Local Model Predictive Control Research Articles

Distributed model predictive control of fan coil system

To address the issues of large overshoot, long system stabilization time, and slow response associated with traditional PID-based and centralized model predictive control (MPC) of fan coil systems, this study proposes a distributed model predictive control (DMPC) method suitable for multi-zone building temperature regulation. Specifically, based on the thermal dynamic model of different zones, and aiming to constrain indoor temperature within set limits and minimize water system pressure drop and fan coil speed, a distributed MPC architecture for fan coil systems is developed. To solve the distributed optimization problem, the Nash equilibrium is introduced to find the global balance point within the distributed structure, and the Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) hybrid algorithm is used for online optimization of local MPCs. Experimental results show that compared to traditional PID control, the distributed MPC achieves coordinated multi-zone indoor temperature regulation with better dynamic performance, with the average absolute temperature error in each zone below 5 % and an energy savings rate of 16.23 %. Through semi-physical simulation, it is demonstrated that the proposed distributed MPC method has better applicability when the number of fan coils exceeds 10, compared to centralized MPC.

Cloud-Assisted Nonlinear Model Predictive Control for Finite-Duration Tasks

Cloud computing creates new possibilities for control applications by offering powerful computation and storage capabilities. In this paper, we propose a novel cloud-assisted model predictive control (MPC) framework in which we systematically fuse a cloud MPC that leverages the computing power of the cloud to compute optimal control based on a high-fidelity nonlinear model (thus, more accurate) but is subject to communication delays with a local MPC that relies on simplified linear dynamics due to limited local computation capability (thus, less accurate) while has timely feedback. Unlike traditional cloud-based control that treats the cloud as a powerful, remote, and sole controller in a networked control system setting, the proposed framework aims at seamlessly integrating the two controllers for enhanced performance. In particular, we formalize the fusion problem for finite-duration tasks with explicit consideration for model mismatches and errors due to request-response communication delays. We analyze stability-type properties of the proposed cloud-assisted MPC framework and establish approaches to robustly handling constraints within this framework in spite of plant-model mismatch and disturbances. A fusion scheme is then developed to enhance control performance while satisfying stability-type conditions, the efficacy of which is demonstrated with multiple simulation examples, including an automotive control example to show its industrial application potentials.

Hierarchical Software-Defined Control Architecture With MPC-Based Power Module to Interface Renewable Sources and Motor Drives

A hierarchical control architecture is designed with model predictive control (MPC)-based power module for generalized renewable applications including single/three-phase grid, solar, battery, electric motor, etc. The hierarchical control architecture is composed of three layers: (1) Central control layer for mode recognition of different types of interfaced load/source, reconstruction of power converter topologies, high level current/voltage/power control, generating references for local power module control and grid services for utility support; (2) Local module control layer for implementing MPC algorithm to track references from central controller with improved dynamic performance, stabilizing common-mode voltage, collecting ADC samplings and generate PWM signals for local power switches; (3) Application layer for the interface with different types of renewable loads/sources including single/three-phase grid, solar, battery, electric motor and so on. The merits of the designed control architecture include: (1) the reconfigurability to be suitable for different types of applications; (2) all non-isolated topologies with common-mode noise attenuation capability for renewable energy interfaces; (3) improved dynamic performance by local MPC power module; (4) high accuracy and robustness of the multi-layer MPC-based control without being influenced by the parametric modeling error from various interfaced applications; (5) grid services for abnormal condition utility support. The experimental results verified the proposed hierarchical control architecture.

Enhancing dynamic stability performance of hybrid ac/dc power grids using decentralised model predictive control

High voltage direct current (HVDC) networks are transforming ac power grids to hybrid ac/dc power grids; hence hybrid ac/dc power grids are facing new stability challenges. In this paper, a decentralised control scheme based on model predictive control (MPC) is proposed to enhance the stability of hybrid ac/dc power grids under disturbances. The proposed scheme controls the dc voltage, frequency, active and reactive power simultaneously with minimum deviations from steady-state values. A communication network is used to share the sampled grid data with the local MPC of each voltage source converter (VSC). Hence, the central controller can be avoided. Moreover, the active power-sharing between converters can be pre-designed. Thus, fast and accurate active power control can be realised. Finally, the proposed control scheme is validated using a five-terminal VSCHVDC test system with MATLAB. The VSCHVDC network with the proposed control scheme shows a smaller deviation and a faster response compared with the conventional droop control scheme and centralised MPC scheme under small disturbances. Under large power variations, the proposed control scheme assists to reach the steady-state rapidly with a minimum impact on the entire power grid. The proposed control scheme has also proved its robustness under communication delays and outages.

