Responsive Economic Model Predictive Control for Next-Generation Manufacturing

There is an increasing push to make automated systems capable of carrying out tasks which humans perform, such as driving, speech recognition, and anomaly detection. Automated systems, therefore, are increasingly required to respond to unexpected conditions. Two types of unexpected conditions of relevance in the chemical process industries are anomalous conditions and the responses of operators and engineers to controller behavior. Enhancing responsiveness of an advanced control design known as economic model predictive control (EMPC) (which uses predictions of future process behavior to determine an economically optimal manner in which to operate a process) to unexpected conditions of these types would advance the move toward artificial intelligence properties for this controller beyond those which it has today and would provide new thoughts on interpretability and verification for the controller. This work provides theoretical studies which relate nonlinear systems considerations for EMPC to these higher-level concepts using two ideas for EMPC formulations motivated by specific situations related to self-modification of a control design after human perceptions of the process response are received and to controller handling of anomalies

On Accounting for Equipment-Control Interactions in Economic Model Predictive Control via Process State Constraints

Traditionally, chemical processes have been operated at steady-state; however, recent work on economic model predictive control (EMPC) has indicated that some processes may be operated in a more economically-optimal fashion under a time-varying operating policy. It is unclear how time-varying operating policies may impact process equipment, which must be investigated for safety and profit reasons. It has traditionally been considered that constraints on process states can be added to EMPC design to prevent the controller from computing control actions which create problematic operating conditions for process equipment. However, no rigorous investigation has yet been performed to analyze whether, when a process is operated in a time-varying fashion, constraints on the process states (rather than states of the equipment behavior itself) are the most appropriate way of preventing unsafe conditions. In this work, we investigate the use of process state constraints for preventing equipment damage due to the operating conditions set up by an EMPC over time when the equipment behavior is modeled within a context based on forces, deformation, and fracture. Through a chemical process example, we elucidate that there are situations in which process state constraints are likely to be adequate for use in preventing an EMPC from setting up operating conditions that may not be desirable, but that there also may be situations when process state constraints are not adequate and constraints on equipment states may be an alternative. We elucidate a number of challenges that remain to be addressed for this proposed method to be practical

State Measurement Spoofing Prevention through Model Predictive Control Design

Security of chemical process control systems against cyberattacks is critical due to the potential for injuries and loss of life when chemical process systems fail. A potential means by which process control systems may be attacked is through the manipulation of the measurements received by the controller. One approach for addressing this is to design controllers that make manipulating the measurements received by the controller in any meaningful fashion very difficult, making the controllers a less attractive target for a cyberattack of this type. In this work, we develop a model predictive control (MPC) implementation strategy that incorporates Lyapunov-based stability constraints and can allow several potential control laws to be available to apply to the process, one of which can be randomly selected at each sampling time, potentially making the response of the controller to a false state measurement more difficult to predict a priori. We investigate closed-loop stability and recursive feasibility of the resulting control design, and utilize a benchmark chemical process example to demonstrate the difference in the control actions computed by such a randomized MPC implementation strategy compared with those for the same process by the same MPC design utilized at every sampling time

Economic Model Predictive Control and Process Equipment: Control-Induced Thermal Stress in a Pipe

Recent work on economic model predictive control (EMPC) has indicated that some processes may be operated in a more economically-optimal fashion under a time-varying operating policy than under a steady-state operating policy. However, a concern for time-varying operation is how such a change in operating policy might impact the equipment within which the processes being controlled are carried out. While under steady-state operation, the operating conditions to which equipment would regularly be exposed can be estimated, this would be more difficult to assess thoroughly a priori under time-varying operation. It could be explored whether the EMPC could be made aware of any impacts the control actions that it chooses might have on equipment, and then to seek to impose constraints on these impacts. This would require explicit consideration of equipment design, material properties/behavior, and material loading at the EMPC design stage. This work provides an initial exploration of this topic by seeking to extract principles related to the integration of equipment material fidelity considerations and EMPC through an example accounting for a simple preliminary case of thermal stresses in a pipe at equilibrium conditions

Interactions between Control and Process Design under Economic Model Predictive Control

conomic model predictive control (EMPC) is a model-based control scheme that integrates process control and economic optimization, which can potentially allow for time-varying operating policies to maximize economic performance. The manner in which an EMPC operates a process to optimize economics depends on the process dynamics, which are fixed by the process design. This raises the question of how process and EMPC designs interact. Works which have addressed process and control design interactions for steady-state operation have sought to simultaneously develop process designs and control law parameters to find the most profitable way to operate a process that is able to prevent process constraints from being violated and to optimize capital costs in the presence of disturbances. Because EMPC has the potential to operate a process in a transient fashion, this work first focuses on how EMPC and process design interact in the absence of disturbances. Using small-scale process examples, we seek to understand the fundamental nature of the interactions between EMPC and process design, including how these interactions can impact computational complexity of the controller and the design procedure. We subsequently utilize the insights gained to suggest controller design variables which might be considered as decision variables for a simultaneous process and control design problem when disturbances are considered

