Distributed Model Predictive Control Applied to a Sewer System

This paper describes the Model Predictive control (MPC) methodologies apply for a Bubble cap distillation column. Essentially in various cases the conventional controllers like PID not provide a sufficient control action for a highly nonlinear system. Here we use two algorithms of MPC like GPC and DMC scheme is designed and it is used to control the composition for a Bubble cap distillation column it's very difficult task it consists a combination of a ethanol and water system. Before that we obtain a mathematical model of distillation process is very complicated Now days one of the most vital System identification method use to implement the transfer functions model Here the Least square technique is used to estimate the parameters and build the accurate model of the Process. The MATLAB platform is used and implementation of the GPC and DMC, PID

Decentralized and distributed model predictive control (DMPC) addresses the problem of controlling a multivariable dynamical process, composed by several interacting subsystems and subject to constraints, in a computation and communication efficient way. Compared to a centralized MPC setup, where a global optimal control problem must be solved on-line with respect to all actuator commands given the entire set of states, in DMPC the control problem is divided into a set of local MPCs of smaller size, that cooperate by communicating each other a certain information set, such as local state measurements, local decisions, optimal local predictions. Each controller is based on a partial (local) model of the overall dynamics, possibly neglecting existing dynamical interactions. The global performance objective is suitably mapped into a local objective for each of the local MPC problems.

Designing a stabilized distributed model predictive control (DMPC) with constraints is an open and important problem for a class of large-scale distributed systems, which are composed by both weakly and strongly coupled subsystems. This paper proposes a design of DMPC network to stabilize this class of large-scale systems. A coupling degree-based clustering method is first designed to classify subsystems into some middle-scale subsystems (M-subsystem) off-line according to the adjacent matrix, so that these M-subsystems are weakly coupled with each other. Then, each M-subsystem is controlled by a virtual model predictive control (MPC), which is realized by several individual controllers with running iterative cooperative DMPC algorithm, since the solution of cooperative DMPC is able to converge to a fixed point without coupling constraints. Each MPC communicates with the corresponding interacted M-subsystems’ MPCs once in a control period for exchanging future state evolution estimation. All the subsystem-based MPCs are composed of the proposed peer-to-peer DMPC network. In addition, an additional consistency and stabilization constraints are added to guarantee the recursive feasibility and stability of the overall system. The convergence of the iterative DMPC algorithm for each M-subsystem and the stabilization analysis of the overall system are provided. The simulation results show the efficiency of the proposed method. Note to Practitioners —The proposed method is designed for the systems, which are composed of both strong and weak coupling subsystems and are controlled in a distributed structure. The aim of this method is to design a distributed model predictive control (MPC) network for the operational optimization of entire system. The designed control structure and the distributed MPC algorithm with the proposed method could guarantee the recursive feasibility and asymptotic stability of the entire system. To apply this method, the first step is to obtain the models of each subsystem. Then, design the distributed MPC network structure according to the proposed cluster algorithm, which determines the set of subsystems that each virtue MPC controls. Next step is to configure the network information exchanging among these subsystems and construct the optimization problem of each subsystem. Finally, use the existing quadratically constrained quadratic program solvers to calculate the optimal control laws and apply these optimal control laws to the corresponding subsystems. This method can be used in large-scale chemical processes, air conditioner systems for multizone buildings, distributed energy generation systems, and so on.

Simulation of irrigation control strategies for cotton using Model Predictive Control within the VARIwise simulation framework

Partitioned Based Cooperative Distributed Model Predictive Control for Large-Scale Nonlinear Systems

Decentralized machine-learning-based predictive control of nonlinear processes

This paper concerns a distributed model predictive control (DMPC) strategy in which each controller views the signals from other subsystems as disturbance inputs in its local model. The DMPC controllers exchange predictions on the bounds of their state trajectories and incorporate this information into their local DMPC problems. They also impose their own predicted state bounds as constraints in subsequent DMPC iterations to guarantee their subsystem satisfies the bounds broadcast to the other controllers. Each controller solves a local min-max problem on each iteration to optimize performance with respect to worst-case disturbances. Parameterized state feedback is introduced into the DMPC formulation to obtain less conservative solutions and predictions. The paper presents sufficient conditions for feasibility and stability. The approach is illustrated with an example.

In model predictive control, the control action is found at each sampling time by solving an online optimization problem. Computationally, this step is very demanding, especially if compared to the evaluation of traditional control laws. This has limited the application of model predictive control to systems with slow dynamics for many years. Recently, several methods have been proposed in the literature which promise a substantial reduction of the computation time by either running the computation in parallel (distributed model predictive control) or exploiting the problem structure (fast model predictive control). A combination of these methods has not yet been considered in the literature. To achieve this goal, different optimization techniques need to be employed at once. The order of how these methods are applied matters. This paper considers fast distributed model predictive control combining the alternating direction method of multipliers (ADMM), the interior point method (IPM) and the Riccati iteration for a particular class of multi-agent systems for which the order of the methods can be arbitrarily changed. This leads to two different solver schemes where a trade-off arises between computation time and number of communications required to reach consensus. A simplified problem involving the formation control of a fleet of vehicles is considered at the end.

