale irrigation networks. Considering the high computation and communication requirements for large-scale operating systems, a distributed model predictive control (DMPC) algorithm based on the alternating direction method of multipliers (ADMM) is developed for the branching open canal irrigation systems (BOCISs). A simplified BOCIS example, originating from Test case 2 proposed by the American Society of Civil Engineers, is used for control tests. Moreover, for control performance comparisons and evaluations, centralized model predictive control (CMPC) is employed by modifying the state space of the integrator delay (ID) model. The results show that the control performance of the proposed DMPC converges to the global optimal solution of the CMPC under the normal scenario, and the degradation is less than 0.35 %. The communication and coordination process of DMPC contributes to more powerful robustness and interference immunity than CMPC. Furthermore, in case of sudden accidents occurring in some subsystems, the proposed DMPC is more convenient and timely to execute fault isolation. The improvements on water level oscillations and overshoots can reach 20.3 % and 52.1 % in the other subsystems which need normal water delivery, respectively. Equipped with outstanding control characteristics, the proposed ADMM-based DMPC framework enables the water authorities of large-scale BOCISs to promote surface water distribution in a practicable, flexible, and secure way, showing great potential in developing intelligent irrigation districts.

Distributed and Localized Model-Predictive Control–Part I: Synthesis and Implementation

The increasing presence of large-scale distributed systems highlights the need for scalable control strategies where only local communication is required. Moreover, in safety-critical systems it is imperative that such control strategies handle constraints in the presence of disturbances. In response to this need, we present the Distributed and Localized Model Predictive Control (DLMPC) algorithm for large-scale linear systems. DLMPC is a distributed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">closed-loop</i> model predictive control (MPC) scheme wherein only local state and model information needs to be exchanged between subsystems for the computation and implementation of control actions. We use the System Level Synthesis (SLS) framework to reformulate the centralized MPC problem, and show that this allows us to naturally impose localized communication constraints between sub-controllers. The structure of the resulting problem can be exploited to develop an Alternating Direction Method of Multipliers (ADMM) based algorithm that allows for distributed and localized computation of closed-loop control policies. We demonstrate that computational complexity of the subproblems solved by each subsystem in DLMPC is independent of the size of the global system. DLMPC is the first MPC algorithm that allows for the scalable computation and implementation of distributed closed-loop control policies, and deals with additive disturbances. In our companion paper, we show that this approach enjoys recursive feasibility and asymptotic stability.

Analysis of Model Predictive Intersection Control for Autonomous Vehicles

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios.

Decentralized Distributionally Robust Coordination Between Distribution System and Charging Station Operators in Unbalanced Distribution Systems

Coordination between the distribution system operator (DSO) and charging station operators (CSOs) is crucial for ensuring stable and economical operation of distribution grids and electric vehicle charging stations (EVCSs). We propose a computationally efficient and privacy-preserving decentralized DSO-CSO coordination framework for robust operation of distribution grids and EVCSs under uncertainties in active unbalanced distribution systems. A centralized model predictive control (MPC) is developed based on distributionally robust optimization (DRO), which characterizes the uncertainties in photovoltaic generation outputs, loads, and electricity prices using the Wasserstein metric. Two scalable optimization approaches are presented to address the high computation time requirement of the proposed centralized DRO-based MPC method through the reduction of constraints: i) a robust optimization method including a distributionally robust bound on the uncertainties, and ii) a bi-level optimization method that determines critical buses associated with voltage violations. Furthermore, to preserve data privacy of electric vehicles, the centralized DRO-based MPC problem is transformed through an alternating direction multiplier method into a decentralized DRO-based MPC problem comprising the DSO and CSO problems. The efficiency and scalability of the proposed DSO-CSO coordination framework are analyzed using the IEEE 37-bus and IEEE 123-bus systems.

Centralized Model Predictive Control for Transient Frequency Control in Islanded Inverter-Based Microgrids

Centralized Model Predictive Control for Transient Frequency Control in Islanded Inverter-Based Microgrids

Agent-Based Decentralized Model Predictive Control for Plants With Multiple Identical Actuators

This article proposes a decentralized model predictive control (DMPC) algorithm without communication for systems consisting of multiple identical, independent actuators acting on a single central plant. The particular system design is relevant for applications where modularity is paramount and for highly dynamic systems, e.g., battery emulator systems controlled by multiple dc–dc converter modules. Each agent, consisting of an actuator and a DMPC, controls a virtually scaled version of the plant to implicitly consider the effects of other agents. The set of DMPCs achieves the same plant performance as a corresponding centralized model predictive controller (CMPC) in unconstrained operation. Also, the states of the independent agents converge toward the globally optimal CMPC solution. This is obtained by dividing the CMPC’s objective function into local objective functions related to the subsystems. The optimality and stability of the DMPC in unconstrained operation are shown analytically. The stability of control input-constrained operation is analyzed by computing the region of attraction. Numerical studies of a battery emulator system compare the performance of the DMPC with the global optimum in detail.