Economic Model Predictive Control Design via Nonlinear Model Identification

Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an EMPC may be utilized to gather non-routine operating data that ideally provides insights on the process physics and thereby allows physics-based process models to be developed on-line. These models can then be used to update the model, objective function, and constraints of the controller. Closed-loop stability and recursive feasibility considerations are discussed for the proposed EMPC design, and the controller\u27s application is illustrated through a chemical process example

Economic Model Predictive Control Design via Nonlinear Model Identification

Increasing pushes toward next-generation/smart manufacturing motivate the development of economic model predictive control (EMPC) designs which can be practically deployed. For EMPC, the constraints, objective function, and accuracy of the state predictions would benefit from process models that describe the process physics. However, obtaining first- principles models of chemical process systems can be time-consuming or challenging such that it is preferable to develop physics-based process models automatically from process operating data. In this work, we take initial steps in this direction by suggesting that because experiments that are used to characterize first-principles models often target specific types of data, an EMPC may be utilized to gather non-routine operating data that ideally provides insights on the process physics and thereby allows physics-based process models to be developed on-line. These models can then be used to update the model, objective function, and constraints of the controller. Closed-loop stability and recursive feasibility considerations are discussed for the proposed EMPC design, and the controller\u27s application is illustrated through a chemical process example

Mitigating Safety Concerns and Profit/Production Losses for Chemical Process Control Systems Under Cyberattacks via Design/Control Methods

One of the challenges for chemical processes today, from a safety and profit standpoint, is the potential that cyberattacks could be performed on components of process control systems. Safety issues could be catastrophic; however, because the nonlinear systems definition of a cyberattack has similarities to a nonlinear systems definition of faults, many processes have already been instrumented to handle various problematic input conditions. Also challenging is the question of how to design a system that is resilient to attacks attempting to impact the production volumes or profits of a company. In this work, we explore a process/equipment design framework for handling safety issues in the presence of cyberattacks (in the spirit of traditional HAZOP thinking), and present a method for bounding the profit/production loss which might be experienced by a plant under a cyberattack through the use of a sufficiently conservative operating strategy combined with the assumption that an attack detection method with characterizable time to detection is available

Data-Based Nonlinear Model Identification in Economic Model Predictive Control

Many chemical/petrochemical processes in industry are not completely modeled from a first-principles perspective because of the complexity of the underlying physico-chemical phenomena and the cost of obtaining more accurate, physically relevant models. System identification methods have been utilized successfully for developing empirical, though not necessarily physical, models for advanced model-based control designs such as model predictive control (MPC) for decades. However, a fairly recent development in MPC is economic model predictive control (EMPC), which is an MPC formulated with an economics-based objective function that may operate a process in a dynamic (i.e., off steady-state) fashion, in which case the details of the process model become important for obtaining sufficiently accurate state predictions away from the steady-state, and the physics and chemistry of the process become important for developing meaningful profit-based objective functions and safety-critical constraints. Therefore, methods must be developed for obtaining physically relevant models from data for EMPC design. While the literature regarding developing models from data has rapidly expanded in recent years, many new techniques require a model structure to be assumed a priori, to which the data is then fit. However, from the perspective of developing a physically meaningful model for a chemical process, it is often not obvious what structure to assume for the model, especially considering the often complex nonlinearities characteristic of chemical processes (e.g., in reaction rate laws). In this work, we suggest that the controller itself may facilitate the identification of physically relevant models online from process operating data by forcing the process state to nonroutine operating conditions for short periods of time to obtain data that can aid in selecting model structures believed to have physical significance for the process and, subsequently, identifying their parameters. Specifically, we develop EMPC designs for which the objective function and constraints can be changed for short periods of time to obtain data to aid in model structure selection. For one of the developed designs, we incorporate Lyapunov-based stability constraints that allow closed-loop stability and recursive feasibility to be proven even as the online “experiments” are performed. This new design is applied to a chemical process example to demonstrate its potential to facilitate physics-based model identification without loss of closed-loop stability. This work therefore reverses a question that has been of interest to the control community (i.e., how new techniques for developing models from data can be useful for control of chemical processes) to ask how control may be utilized to impact the use of these techniques for the identification of physically relevant process dynamic models that can aid in improving process operation and control for economic and safety purposes

Integrated Cyberattack Detection and Resilient Control Strategies using Lyapunov-Based Economic Model Predictive Control

The use of an integrated system framework, characterized by numerous cyber/physical components (sensor measurements, signals to actuators) connected through wired/wireless networks, has not only increased the ability to control industrial systems, but also the vulnerabilities to cyberattacks. State measurement cyberattacks could pose threats to process control systems since feedback control may be lost if the attack policy is not thwarted. Motivated by this, we propose three detection concepts based on Lyapunov‐based economic model predictive control (LEMPC) for nonlinear systems. The first approach utilizes randomized modifications to an LEMPC formulation online to potentially detect cyberattacks. The second method detects attacks when a threshold on the difference between state measurements and state predictions is exceeded. Finally, the third strategy utilizes redundant state estimators to flag deviations from “normal” process behavior as cyberattacks