Fil: Giovanini, Leonardo Luis. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Santa Fe. Instituto de Investigacion en Senales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingenieria y Ciencias Hidricas. Instituto de Investigacion en Senales, Sistemas e Inteligencia Computacional; Argentina

Combined environmental and economic dispatch of smart grids using distributed model predictive control

In this paper, we study the distributed model predictive control (MPC) of polytopic uncertain systems with quantised communication and packet dropouts. The model of the whole plant is divided into a certain number of incomplete subsystems. Due to the nature of the distributed control structure, there is generally a lack of information about the state of the overall system. Each subsystem shares its information with neighbour subsystems via reliable connection. Distributed MPC controllers are designed for each subsystem by solving the linear matrix inequalities optimisation problem. The distributed state feedback laws are quantised and transmitted via communication network. An iterative algorithm is presented to make coordination among distributed state feedback laws. The communication is assumed to be affected by random packet dropouts in a representation of Bernoulli distributed white sequences with known conditional probabilities. A case study is carried out to demonstrate the effectiveness of the proposed distributed MPC technique.

Due to the complex and non linear character, wastewater treatment process is difficult to be controlled. And the demand for removing the pollutant, especially for nitrogen (N) and phosphorus (P), as well as reducing the cost of wastewater treatment plant (WWTP) is an important research topic. This paper applies PI control and decentralised model predictive control control to the combined phosphorus removal Benchmark Simulation Model 1 (BSMI-P) wastewater treatment process to enhance the P removal. A default control strategy which contains two control loops and is similar to the one given by the original version of BSMI was tested on BSMI-P. In addition, linear models have been identified and model predictive controllers implemented for each one of these loops. The simulation results showed that the MPC controllers are able to the controller performance in all the three weather conditions.

This work focuses on a class of nonlinear control problems that arise when new control systems which may use networked sensors and/or actuators are added to already operating control loops to improve closed‐loop performance. In this case, it is desirable to design the pre‐existing control system and the new control system in a way such that they coordinate their actions. To address this control problem, a distributed model predictive control method is introduced where both the pre‐existing control system and the new control system are designed via Lyapunov‐based model predictive control. Working with general nonlinear models of chemical processes and assuming that there exists a Lyapunov‐based controller that stabilizes the nominal closed‐loop system using only the pre‐existing control loops, two separate Lyapunov‐based model predictive controllers are designed that coordinate their actions in an efficient fashion. Specifically, the proposed distributed model predictive control design preserves the stability properties of the Lyapunov‐based controller, improves the closed‐loop performance, and allows handling input constraints. In addition, the proposed distributed control design requires reduced communication between the two distributed controllers since it requires that these controllers communicate only once at each sampling time and is computationally more efficient compared to the corresponding centralized model predictive control design. The theoretical results are illustrated using a chemical process example. © 2009 American Institute of Chemical Engineers AIChE J, 2009

This is a position paper on the current state of the art in distributed Model Predictive Control (MPC) and our view on its future potential. We present results from a recent survey of 35 distributed MPC approaches. For this, we propose a way in which distributed MPC approaches can be categorized for comparison. We also link the potential that these approaches have to the domain of other fields such as integrated environmental optimal decision making and cyber-physical systems. Many challenges for realizing these links have to be faced. We present our views on how the advances in several highly active research domains could be used to overcome these challenges. As such, this paper is intended as a starting point for the exploration of various novel research cooperations.

This paper presents a decentralized Model Predictive Control (MPC) for Automatic Generation Control (AGC) of a realistic multi power system in Egypt with inherent nonlinearities. The Egyptian Power System (EPS) is decomposed into three dynamic subsystems which are non-reheat, reheat, and hydro power plants. Moreover, each subsystem has its private characteristics compared to the others. Therefore, each subsystem controller has been designed independently to guarantee the stability of the overall closed loop system. Hence, MPC is proposed for every subsystem separately to regulate the frequency and to track the load demands of EPS. The performance of the proposed decentralized MPC of each subsystem is compared with the centralized one at different operational scenarios. The results by nonlinear simulation MATLAB/SIMULINK for the generation control of EPS approves that the decentralized predictive model gives almost the same performance as the centralized one. However, the centralized model cannot address the large load disturbance and system parameters variation. In contrast, the decentralized MPC scheme is more robust and effective against all disturbances and operating conditions.