Data-Driven Modeling and Distributed Predictive Control of Mixed Vehicle Platoons

With the development of automatic driving technology and the internet of vehicles, platooning based on control of connected autonomous vehicles has become one of the most promising approaches to improve traffic efficiency. This paper studies the control problem of mixed vehicle platoons consisting of human-driven vehicles and connected autonomous vehicles. Firstly, we propose a data-driven method to model mixed vehicle platoons based on Koopman operator theory. This method gives a way to represent the mixed vehicle platoon by a linear model in a high-dimensional space, the approximation of which is obtained by a neural network framework. Secondly, we employ model predictive control (MPC) to address the platoon control problem of mixed vehicle platoons, where both centralized MPC and distributed MPC algorithms are designed. Finally, the effectiveness of the data-driven modeling method and the centralized/distributed MPC algorithms is verified by numerical simulations. It is revealed that the proposed data-driven DMPC algorithm exhibits comparable control performance with less computation cost compared with the centralized MPC algorithm, and it shows faster convergence speed than the nonlinear model based DMPC algorithm.

Distributed Coordinated Brake Control for Longitudinal Collision Avoidance of Multiple Connected Automated Vehicles

The emerging connected automated vehicle (CAV) technique brings a new opportunity for the development of cooperative collision avoidance systems. This paper proposes a distributed coordinated brake control (DCBC) algorithm for longitudinal collision avoidance of multiple CAVs. Firstly, the studied scenario is defined, within which the communication topology and vehicle dynamics are modeled for a vehicle string and the control objective is designed to minimize the total relative kinetic energy. Next, the control problem is formulated into a centralized model predictive control problem, which is then transformed into a quadratic programming (QP) problem. By separating the QP problem into several sub-problems assigned to each CAV, distributed optimization is introduced to solve the QP problem in a distributed manner. Elaborate numerical simulation comparisons are conducted to demonstrate the advantage of the proposed DCBC algorithm.

Stochastic wind energy integrated multi source power system control via a novel model predictive controller based on Harris Hawks optimization

ABSTRACT Combined voltage and frequency control is a critical control problem of modern power system to avoid blackouts. This paper discusses the simultaneous voltage and frequency control for an interconnected multi-source multi-area power system having two areas of equal generating capacity, each containing thermal, diesel, and wind units. A centralized model predictive controller (MPC) scheme is suggested to minimize voltage and frequency fluctuations. For effective control, a normalized performance criterion has been considered, and recently appeared Harris hawks optimization (HHO) algorithm has been integrated for the optimal tuning of MPC weights for the first time. The effectiveness of the proposed MPC-HHO controller has been verified after comparing its transient response performance indices with that of various existing controllers available in the literature. Quantitatively, the proposed controller yields a minimum performance index value, i.e. 0.000741, and it has been observed that the proposed MPC-HHO controlled system has 79.28%, 68.89%, 77.19%, and 81.96% better settling time for frequency deviation in area 1 when compared with PIλDF-LSA, FOIDF-LSA, MFO-FOPID, and PID-FA controllers, respectively. Further, the stochasticity in wind power generation has been considered with the help of Monte Carlo simulation of Autoregressive Integrated Moving Average model (ARIMA) model for practical wind speed data from National Renewable Energy Laboratory. The stability of the proposed controller has been verified using eigenvalues and Bode plot analysis, where the MPC-HHO controlled system was having more gain and phase margin values as compared to the PID controlled system. Furthermore, the robustness of the approach has been successfully evaluated by considering the nonlinearities, and parameter sensitivity analysis has been conducted after varying the parameters of voltage and frequency control loops and reevaluating the transient performance of the controlled system. Besides this, a separate case study wherein the most recent concepts like Electric Vehicle (EV) integration with grid and Virtual Inertia (VI) have been applied in addition to the proposed controller to provide auxiliary control support in the system. It has been validated that EV-VI coordinated MPC-HHO controller provides quality transient response under different scenarios including reduced inertia and stochastic load variations.

