Model Predictive Control Architecture Research Articles

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control (CMPC) strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

Stochastic control optimization for a mixed mode building considering occupant window opening behaviour

A methodology has been developed for optimizing building supervisory control strategies, employing building models that incorporate stochastic models of occupant behaviour and serve as the objective function evaluator in a stochastic model predictive control (SMPC) architecture. The SMPC architecture accounts for variability in building performance due to occupant behaviour and is shown to generate a sequence of automatic window opening decisions for a mixed mode building which lead to more robust building performance in the face of occupant window use than a heuristic controller. A set of receding optimization time horizons are described which enable the use of complex building models in simulated SMPC. Results of a case study show that deterministic optimization predicts a 50% increase in building performance, while stochastic optimization leads to a more conservative and more reliable 33% performance improvement, which takes into consideration the impact of occupant behaviour.

Trajectory Tracking of Batch Product Quality Using Latent Variable Models

A practical strategy for controlling batch product quality evolution by means of latent variable models and intermittent measurements is presented. The methodology is based on the identification of data-based models using multivariate statistical methods such as Partial Least Squares (PLS). PLS is able to identify models with a reduced number of latent variables, which account for most of the process variability. The data-based models are employed along with a moving window strategy in order to predict product quality throughout the batch operating time. The predictions can be utilized within a Model Predictive Control (MPC) architecture so that trajectory tracking control can be directly applied to batch product quality. A simulated example of fed-batch aerobic growth of Saccharomyces Cerevisiae is used to demonstrate the capabilities of the proposed trajectory tracking controller.

Distributed Supervisory Predictive Control of Distributed Wind and Solar Energy Systems

In this work, we design a distributed supervisory model predictive control (MPC) system for optimal management and operation of distributed wind and solar energy generation systems integrated into the electrical grid to facilitate the development of the so-called “smart electrical grid”. We consider a topology in which two spatially distributed energy generation systems, a wind subsystem and a solar subsystem, are integrated in a DC power grid, providing electrical power to a local area, and each subsystem is coupled with an energy storage device. A supervisory MPC optimization problem is first formulated to take into account optimality considerations on system operation and battery maintenance; then a sequential and an iterative distributed supervisory MPC architectures are developed to coordinate the actions of the subsystems accordingly. Simulations of 24-hour system operation are carried out under the different control architectures to demonstrate the applicability and effectiveness of the distributed supervisory predictive control design.

Distributed model predictive control of switched nonlinear systems with scheduled mode transitions

A method for the design of distributed model predictive control (DMPC) systems for a class of switched nonlinear systems for which the mode transitions take place according to a prescribed switching schedule is presented. Under appropriate stabilizability assumptions on the existence of a set of feedback controllers that can stabilize the closed‐loop switched, nonlinear system, a cooperative DMPC architecture using Lyapunov‐based model predictive control (MPC) in which the distributed controllers carry out their calculations in parallel and communicate in an iterative fashion to compute their control actions is designed. The proposed DMPC design is applied to a nonlinear chemical process network with scheduled mode transitions and its performance and computational efficiency properties in comparison to a centralized MPC architecture are evaluated through simulations. © 2013 American Institute of Chemical Engineers AIChE J, 59:860‐871, 2013

Flexible operation of solvent regeneration systems for CO2 capture processes using advanced control techniques: Towards operational cost minimisation

We present a novel approach for the control of the solvent regeneration system of amine-based CO2 capture processes. This relies on the real-time evaluation of the cost-optimal extent of solvent regeneration at given energy and CO2 prices. To achieve this, a multilevel, model predictive control architecture is proposed and developed for this system. The low-level control regulates the level of solvent inside the reboiler and the power supplied to the system, while the high-level control regulates the extent of the solvent regeneration in order to minimize the operating cost. This approach ensures the safe operation of the system while concurrently enhancing process flexibility. We demonstrate that this new technique can result in a reduction of up to 10% in energy cost required for solvent regeneration. A simulator tool developed in Matlab™ is available on request.

Composite fast‐slow MPC design for nonlinear singularly perturbed systems

AbstractThe design of a composite control system for nonlinear singularly perturbed systems using model predictive control (MPC) is described. Specifically, a composite control system comprised of a “fast” MPC acting to regulate the fast dynamics and a “slow” MPC acting to regulate the slow dynamics is designed. The composite MPC system uses multirate sampling of the plant state measurements, i.e., fast sampling of the fast state variables is used in the fast MPC and slow‐sampling of the slow state variables is used in the slow MPC. Using singular perturbation theory, the stability and optimality of the closed‐loop nonlinear singularly perturbed system are analyzed. A chemical process example which exhibits two‐time‐scale behavior is used to demonstrate the structure and implementation of the proposed fast–slow MPC architecture in a practical setting. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1802–1811, 2012

Sequential and iterative architectures for distributed model predictive control of nonlinear process systems

AbstractIn this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one‐directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi‐directional communication strategy, are evaluated in parallel and iterate to improve closed‐loop performance. In the design of the distributed model predictive controllers, Lyapunov‐based model predictive control techniques are used. To ensure the stability of the closed‐loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov‐based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed‐loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010

A System-on-a-Chip Implementation for Embedded Real-Time Model Predictive Control

