Multi-parametric Model Predictive Control Research Articles

Integration and capacity optimization of molten-salt heat storage in coal-fired power plant with carbon capture system

Developing a new generation of economical, low-carbon, and flexible coal-fired power plants (CFPP) is crucial for achieving the low-carbon transformation of the energy industry. Deploying molten salt heat storage (MSHS) in the CFPP can effectively enhance its peak shaving capability and reduce the limitations of carbon capture on wide range operation of the plant. To this end, this paper proposes four integration schemes for MSHS into the power plant-carbon capture system. The thermal efficiencies, exergy efficiencies, and peak shaving capabilities of the entire plant under different integration schemes are evaluated and compared. The results indicate that the optimal integration scheme involves utilizing hot reheat steam to heat the cold molten salt and further provide heat for the carbon capture system during the heat charging process and utilizing hot molten salt to heat the outlet water of the feed water pump during the heat discharging process, which achieves the highest thermal efficiency of 38.00 % and exergy efficiency of 37.09 % across the entire cycle. A novel integrated design, scheduling, and control optimization (IDSCO) method is proposed to find the optimal capacity configuration of MSHS, in which the investment, maintenance, fuel, carbon emission, and long-timescale and short-timescale load tracking deviation penalty costs are fully considered. A design parameter-aware control system is developed based on a multi-parametric programming model predictive control approach to ensure a fair evaluation of closed-loop dynamic performance under different configuration capacities. The simulation results demonstrate that the deployment of MSHS can significantly enhance the flexibility of the power plant-carbon capture system and the proposed IDSCO approach can reduce 17.05 % of load tracking deviation penalty cost and 1.38 % of totalized cost compared to the conventional configuration approach. This paper effectively leverages the potential of MSHS in enhancing the operational flexibility of CFPP-PCC system, thereby providing useful guidance for future deployment of MSHS within CFPP-PCC system.

Dynamic risk-based process design and operational optimization via multi-parametric programming

We present a dynamic risk-based process design and multi-parametric model predictive control optimization approach for real-time process safety management in chemical process systems. A dynamic risk indicator is used to monitor process safety performance considering fault probability and severity, as an explicit function of safety–critical process variables deviation from nominal operating conditions. Process design-aware risk-based multi-parametric model predictive control strategies are then derived which offer the advantages to: (i) integrate safety–critical variable bounds as path constraints, (ii) control risk based on multivariate process dynamics under disturbances, and (iii) provide model-based risk propagation trend forecast. A dynamic optimization problem is then formulated, the solution of which can yield optimal risk control actions, process design values, and/or real-time operating set points. The potential and effectiveness of the proposed approach to systematically account for interactions and trade-offs of multiple decision layers toward improving process safety and efficiency are showcased in a real-world example, the safety–critical control of a continuous stirred tank reactor at T2 Laboratories.

Noncooperative Distributed Model Predictive Control: A Multiparametric Programming Approach

The distributed control system architecture strikes a balance between the decentralized control system architecture, where subsystem interactions are unaccounted for, and the computationally expensive centralized control system architecture. Subsystem interactions can be significant in chemical process systems, especially when energy or material recycle loops are present. A drawback of the distributed control system is that it is computationally expensive as it requires intermediate iterations involving the solution of multiple optimization problems to be performed. To address this drawback, we develop a noncooperative, iterative, multiparametric distributed model predictive control (mpDiMPC) technique with an aim to decrease the computational costs of conventional, online distributed controllers by avoiding the need to solve an optimization problem at each intermediate iteration. We apply the developed control algorithm on an interacting reactor–separator process and study its control and computational performance. For the case study presented in this paper, mpDiMPC resulted in a reduction in computational costs by approximately 95% compared to its online counterpart.

Data‐driven prescriptive maintenance toward fault‐tolerant multiparametric control

AbstractPrescriptive maintenance can improve system effectiveness and system safety via integrated production and maintenance optimization. However due to system disruptions there is potential for abnormal operations and an undesirable increased occurrence of process safety incidents. This research provides a multiparametric‐based framework for safety‐aware, maintenance‐aware, and disruption‐aware process control. It leverages ensemble classification via machine learning classifiers for fault detection, mixed‐integer nonlinear programming for integrated safety‐aware production and maintenance scheduling, as well as hybrid multiparametric model predictive control for fault‐tolerant setpoint tracking. The results show that the ensemble classifier outperforms the individual classifiers in terms of fault detection accuracy, sensitivity, and specificity. Furthermore, it is seen that the developed controllers are able to reconfigure the control actions based on process disruption information. The framework is illustrated with a chemical complex system, and a cooling water system. The approach can be used to help improve the safety and productivity of industrial processes.