A Non-Cooperative Distributed Model Predictive Control Using Laguerre Functions for Large-Scale Interconnected Systems

This paper deals with a new non-cooperative distributed controller for linear large-scale systems based on designing multiple local Model Predictive Control (MPC) algorithms using Laguerre functions to enhance the global performance of the overall closed-loop system. In this distributed control scheme, that does not require a coordinator, local MPC algorithms might transmit and receive information from other sub-controllers by means of the communication network to perform their control decisions independently on each other. Thanks to the exchanged information, the sub-controllers have in this way the ability to work together in a collaborative manner towards achieving a good overall system performance. To decrease drastically the computational load in the small-size optimization problem with a short prediction horizon, discrete-time Laguerre functions are used to tightly approximate the optimal control sequence. For evaluating the proposed distribution control framework, a simulation example is proposed to show the effectiveness of the proposed scheme and its applicability for large-scale interconnected systems. The obtained simulation results are provided to demonstrate clearly that the proposed Non-Cooperative Distributed MPC (NC-DMPC) outperforms Decentralized MPC (De-MPC) and achieves performance comparable to centralized MPC with a reduced computing time. The system performance of the proposed distributed model predictive control is given.

Model Predictive Control for Stabilization of DC Microgrids in Island Mode Operation

DC microgrid (DCMG) is a promising technology for integrating distributed resources, such as solar generation and energy storage devices, that are intrinsically DC. Recently, model predictive control (MPC) is one of the control techniques that has been widely used in microgrid applications due to its advantages, such as transient response and flexibility to nonlinearity inclusion. MPC applications can be centralized, distributed, or decentralized based on the communication architecture. A major disadvantage of the centralized model predictive control (CMPC) is the high computational effort. This paper proposes a CMPC for DCMG stabilization that uses the admittance matrix of a reduced DCMG in the prediction equation and the one-step prediction horizon to decrease the computational effort. The proposed CMPC also replaces the hierarchical architecture primary and secondary controls, achieving voltage or power regulation. A hardware-in-the-loop (HIL) tool, known as RT-Box 2, has been used to emulate an 8-node DC microgrid with versatile buck–boost converters at the supply and power consumption nodes. The proposed predictive control exhibited better performance in comparison with the averaged voltage control in the HIL experiments.

Attitude tracking control for reentry vehicles using centralised robust model predictive control

In this work, a centralised robust model predictive control (CRMPC) algorithm is proposed for reentry vehicles to track reference attitude trajectories subject to state/input constraints and uncertainties. In contrast to most designs that apply a cascade control structure for the two-timescale attitude dynamical systems, the proposed control scheme utilises a centralised structure to avoid additional controller development and parameter turning. By designing a nonlinear feedback law and tightening the system constraints, robust constraint satisfaction can be ensured for all admissible uncertainties. In addition, to guarantee the recursive feasibility and closed-loop stability of the proposed CRMPC, a terminal controller, along with a terminal region, is introduced. The validity of using the proposed approach to solve the considered problem is confirmed by executing several experimental studies, which were compared against two other established methods.

A novel model predictive control strategy for multi-time scale optimal scheduling of integrated energy system

Model predictive control is one of the key technologies to realize the multi-time scale optimal scheduling of integrated energy system. However, the traditional centralized model predictive control has the high model order, a large amount of online calculation and is not easy to expand, it is not suitable for the optimal scheduling of an integrated energy system with many distributed units. In this paper, a multi-time scale optimal scheduling method of integrated energy system based on distributed model predictive control is proposed, which realizes the flexible scheduling of the integrated energy system through the coordination and cooperation of various subsystems. Firstly, the detailed models of various generation equipment are established according to the four energy forms of cold, heat, electricity and gas. Then, a multi-time scale optimal scheduling is divided based on three scales: day-ahead long time scale scheduling in 1 h, intra-day predictive control in 15 min and real-time adjustment in 5 min. Next, during the day-ahead scheduling and intra-day rolling optimization, we establish an optimization model based on the best economic operation of the system, the daily operating cost of the system and the minimum penalty cost of start-up and shutdown. During the real-time adjustment, a distributed model predictive control method is proposed to decompose the overall optimization problem of the integrated energy system. Each subsystem estimates the state according to the input sequence of other subsystems at the previous time and optimizes its own performance index. Finally, the case shows that the model predictive control strategy proposed in this paper can increase the speed of optimal operation by about 15% and reduce the cost of optimal operation by about 3.8% compared with the traditional centralized model predictive control method, which not only improves the control performance of system operation, but also improves the economy of system operation.