Infinite Horizon Model Predictive Control Research Articles

Embedded MPC Strategies for ESP-Lifted Oil Wells: Hardware-in-the-Loop Performance Analysis of Nonlinear and Robust Techniques

This paper proposes embedded model predictive control strategies for oil-production processes equipped with electric submersible pump (ESP) installations. The novelty of this paper is the robustness and computational performance analysis of the Robust Infinite-Horizon Model Predictive Controller (RIHMPC) and Nonlinear Model Predictive Controller (NMPC) strategies, which have not yet been documented by the oil and gas exploration and production literature. The proposed method to embed the control laws is flexible with different hardware and is based on automatic code generation, which facilitates the project workflow. Hardware-in-the-loop simulation cases were used to compare the performance of both control strategies embedded in the Teensy 4.1 microcontroller, using key indices for real applications. The results showed that the RIHMPC strategy is a very promising alternative for real-time operation in ESP-lifted oil wells, with overall performance similar to the NMPC controller, even in noisy and plant–model mismatch scenarios, and using only linear models in its formulation.

Advanced process control system with MPC as a new approach for layer of protection analysis

Layer of protection analysis (LOPA) is a widely used method to support process safety in the chemical industries. In the LOPA, the process is classified into many layers, one of such layers considers the basic process control system (BPCS) which commonly uses PID controllers. This kind of controllers cannot deal with constraints. For this reason, the main purpose of this work is to provide a framework to enhance the control layer in the LOPA, which consists of a model predictive control (MPC) with safety features. These features include: sublayers in the controller system (such as real time optimization, target calculation, and MPC), safety constraints, and guarantee of stability by adopting an Infinite Horizon MPC (IHMPC). Here, we propose an approach for control-inspired view to process safety, replacing the BPCS by an Advanced Process Control System (APCS). Moving forward with these concepts, first, a literature review emphasizes the content, showing two perspectives for the APCS. The APCS is designed for two varieties of controllers, a basic IHMPC and IHMPC with zone control to compare the performance. In this framework, the first sublayer consists of a real time optimization (RTO) structure, that calculates the optimal operating condition for the process controller, which computes the control action. Besides, RTO has an additional constraint called the safety index, based on the protection of process operational. RTO and basic IHMPC communicate directly, while for IHMPC with zone control there is an inner sublayer called Target Calculation, it computes a feasible target to the controller, working as another safety strategy in APCS. After that, we demonstrate both structures applied to a CSTR reactor. From the case study, we compared both controllers, and evaluated the effect that the safety index constraint causes in the setpoints, outputs, and control actions. The use of safety constraint in RTO proved to be a safe strategy for the control layer, as well as IHMPC with zone control presented a safer profile than basic IHMPC. Furthermore, the results show that safety constraint affect the economic goal, decreasing its value.

Embedded predictive controller based on fuzzy linear parameter-varying model: A hardware-in-the-loop application to an ESP-lifted oil well system

This work addresses the development of a fuzzy infinite horizon model predictive control (FIHMPC) strategy and its application to an ESP-lifted oil well system in an embedded hardware. The proposed scheme is based on a fuzzy model to approximate the nonlinear system to a linear parameter-varying model and incorporate it into the proposed MPC law. Slacked terminal constraints and zone control scheme are incorporated to the FIHMPC controller assuring optimization feasibility to handle ESP time-varying operation envelope. The implementable zone FIHMPC controller is applied through an ESP32 micro-controller to ESP-lifted oil well system equations, executed in MATLAB, composing a hardware-in-the-loop (HIL) simulation scheme. Nonlinear mismatch scenarios with unmeasured disturbances show the effectiveness of the proposed controller over multiple operating conditions. Computational memory and processing analysis over simulations also validates the controller application.

A novel linear matrix inequality‐based robust event‐triggered model predictive control for a class of discrete‐time linear systems

AbstractThis paper studies a linear matrix inequality (LMI)‐based robust event‐triggered model predictive control (ET‐MPC) for a class of discrete linear time‐invariant systems subject to bounded disturbances. In the presented robust event‐triggered MPC, the event‐triggered mechanism is first set by the deviation between the optimal state and actual state. In addition, the Lyapunov‐based stability condition is employed in the constraints for simplifying parameters and enhancing the universality. Subsequently, a novel design framework that involves LMIs approach is developed. In such a framework, the Lyapunov weight matrix is predesigned by solving a convex optimization problem with LMIs and the infinite horizon MPC optimization problem is also transformed as LMIs such that the computational complexity is significantly reduced. To guarantee robust constraint satisfaction, a dual‐mode control strategy is adopted. In this way, the proposed robust ET‐MPC has the ability to deal with the constrained system. Furthermore, the theoretical analysis of the recursive feasibility and stability is provided. The numerical simulations and comparison studies demonstrate that the proposed robust ET‐MPC not only has satisfying control performance but also significantly reduces the computational burden.