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.

Event-triggered distributed robust model predictive control for a class of nonlinear interconnected systems

In this work, we develop an event-triggered distributed robust model predictive control algorithm to stabilize the origin of a class of interconnected systems with nonlinear coupling terms and additive bounded disturbances. Each subsystem is assumed to be able to exchange information with its neighboring subsystems. The transmitted information between neighbors is used to estimate their future behaviors. Then, a robust distributed model predictive control algorithm for each subsystem is developed by integrating the estimations of its neighbors and each subsystem executes a local model predictive control law after solving its optimization problem. Furthermore, a distributed event-triggered mechanism is designed to trigger a series of asynchronous computations of the optimization problems, which achieves a trade-off between communication resource usage and control performance. Theoretical conditions on ensuring feasibility and closed-loop stability are provided. Finally, a practical example of the multi-machine power system with governor controllers is provided to show the effectiveness of the proposed algorithm.

Supervised model predictive control of large‐scale electricity networks via clustering methods

AbstractThis article describes a control approach for large‐scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118‐bus system is used to test the proposed design procedure in a nontrivial scenario.

A dissipativity‐based model predictive control algorithm for power flow systems with equilibrium‐independent stability guaranteed

AbstractThis article presents a distributed model predictive control algorithm for power flow systems that can maintain overall stability when the system equilibrium configuration changes. The dynamics of the large‐scale power flow systems can be described by transportation, conversion, and storage of energy among and across subsystems. By strategically choosing the output for each subsystem and augmenting each local model predictive controller with a special passivity‐incremental constraint, the equilibrium‐independent dissipativity property of each closed‐loop subsystem can be guaranteed. The dissipativity‐preserving coupling between subsystems in the power flow system can be utilized to maintain equilibrium‐independent dissipativity and stability of the overall system. Simulation results of a fluid tank system with a changing equilibrium configuration show the effectiveness of the proposed algorithm.

Distributed predictive control of interconnected systems based on disturbance observation

In this study, a distributed model predictive control (MPC) method based on extended state observer is proposed for a class of large-scale interconnected systems by using a reduced state-space decomposition strategy. Firstly, the full state of the system is divided into internal state and external state to decrease computation and communication burden, where the former one is used for local optimisation, and the latter one is used to exchange with its neighbours. Based on the receding optimisation strategy, a reduced state-space decomposition iterative algorithm is proposed, and the convergence of the proposed algorithm is given. To improve the disturbance rejection capability of the system, an extended state observer is designed to estimate the disturbance by using a pole assignment method. By applying a feedforward compensation strategy, a composite controller is derived by combining with local MPC controller and compensation controller, then the input-to-state stability is analysed for the closed-loop system. Finally, an example of a ship engine room power system is given to demonstrate the effectiveness of the proposed method.

Study on the distributed model predictive control for multi-zone buildings in personalized heating

To meet demands of smart heating development, the novel dynamic regulation methods of heating systems need to be pursued. This paper presents a model predictive control (MPC) approach for room temperature regulation in multi-zone buildings. Among them, a) For heating systems with nonlinearity and large time delay, a hybrid optimization algorithm consisting of the improved particle swarm optimization (IPSO) algorithm and Newton-Raphson (NR) method in online optimization of MPC for a single zone building is proposed. Compared with the quadratic programming method, it can identify successfully the optimal control vector. b) A distributed MPC approach is presented for multi-zone buildings existing hydraulic coupling. Firstly, Nash-optimization acts on searching the global equilibrium point in the distributed structure, then the hybrid optimization algorithm works in the online optimization of these local MPCs. Compared with the decentralized MPC, the control performance is improved through co-operating and communicating between subsystems via network to share information related to their future behavior. Besides, the global performance index in the distributed MPC is redefined as the weighted sum of energy consumption and thermal comfort, which provides the possibility to eliminate quickly the most unfavorable thermodynamic loop in heating systems and reduce energy consumption.

A Distributed Model Predictive Control Framework for Grid-Friendly Distributed Energy Resources

This work develops a distributed fast-frequency control framework for application in self-consuming energy communities. We propose a distributed consensus optimization of inertia coefficients based on the individual resource constraints of grid-friendly distributed energy resources (ders), which relies on the presence of a peer-to-peer communication infrastructure. In the proposed control strategy, the inertia coefficients of the individual ders are optimally tuned in real-time by a faster control loop to improve the frequency response metrics such as nadir frequency, peak overshoot, and the settling time of the frequency transient with the help of local model predictive controllers. The coordination among the ders are simulated on the cigre benchmark microgrid and the control is validated on a power hardware-in-the-loop platform.