This paper presents a hardware architecture for embedded real-time model predictive control (MPC). The computational cost of an MPC problem, which relies on the solution of an optimization problem at every time step, is dominated by operations on real matrices. In order to design an efficient and low-cost application-specific processor, we analyze the computational cost of MPC, and we propose a limited-resource host processor to be connected with an application-specific matrix coprocessor. The coprocessor uses a 16-b logarithmic number system arithmetic unit, which is designed using cotransformation, to carry out the required arithmetic operations. The proposed architecture is implemented by means of a hardware description language and then prototyped and emulated on a field-programmable gate array. Results on computation time and architecture area are presented and analyzed, and the functionality of the proposed architecture is verified using two case studies: a linear problem of a rotating antenna and a nonlinear glucose-regulation problem. The proposed MPC architecture yields a small-in-size and energy-efficient implementation that is capable of solving the aforementioned problems on the order of milliseconds, and we compare its performance and area requirements with other MPC designs that have appeared in the literature.

Constrained Model Predictive Control for Nonholonomic Vehicle Regulation Problem

A model predictive control architecture based on discrete time nonlinear car model is derived to solve regulation(“parking”) problem. Parameters of the proposed controller are chosen by considering terminal state constraints. This setup combined with terminal state penalty in the cost function could assure control stability. The generated trajectory satisfies minimum curvature requirements and actuator saturations of the vehicle are considered in controller design. Obstacle avoidance is realized by considering distance constraints in the open-loop optimization process. Simulation results are given to illustrate the feasibility of the proposed control architecture.

Dantzig–Wolfe decomposition and plant-wide MPC coordination

Due to the enormous success of model predictive control (MPC) in industrial practice, the efforts to extend its application from unit-wide to plant-wide control are becoming more widespread. In general, industrial practice has tended toward a decentralized MPC architecture. Most existing MPC systems work independently of other MPC systems installed within the plant and pursue a unit/local optimal operation. Thus, a margin for plant-wide performance improvement may be available beyond what decentralized MPC can offer. Coordinating decentralized, autonomous MPC has been identified as a practical approach to improving plant-wide performance. In this work, we propose a framework for designing a coordination system for decentralized MPC which requires only minor modification to the current MPC layer. This work studies the feasibility of applying Dantzig–Wolfe decomposition to provide an on-line solution for coordinating decentralized MPC. The proposed coordinated, decentralized MPC system retains the reliability and maintainability of current distributed MPC schemes. An empirical study of the computational complexity is used to illustrate the efficiency of coordination and provide some guidelines for the application of the proposed coordination strategy. Finally, two case studies are performed to show the ease of implementation of the coordinated, decentralized MPC scheme and the resultant improvement in the plant-wide performance of the decentralized control system.

A decomposition approach to multi-vehicle cooperative control

We use a decomposition approach to generate cooperative strategies for a class of multi-vehicle control problems. By introducing a set of tasks to be completed by the team of vehicles and a task execution method for each vehicle, we decompose the problem into a combinatorial component and a continuous component. The continuous component of the problem is captured by task execution, and the combinatorial component is captured by task assignment. In this paper, we present a solver for task assignment that generates near-optimal assignments quickly and can be used in real-time applications. To motivate our methods, we apply them to an adversarial game between two teams of vehicles. One team is governed by simple rules and the other by our algorithms. In our study of this game we found phase transitions, showing that the task assignment problem is most difficult to solve when the capabilities of the adversaries are comparable. Finally, we utilize our algorithms in a hierarchical model predictive control architecture with a variable replanning rate at each level to provide feedback in dynamically changing and uncertain environments.

Vision system for model based control of cryogenic tunnel freezers

This paper presents a novel solution, using a vision-sensor, to a challenging control problem in cryogenic food freezing industry. This industrial application is characterized by significant variation in input food products because cryogenics-freezing technology with its inherent process flexibility is typically used in low volume/high mix applications. Current industrial controllers use PLCs for regulating the belt speed of the tunnel, which leads to conservative set-points and consequently significant operational cost and frequent over-freezing. Servo control of the process is difficult because of the complicated non-linear dynamics of cryogenic freezing caused by phase-change, and thermal dynamics between the frozen products and the tunnel. The solution presented in this paper uses a vision-sensor to estimate the shape, size, and heat load of food products that will enter the freezing tunnel. An analysis of the sensor location and its impact on disturbance feed-forward control is also presented. Efficacies of these developments are verified in an industrial case study using a commodity webcam for capturing and processing two-dimensional streaming images, and integrating the processed information with an industrial control system using model-predictive control architecture. The proposed solution is especially attractive for the food industry because of the low-cost and non-contact features of webcam, operational cost savings through reduced consumption of cryogen, and improved quality through reduction in variation of temperature of the frozen products.

Developing a robust model predictive control architecture through regional knowledge analysis of artificial neural networks

Abstract Chemical processes are nonlinear. Model based control schemes such as model predictive control are highly related to the accuracy of the process model. For a highly nonlinear chemical system, it is clear to implement a nonlinear empirical model, such as artificial neural network model, should be superior to a linear model such as dynamic matrix model. However, unlike linear systems, the accuracy of a nonlinear empirical model strongly depends on its original data or training data based on how the model is built up. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. New input patterns that imply extrapolations and thus unreliable prediction by an artificial neural network model can be recognized from a significant decrease in the regional-knowledge index. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The present state of the controlled process and the model fitness to the present input pattern determine the weightings of the controller's output. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system.