An Adaptive Infinite Horizon Model Predictive Control Strategy Applied to an ESP-lifted Oil Well System

This work presents the benefits of using an adaptive model predictive control approach for controlling an ESP-lifted oil well system. The idea behind the proposed scheme is the successive linearization of the plant nonlinear model and incorporate it into an infinite-horizon MPC formulation, which handles feasibility issues by considering slacked terminal constraints. Also, suitable use of a zone control strategy to deal with time-varying ESP operating envelope constraints (downthrust and upthrust) becomes the approach implementable in practice. Nonlinear plant-model mismatch scenarios, including simulation of unmeasured disturbances and the tracking of ESP operation economic targets, show the effectiveness of the proposed controller concerning different operating conditions of the oil production process equipped with ESP.

Closed-loop dynamic real-time optimization (CL-DRTO) of a bioethanol distillation process using an advanced multilayer control architecture

The ethanol production has become an important part of the worldwide economy driven by its use as renewable energy and environmentally clean fuel. To obtain a high-level quality product, a large quantity of energy is used in the distillation stage. This work proposes to optimize the bioethanol production process applying a closed-loop dynamic real-time optimization (CL-DRTO) framework associated with advanced control strategies in the ethanol distillation facilities to improve production and minimize energy losses. A high-fidelity computational platform is developed using a software-in-the-loop (SIL) technique to demonstrate the feasible application in real cases. OLE (Object Linking and Embedding) Automation integrates Matlab and Aspen Hysys software to simulate practical scenarios currently applied in the industry. The optimization and control algorithms are developed in Matlab and the ethanol distillation process is modeled in the Aspen Hysys. Different advanced control strategies, such as IHMPC (Infinite Horizon Model Predictive Control), MIMO FSP (Filtered Smith-Predictor) and DTCGPC (Dead-Time Compensator Generalized Predictive Controller), are used to overcome the process dynamic issues, for instance, strong nonlinearity, dead-times, long time constants and coupled loops, on ethanol distillation process. A CL-DRTO layer is developed in the proposed control structure to consider economic and production aspects, guiding the process to optimal operating points. Performance indices and computational effort are used to evaluate the control behavior for disturbance rejection and reference tracking scenarios. Furthermore, economic performance is analyzed to ensure the advantages of the proposal. Results show that the proposed computational platform is able to reproduce industrial scenarios with high-fidelity. In addition, preliminary results demonstrate that advanced control structure can improve the production and profitability of the bioethanol distillery and present a powerful alternative to replace classical controllers currently used in this type of industry.

Simultaneous Process Design and Control of the Williams–Otto Reactor Using Infinite Horizon Model Predictive Control

In this work, we study a simultaneous process design and control methodology using infinite horizon model predictive control (IHMPC). The methodology is based on the change in market conditions cau...

Model predictive control of resistive wall mode for ITER

Active feedback stabilization of the dominant resistive wall mode (RWM) for an ITER H-mode scenario at high plasma pressure using infinite-horizon model predictive control (MPC) is presented. The MPC approach is closely-related to linear-quadratic-Gaussian (LQG) control, improving the performance in the vicinity of constraints. The control-oriented model for MPC is obtained with model reduction from a high-dimensional model produced by CarMa code. Due to the limited time for on-line optimization, a suitable MPC formulation considering only input (coil voltage) constraints is chosen, and the primal fast gradient method is used for solving the associated quadratic programming problem. The performance is evaluated in simulation in comparison to LQG control. Sensitivity to noise, robustness to changes of unstable RWM dynamics, and size of the domain of attraction of the initial conditions of the unstable modes are examined.

An implementable stabilizing model predictive controller applied to a rotary flexible link: An experimental case study

This work addresses the application of implementable stabilizing model predictive control (MPC) strategies to a rotary flexible link (RFL). Despite their practice usefulness and design simplicity, the implementation of these stable MPC controllers with guaranteed feasibility in a real experiment, and dedicated to dynamic features of RFL systems, have not yet been documented by the literature. In contrast to conventional finite-horizon MPC strategies, infinite-horizon MPC (IHMPC) techniques ensure nominal closed-loop stability irrespective of the cost function parameters. Also, if these IHMPC strategies have feasible-optimization problem based formulations, such as implementable ones investigated here, their applicability naturally becomes attractive to the industry. Simulation and experimental results illustrate the usefulness of the implementable stabilizing MPC controllers when compared to non-feasible in practice ones, such as a classical infinite-horizon MPC and a conventional finite-horizon generalized predictive controller, thus ensuring their benefits in terms of performance and computational burden in the context of constrained RFL mechatronic systems.