HIL Evaluation of a Novel Real-time Energy Management System for an HEV with a Continuously Variable Transmission

One of the most important challenges facing automotive engineers is reducing vehicle fuel consumption and improving the drivability index. Modern hybrid electric powertrains play an important role in reducing fuel consumption. Continuously variable transmission (CVT) is an automatic transmission that can change the gear ratio seamlessly using a belt and pulleys. CVT performs with infinite gear ratios. Controlling and determining the optimal gear ratio, especially in a complex hybrid powertrain, is a major challenge. Therefore, a multi-parametric model predictive controller with real-time implementation capability is proposed that can handle the energy management task concurrently with gear shifting strategy in a parallel pre-transmission hybrid electric vehicle. The proposed controller hardware-in-the-loop (HIL) validation procedure on the high-fidelity Autonomie model shows significant improvement in fuel economy while maintaining drivability in three different driving schedules. HIL evaluation guarantees the real-time capability and the proposed controller readiness to be implemented to real-world control hardware.

Multi parametric model predictive control based on laguerre model for permanent magnet linear synchronous motors

<p>The permanent magnet linear motors are widely used in various industrial applications due to its advantages in comparisons with rotary motors such as mechanical durability and directly creating linear motions without gears or belts. The main difficulties of its control design are that the control performances include the tracking of position and velocity as well as guarantee limitations of the voltage control and its variation. In this work, a cascade control strategy including an inner and an outer loop is applied to synchronous linear motor. Particularly, an offline MPC controller based on MPP method and Laguerre model was proposed for inner loop and the outer controller was designed with the aid of nonlinear damping method. The numerical simulation was implemented to validate performance of the proposed controller under voltage input constraints.</p>

Assisting continuous biomanufacturing through advanced control in downstream purification

Aiming to significantly improve their processes and secure market share, monoclonal antibody (mAb) manufacturers seek innovative solutions that will yield improved production profiles. In that space, continuous manufacturing has been gaining increasing interest, promising more stable processes with lower operating costs. However, challenges in the operation and control of such processes arise mainly from the lack of appropriate process analytics tools that will provide the required measurements to guarantee product quality. Here we demonstrate a Process Systems Engineering approach for the design a novel control scheme for a semi-continuous purification process. The controllers are designed employing multi-parametric Model Predictive Control (mp-MPC) strategies and the successfully manage to: (a) follow the system periodicity, (b) respond to measured disturbances and (c) result in satisfactory yield and product purity. The proposed strategy is also compared to experimentally optimized profiles, yielding a satisfactory agreement.

Multi-parametric model predictive control for autonomous steering using an electric power steering system

Electric power steering systems have been used to generate assist torque for driver comfort. This study makes use of the functionality of electric power steering systems for autonomous steering control without driver torque. A column-type electric power steering test bench, equipped with a brushless DC motor as an assist motor, and the Infineon TriCore AURIX TC 277 microcontroller was used in this study. Multi-parametric model predictive control is based on a model predictive control–based approach that employs a multi-parametric quadratic programming technique. This technique allows the reduction of the huge computational burden resulting from the online optimization in model predictive control. The proposed controller obtains an optimal input based on multi-parametric quadratic programming at each sampling time. The weighting matrix definition, which is the main task when designing the proposed controller, was analyzed. The experimental results of the step response of the steering wheel angle verified the tracking ability of the proposed controller for different ranges of the prediction horizon. Since the computational loads are directly related to functional safety, the results of this study support the use of the multi-parametric model predictive control scheme as an effective control method for autonomous steering control.

The impact of model approximation in multiparametric model predictive control

Incorporating a high fidelity model that accurately describes a dynamical system in a model predictive control study may often lead to an intractable formulation where the use of model approximation is required. This study examines system identification, time series modeling, and linearization in the context of multiparametric model predictive control with the use of key error metrics including: (i) a novel comparison of key features of the feasible space and objective function in the optimization formulation, (ii) integral time absolute error, (iii) error distribution analysis, and (iv) step response profiles. Two examples are used as a basis for this study: a tank system which highlights the techniques used and a Continuously Stirred Tank Reactor (CSTR).