Computation of Least-Conservative State-Constraint Sets for Decentralized MPC With Dynamic and Constraint Coupling

We address the problem of synthesizing state-constraint sets for a fully decentralized Model Predictive Control (MPC) scheme. We consider linear time-invariant discrete time systems, with subsystems possibly coupled in both dynamics and state constraints. For each individual subsystem we employ a set-based framework to compute the state-constraint sets, that are used to synthesize local tube-based MPC controllers. The offline problem that computes the constraint sets explicitly ensures that the feasible regions of the MPC controllers are non-empty, and whenever the controllers are feasible, the overall system constraints are satisfied with the least conservativeness possible. We demonstrate the closed-loop performance of the decentralized scheme, assessed with respect to centralized MPC, using a numerical example.

A Cascaded Optimization Approach for Modeling a Professional Driver's Driving Style

Abstract In the context of minimum-time vehicle maneuvering, previous works have shown that different professional drivers drive differently while achieving nearly identical performance. In this paper, a cascaded optimization framework is presented for modeling individual driving styles of professional drivers. Therein, an inner loop model predictive controller (MPC) finds the optimal vehicle inputs that minimize a blended-cost function over each receding horizon. The outer loop of this framework is an optimization computation which finds the optimal weights for each local MPC horizon that best fit data obtained from onboard vehicle measurements of the targeted drivers to the simulation of the maneuver under the cascaded control. This cascaded optimization is exercised for a case study on Sebring International Raceway where two different professional drivers were able to achieve nearly identical lap times while adopting different driving styles. It will be shown that this framework is able to model key differences in style between the two drivers during a particular corner. The models of the individual drivers are then fixed, and another optimization is used to tune tire parameters to suit each driving style and illustrate the utility of the approach.

Distributed model predictive control with guaranteed performance for reconfigurable power flow systems based on passivity

AbstractThis paper presents a passivity‐based distributed model predictive control algorithm that can provide minimum performance bound on power flow systems. The dynamics of large‐scale power flow systems can be described by the transportation, conversion, and storage of energy among and across subsystems. By strategically choosing a passive output for each subsystem and augmenting each local model predictive controller with a special passivity constraint, the passivity property of each closed‐loop subsystem can be guaranteed; in addition, the corresponding passivity index can be characterized by the designed parameters of the local controller. The passivity‐preserving coupling between subsystems can be utilized to maintain passivity and the stability properties of the overall system. Moreover, the passivity index of the overall system can be characterized by the transition of passivity indices of subsystems based on interconnection topology. Based on the passivity index, the minimum performance bound of the overall power flow system can be characterized. Even if the structure of the power flow system changes, the entire system can remain stable and achieve a certain performance level. Simulation results of a fluid tank system show the effectiveness of the proposed algorithm.

Enhanced information reconfiguration for distributed model predictive control for cyber‐physical networked systems

SummaryThis paper considers a class of cyber‐physical networked systems, which are composed of many interacted subsystems, and are controlled in a distributed framework. The operating point of each subsystem changes with the varying of working conditions or productions, which may cause the change of the interactions among subsystems correspondingly. How to adapt to this change with good closed‐loop optimization performance and appropriate information connections is a problem. To solve this problem, the impaction of a subsystem's control action on the performance of related closed‐loop subsystems is first deduced for measuring the coupling among subsystems. Then, a distributed model predictive control (MPC) for tracking, whose subsystems online reconfigure their information structures, is proposed based on this impaction index. When the operating points changed, each local MPC calculates the impaction indices related to its structural downstream subsystems. If and only if the impaction index exceeds a defined bound, its behavior is considered by its downstream subsystem's MPC. The aim is to improve the optimization performance of entire closed‐loop systems and avoid the unnecessary information connections among local MPCs. Besides, contraction constraints are designed to guarantee that the overall system converges to the set points. The stability analysis is also provided. Simulation results show that the proposed impaction index is reasonable along with the efficiency of the proposed distributed MPC.

Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use

This paper presents the development of a self-tuned HVAC controller that provides customized thermal conditions to satisfy occupant preferences (i.e., online learning) while minimizing energy consumption, and the implementation of this controller in a real occupied office space. The evolution of personalized thermal preference models and the delivery of thermal conditions with model predictive control (MPC) form a closed-loop. To integrate these two parts, we propose a new method that always provides a set of lower and upper indoor temperature bounds. Different from ad hoc rules proposed in previous research, the control bounds are based on a decision-making method that minimizes the expected cost. We implemented the self-tuned controller in an actual open-plan office space conditioned with a radiant floor cooling system with eight independently controlled loops. Localized operative temperature bounds in each radiant floor loop were determined based on occupants’ feedback and personalized thermal preference models, developed using a Bayesian clustering and online classification algorithm. The self-tuned controller can decrease occupant dissatisfaction compared to a baseline MPC controller, tuned based on general comfort bounds. To generalize the findings of this work: (i) we integrated the self-tuned controller with local MPC into a building simulation platform using synthetic occupant profiles, and (ii) demonstrated a method for automatic system adjustment based on comfort-energy trade-off tuning. In this way, decisions resulting in energy waste or occupant dissatisfaction are eliminated, i.e., the energy is deployed where it is actually needed.

Two-layer model predictive control of systems with independent dynamics and shared control resources

Abstract This paper describes an approach to the design of a controller for a system made by dynamically decoupled subsystems. Each subsystem is endowed with local actuators that, in normal operating conditions, are able to compensate for disturbances and to provide the required control action. If, due to too large disturbances, the local control action is not sufficient, the actuators of the neighbouring subsystems can provide the required control contribution. The control scheme is designed according to a hierarchical approach: at the lower layer local Model Predictive Controllers (MPC) are used to compute the required control action. The information about any shortage or excess of control is sent to a supervisor that, if needed, re-balances the local control actions to guarantee global disturbance rejection properties. A simulation example concerning the coordination of small electrical grids is discussed to show the performances of the proposed solution.

Self‐triggered distributed model predictive control for flocking of multi‐agent systems

This study presents a self-triggered distributed model predictive control algorithm for the flock of a multi-agent system. All the agents in a flock are endowed with the capability of determining the sampling time adaptively to reduce the unnecessary energy consumption in communication and control updates. The agents are dynamically decoupled in a flock, and each agent is driven by a local model predictive controller, which is designed by minimising the position irregularity between the agent and its neighbours, velocity tracking errors as well as its control efforts. Moreover, the collision avoidance is considered by introducing constraints in the model predictive minimisation problem. In order to adaptively determine the sampling time, a self-triggered algorithm is designed by guaranteeing the decrease of the Lyapunov function. Finally, numerical simulations are given to demonstrate the feasibility of the proposed flocking algorithm.

Model based urban traffic control, part I: Local model and local model predictive controllers

This paper is the first in a series of reports presenting a framework for the hierarchical design of feedback controllers for traffic lights in urban networks. The goal of the research is to develop an easy to understand methodology for designing model based feedback controllers that use the current state estimate in order to select the next switching times of traffic lights. In this paper we introduce an extension of the cell transmission model that describes with sufficient accuracy the major causes of delay for urban traffic. We show that this model is computationally fast enough such that it can be used in a model predictive controller that decides for each intersection, taking into account the vehicle density as estimated along all links connected to the intersection, what switching time minimizes the local delay for all vehicles over a prediction horizon of a few minutes. The implementation of this local MPC only requires local online measurements and local model information (unlike the coordinated MPC, to be introduced in the next paper in this series, that takes into account interactions between neighbouring intersections). We study the performance of the proposed local MPC via simulation on a simple 4 by 4 Manhattan grid, comparing its delay with an efficiently tuned pretimed control for the traffic lights, and with traffic lights controlled according to the max pressure rule. These simulations show that the proposed local MPC controller achieves a significant reduction in delay for various traffic conditions.

A Game-Theoretic Decentralized Model Predictive Control of Thermal Appliances in Discrete-Event Systems Framework

This paper presents a decentralized model predictive control (MPC) scheme for thermal appliances coordination control in smart buildings. The general system structure consists of a set of local MPC controllers and a game-theoretic supervisory control constructed in the framework of discrete-event systems (DES). In this hierarchical control scheme, a set of local controllers work independently to maintain the thermal comfort level in different zones, and a centralized supervisory control is used to coordinate the local controllers according to the power capacity and the current performance. Global optimality is ensured by satisfying the Nash equilibrium at the coordination layer. The validity of the proposed method is assessed by a simulation experiment including two case studies. The results show that the developed control scheme can achieve a significant reduction of the peak power consumption while providing an adequate temperature regulation performance if the system is $\mathcal{P}$ -observable.