Novel Switch Stabilizing Model Predictive Control Strategy Applied in the Control of a Simulated Moving Bed for the Separation of Bi-Naphthol Enantiomers

The control of cyclic processes is an open issue in the literature because of their very peculiar dynamic behavior. Thus, deeper studies about this problem are necessary in order to promote an efficient operation of these processes at the industrial level. This work presents a nominally stabilizing MPC controller, also known as infinite horizon model predictive control (IHMPC) applied in the control of a simulated moving bed (SMB) process where bi-naphthol enantiomers are separated. A novel advanced control strategy is presented in the field of SMB process control, while grounds are provided for further developments in the field of cyclic process dynamics and control. The IHMPC performance is evaluated in terms of feasibility and robustness in a realistic nonlinear plant-model mismatch scenario. It is tested in terms of both unmeasured disturbance rejection and conflicting output-tracking scenarios. The results presented indicate that even though the conventional finite horizon-based MPC controller is able to control the process around its optimal point, far from this condition, the conventional MPC loses the process track. On the other hand, the IHMPC controller performed well the process control in all conditions evaluated, far and close to the design conditions. This demonstrates the ability of an IHMPC-like stabilizing control law not only for controlling the system but also for improving the robustness in plant-model mismatch scenarios by considering an infinite prediction horizon.

Infinite horizon model predictive control tracking application to helicopters

A novel formulation for infinite horizon model predictive control that is general and can accurately approximate and minimize the control cost is proposed. This method allows output tracking over a finite horizon when constraints are considered and equilibrium tracking in the linearized dynamics after that for the infinite horizon when constraints are not considered. The method allows prescribing weights simultaneously on the output tracking error, control rates, and control energy. Methods of weight selection are discussed to ensure strong cost convexity for reliable optimization. The control method is used to bring a helicopter that is far from a ship to a stable flight condition that is much closer to the ship. This is very important for helicopter ship landing automation.

Dual-Mode Model Predictive Control of an Omnidirectional Wheeled Inverted Pendulum

This article describes the position and heading control of a novel form of omnidirectional wheeled inverted pendulum platform known as a collinear Mecanum drive. This concept uses four collinear Mecanum wheels to balance in a similar manner to a typical two-wheeled inverted pendulum while also being able to simultaneously translate directly along its balance axis. Control is performed using a constrained time-optimal infinite horizon model predictive controller, with feasibility maintained across the full reference input set. Explored in this article is the derivation of the system dynamics model and controller, a systematic approach to selection of controller parameters and analysis of their effect on control performance and complexity, and an evaluation of the controller's efficacy in both simulation and on a real-world experimental prototype for simple and complex trajectories.

A robustly model predictive control strategy applied in the control of a simulated industrial polyethylene polymerization process

This work investigates the quality control problem of polyethylene grades produced in an industrial polymerization plant. The plant is represented by a phenomenological model previously validated using industrial data. Three advanced control strategies were developed in order to propose strategies capable of dealing with different grades of products. A stability guaranteed [IHMPC (Infinite Horizon Model Predictive Control)] technique and a robust stabilized [RIHMPC (Robust IHMPC)] technique were designed and compared with a conventional MPC. The results show that RIHMPC outperformed the other techniques, providing smoother and faster grade transitions and setpoint tracking. This result provides a significative contribution, since no report of application of RIHMPC on polymerization plants was found. The RHIMPC controller seems to be a promising option for other polymerization reactor control problems in which the diversity of resins produced, coupled with the nonlinear characteristic of the system, lend themselves to the use of different linear models.

A modelling and predictive control approach to linear two-stage inverted pendulum based on RBF-ARX model

The Underactuated, Fast-responding, Nonlinear and Unstable (UFNU) system is a typical hard-to-control plant, such as multi-stage inverted pendulum (IP). This paper considers the modelling and stabilisation control of a Linear Two-Stage IP (LTSIP). To avoid the problems resulted from using first principle model this paper uses a data-driven approach to building a State-Dependent AutoRegressive eXogenous (SD-ARX) model without offset term, whose coefficients are approximated by Radial Basis Function (RBF) neural networks, to describe the LTSIP. Based on the RBF-ARX model, an infinite horizon Model Predictive Control (MPC) strategy is proposed to control the LTSIP plant, which is designed by using the locally linearised model obtained from the RBF-ARX model, and obtaining the locally optimal state feedback control law at each control period. Stability of the close loop system is proved. Real-time control experimental results demonstrate that the proposed modelling and control method is effective in modelling and controlling the UFNU system.