Dynamic Modeling and Explicit Control of a PEM Water Electrolysis Process

Abstract Hydrogen production from water electrolysis has the potential to mitigate the intermittency associated with power generated from variable renewable energy. In addition, proton exchange membrane water electrolyzer (PEMWE) has gained attention within the last decade because of its relative advantage over other water electrolysis technologies. However, the high cost of operation that is due to its energy intensity has been a major setback. In this work, we develop an optimal operating strategy for the PEMWE process using the parametric optimization and control framework. First, we present a dynamic mathematical model of the PEMWE that captures the detailed electrochemical interaction, transport phenomenon, bubble coverage, and other interactions associated with energy losses in the system. Secondly, we design a model predictive control (MPC), which is then reformulated into a multiparametric model predictive control (mp-MPC). Unlike the MPC, the mp-MPC avoids the online optimization procedure at every time step because the optimization is done once and offline. The control action is an explicit function of parameters that are realized during the process. The controller is tested on the original dynamic model in a closed loop validation scheme.

Model predictive control (MPC) strategies for PEM fuel cell systems – A comparative experimental demonstration

The aim of this work is to demonstrate the response of advanced model-based predictive control (MPC) strategies for Polymer Electrolyte Membrane Fuel cell (PEMFC) systems. PEMFC are considered as an interesting alternative to conventional power generation and can be used in a wide range of stationary and mobile applications. An integrated and modular computer-aided Energy Management Framework (EMF) is developed and deployed online to an industrial automation system for monitoring and operation of a PEMFC testing unit at CERTH/CPERI. The operation objectives are to deliver the demanded power while operating at a safe region, avoiding starvation, and concurrently minimize the fuel consumption at stable temperature conditions. A dynamic model is utilized and different MPC strategies are online deployed (Nonlinear MPC, multiparametric MPC and explicit Nonlinear MPC). The response of the MPC strategies is assessed through a set of comparative experimental studies, illustrating that the control objectives are achieved and the fuel cell system operates economically and at a stable environment regardless of the varying operating conditions.

다중-파라미터 모델 예측 제어를 적용한 컬럼타입 전기식 동력 조향 시스템에 대한 시뮬레이션

This paper presents a multi-parametric Model Predictive Control (mpMPC) controller employed to improve the performance and robustness of a Column type Electric Power Steering (C-EPS) system. The EPS systems have been regarded as an important aid for drivers owing to their advantages over hydraulic power steering systems. An electric motor attached to the steering mechanism generates the assisted torque to reduce the required driver torque. The MPC has been more frequently used in automotive industries owing to its ability to fulfil hard constraints and anticipate future events; however, as this control scheme is based on online optimization, relatively large computational complexities are inevitably encountered. Therefore, the mpMPC has been suggested to reduce the complexities using a technique called multi-parametric Quadratic Programming (mp-QP), which considers the state vector as a vector of parameters. To verify the performance and the robustness of the proposed controller, a Linear-Quadratic Regulator (LQR) controller is also designed for comparison. A simulation via MATLAB/Simulink varying the parameters in a C-EPS system are conducted in this paper.

Modeling, estimation and control of the anaesthesia process

In this work we present different design strategies towards model based simultaneous multiparametric model predictive control and state estimation for intravenous anaesthesia. We first present a detailed compartmental mathematical model featuring a pharmacokinetic and a pharmacodynamics part. Due to unavailability of data and information, different estimation techniques are formulated and implemented. Furthermore these estimation techniques are implemented simultaneously with multiparametric model predictive controllers and tested for real patient data under the assumption that the output is either noise free or corrupted by noise. The derived control schemes are able to deal with two of the main challenges in controlling the depth of anaesthesia: (i) model nonlinearity and (ii) inter- and intra- patient variability.

Explicit hybrid model predictive control strategies for intravenous anaesthesia

In this work, we first present a piece-wise affine model for intravenous anaesthesia, based on which a hybrid explicit/multiparametric model predictive control strategy is developed. To deal with the inter- and intra-patient variability, an estimation strategy, the multiparametric moving horizon estimator, and different robust algorithms such as Offset Correction, State-Output Correction and Prediction Output Correction are further designed and implemented simultaneously with the hybrid multiparametric model predictive control. Simulation results for a set of 12 virtually generated patients for the regulation of the depth of anaesthesia by means of the Bispectral Index with Propofol as the anaesthetic, demonstrate the validity and usefulness of the proposed advanced control and estimation strategies.

Fuzzy Aggregation Based Multiple Models Explicit Multi Parametric MPC Design for a Quadruple Tank Process

Abstract In this study, design of fuzzy aggregated multi parametric model predictive controller (mpMPC) for multivariable systems is proposed. Using first principles model (FPM) of the process, a steady state map (SSM) of input output is generated. A minimum number of linearized models are obtained from the FPM in different nominal operating regions to satisfy prediction error criterion. From gap metric measure, these models are clustered by k-means algorithm, followed by cluster representatives (CR) selection and fuzzy weights (to represent each model using aggregated CR's) calculation. For a given operating point, fuzzy weights are determined by a supervisory structure. Using these weights, the estimation model and control output (from mpMPC's designed for each CR) are aggregated. A real time quadruple tank (QT) is controlled using the proposed strategy, which is implemented on an embedded platform. Performance metrics indicate a 20% improvement in prediction and 20% improvement in control under closed loop using proposed strategy.

Explicit model predictive control of split-type air conditioning system

Domestic air conditioners are a major source of energy consumption. In this study, utilizing real-time data from a public domain, a cascaded hardware in loop approach to the control of room temperature is considered. An inner loop to control the supply air temperature by adjusting the electronic expansion valve using a second-order plus delay time model is proposed. The room temperature control is considered the outer loop. A simplified lumped parameter representation of the thermal dynamics of the building is modelled in MATLAB using ordinary differential equations. A constrained multi parametric model predictive controller (mpMPC) is designed for both the control loops. The constraints include safety limits on the superheat and manipulation rates for the inner loop and a rate constraint on the reference signal in the outer loop. Model uncertainties like ambient temperature and thermal load variations (representing an office space) are considered for hardware in the loop testing of the proposed strategy. From performance analysis, using power spent and thermal comfort quantization, it is observed that the mpMPC scheme outperforms traditional control strategies.

Decentralized Multiparametric Model Predictive Control for Domestic Combined Heat and Power Systems

In an effort to provide affordable and reliable power and heat to the domestic sector, the use of cogeneration methods has been rising in the past decade. We address the issue of optimal operation of a domestic cogeneration plant powered by a natural gas, internal combustion engine via the use of explicit/multiparametric model predictive control. More specifically, we take advantage of the natural division of a combined heat and power (CHP) cogeneration system into two distinct but interoperable subsystems, namely, the power generation subsystem and the heat recovery subsystem, in order to derive a decentralized, two-mode model predictive control scheme that specifically targets the production of either electrical power or usable heat at a given time. We follow our recently developed PAROC framework for the design of the controllers, and we apply it in a decentralized manner. We show how the CHP system can efficiently operate in both modes of operation through closed-loop validation of the control scheme ...

Simulation Work for the Control of Blood Glucose Level in Type 1 Diabetes Using Hovorka Equations

Recently, diabetes is known as one of non-communicable diseases that can lead to fatal if there is no further cure is to be taken especially in South-East Asia regions. An artificial pancreas is introduced to help diabetes patient controls their blood glucose level but the current device is not functioning as fully automated yet. In order to have fully automated artificial pancreas, a controller needs to be improved as the current controller is 33% less accuracy than required. This improvement will help Type 1 diabetes patient in managing their blood glucose level at recommended range. Besides, the presence of controller will help the patient to live normally as non-diabetes people. This research is done to study behaviours of variables in Hovorka model for Type 1 diabetes and to simulate the Hovorka equations. gPROMS software is used due to its speciality in real-time dynamic simulation, fast calculation in complex mathematical equations and capable to adapt multi-parametric programming and Model Predictive Control (MPC). The study is conducted using simulation software based on previous studies experimental data; focusing on the algorithm of the controller. The results illustrate the most active parameter in the model is the administration (bolus & infusion) of insulin.

Optimal Rules for Central Bank Interest Rates Subject to Zero Lower Bound

Abstract The celebrated Taylor rule provides a simple formula that aims to capture how the central bank interest rate is adjusted as a linear function of inflation and output gap. However, the rule does not take explicitly into account the zero lower bound on the interest rate. Prior studies on interest rate selection subject to the zero lower bound have not produced derivations of explicit formulas. In this work, Taylor-like rules for central bank interest rates bounded below by zero are derived rigorously using a multi-parametric model predictive control framework. This framework is used to derive rules with or without inertia. The proposed approach is illustrated through simulations. Application of the approach to US economy data demonstrates its relevance and provides insight into the objectives underlying central bank interest rate decisions. A number of issues for future study are proposed.

Advanced model-based control studies for the induction and maintenance of intravenous anaesthesia.

This paper describes strategies toward model-based automation of intravenous anaesthesia employing advanced control techniques. In particular, based on a detailed compartmental mathematical model featuring pharmacokinetic and pharmacodynamics information, two alternative model predictive control strategies are presented: a model predictive control strategy, based on online optimization, the extended predictive self-adaptive control and a multiparametric control strategy based on offline optimization, the multiparametric model predictive control. The multiparametric features to account for the effect of nonlinearity and the impact of estimation are also described. The control strategies are tested on a set of 12 virtually generated patient models for the regulation of the depth of anaesthesia by means of the bispectral index (BIS) using Propofol as the administrated anaesthetic. The simulations show fast response, suitability of dose, and robustness to induce and maintain the desired BIS setpoint.