Application of Model Predictive Control to a Continuous Multiple‐Effect Crystallizer

AbstractAn infinite horizon model predictive controller (IHMPC) with zone control is applied to a continuous five‐effect evaporative sodium chloride crystallizer. Firstly, a phenomenological dynamic model of the process is developed considering mass, energy, and moment balances coupled to crystallization kinetics. The developed model plays the role of the real system in order to study the proposed optimization/control strategy. The proposed approach is compared to a classical proportional integral derivative (PID) control system. The control strategy based on the prediction of the future state of the plant provides a faster response, a better stability to the process, and a reduction in energy consumption.

Combining Short-Term and Long-Term Reservoir Operation Using Infinite Horizon Model Predictive Control

AbstractModel predictive control (MPC) can be employed for optimal operation of adjustable hydraulic structures. MPC selects the control to be applied to the system by solving an optimization problem over a finite horizon in real-time. The horizon finiteness is both the reason for MPC’s success and its main limitation. MPC has in fact been successfully employed for short-term reservoir management. Short-term reservoir management deals effectively with fast processes, such as flood, but it is not capable of looking sufficiently ahead to handle long-term issues, such as drought. This study proposes an infinite horizon MPC solution that deals with both short and long-term objectives, tailored for reservoir management. In the proposed solution, the control signal is structured by the use of basis functions. Basis functions reduce the optimization argument to a small number of variables, making the control problem solvable in a reasonable time. The solution is tested for the operational management of Manantali...

REALIGNED MODEL PREDICTIVE CONTROL OF A PROPYLENE DISTILLATION COLUMN

In the process industry, advanced controllers usually aim at an economic objective, which usually requires closed-loop stability and constraints satisfaction. In this paper, the application of a MPC in the optimization structure of an industrial Propylene/Propane (PP) splitter is tested with a controller based on a state space model, which is suitable for heavily disturbed environments. The simulation platform is based on the integration of the commercial dynamic simulator Dynsim® and the rigorous steady-state optimizer ROMeo® with the real-time facilities of Matlab. The predictive controller is the Infinite Horizon Model Predictive Control (IHMPC), based on a state-space model that that does not require the use of a state observer because the non-minimum state is built with the past inputs and outputs. The controller considers the existence of zone control of the outputs and optimizing targets for the inputs. We verify that the controller is efficient to control the propylene distillation system in a disturbed scenario when compared with a conventional controller based on a state observer. The simulation results show a good performance in terms of stability of the controller and rejection of large disturbances in the composition of the feed of the propylene distillation column.

無限ホライズンモデル予測制御の計算負荷評価

無限ホライズンモデル予測制御の計算負荷評価

Model predictive control of P-time event graphs

ABSTRACTThis paper deals with model predictive control of discrete event systems modelled by P-time event graphs. First, the model is obtained by using the dater evolution model written in the standard algebra. Then, for the control law, we used the finite-horizon model predictive control. For the closed-loop control, we used the infinite-horizon model predictive control (IH-MPC). The latter is an approach that calculates static feedback gains which allows the stability of the closed-loop system while respecting the constraints on the control vector. The problem of IH-MPC is formulated as a linear convex programming subject to a linear matrix inequality problem. Finally, the proposed methodology is applied to a transportation system.

Study of the implementation of a robust MPC in a propylene/propane splitter using rigorous dynamic simulation

This work addresses the application of the Robust Infinite Horizon Model Predictive Control (RIHMPC) to a heat integrated propylene distillation system at a Petrobras refinery. The approach proposed here is tested on the rigorous dynamic simulation software (Dynsim®) that reproduces the system as a virtual plant and is able to communicate with the MPC algorithms developed in Matlab, through an Open Platform Communication (OPC) interface. The controller is based on a minimal order state‐space model that is equivalent to the system step response and considers the zone control of the outputs and optimizing targets for the inputs. The optimizing targets are obtained through the steady‐state economic optimization using the real‐time optimization package (ROMeo®1). The proposed integration approach provides convergence and stability to the closed‐loop system. The propylene distillation system is simulated with the proposed control and optimization strategies and the results show that, from the economic performance and robustness viewpoint, for this particular system, the proposed robust MPC is significantly better than the nominal IHMPC based on a single linear model obtained at the most probable operating point.