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21st IFAC World Congress on Automatic control - Meeting Societal Challenges

作者： Osman, Mostafa Mehrez, Mohamed W. Yang, Shiyi Jeon, Soo Melek, William Univ Waterloo Mech & Mechatron Engn Waterloo ON N2L 3G1 Canada

Motion control of mobile manipulators (a robotic arm mounted on a mobile base) can be challenging for complex tasks such as material and package handling. In this paper, a task-space stabilization controller based on nonlinear model predictive control (NMPC) is designed and implemented to a 10 Degrees of Freedom (DOF) mobile manipulator which consists of a 7-DOF robotic arm and a 3-DOF mobile base. The system model is based on kinematic models where the end-effector orientation is parameterized directly by a rotation matrix. The state and control constraints as well as singularity constraints are explicitly included in the NMPC formulation. The controller is tested using real-time simulations, which demonstrate high positioning accuracy with tractable computational cost. Copyright (C) 2020 The Authors.

关键词： Mobile manipulator task-space control non-linear model predictive control

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A novel real-time non-linear wavelet-based model predictive controller for a coupled tank system

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART I-JOURNAL OF SYSTEMS AND control ENGINEERING 2014年第6期228卷 419-432页

作者： Owa, Kayode Sharma, Sanjay Sutton, Robert Univ Plymouth Sch Marine Sci & Engn Plymouth PL4 8AA Devon England Marine & Ind Dynam Anal Res MIDAS Grp Plymouth Devon England

This article presents the design, simulation and real-time implementation of a constrained non-linear model predictive controller for a coupled tank system. A novel wavelet-based function neural network model and a genetic algorithm online non-linear real-time optimisation approach were used in the non-linear model predictive controller strategy. A coupled tank system, which resembles operations in many chemical processes, is complex and has inherent non-linearity, and hence, controlling such system is a challenging task. Particularly important is low-level control where often instability and oscillatory responses are observed. This article designs a wavelet neural network with high predicting precision and time-frequency localisation characteristics for an online prediction model in the non-linear model predictive controller to show the effectiveness of this approach in controlling the liquid at low level. To speed up the training process, a fast global search stochastic non-linear conjugate wavelet gradient algorithm is initially used to train the wavelet neural network structure before the genetic algorithm optimisation technique is utilised to tune adaptively the wavelet neural network parameters. The non-linear model predictive controller algorithm is tested for both approaches: first, in a simulation using identified models, and second, in a real-time practical application to a single-input single-output system coupled tank system. The results show an excellent control performance with respect to mean square error and average control energy values obtained.

关键词： Wavelet neural network artificial neural network modelling system identification non-linear model predictive control real-time practical implementation genetic algorithms single input single output non-linear optimisation coupled tank system Simulink model

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Efficiency-Oriented model predictive control: A Novel MPC Strategy to Optimize the Global Process Performance

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SENSORS 2024年第17期24卷 5732页

作者： Xu, Jiahong Ningbo Univ Technol Robot Inst Ningbo 315211 Peoples R China

Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic model predictive control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a novel control strategy, named the efficiency-oriented model predictive control (MPC), which can fully realize the potential of the optimization margin to improve the global process performance of the whole system. The ideas of optimization margin and optimization efficiency are first proposed to measure the superiority of the control strategy. Our new efficiency-oriented MPC innovatively uses a nested optimization structure to optimize the optimization margin directly online. To realize the computation, a Periodic Approximation technique is proposed, and an Efficiency-Oriented MPC Type I is constructed based on the Periodic Approximation. In order to alleviate the strict constraint of Efficiency-Oriented MPC Type I, the zone-control-based optimization concept is used to construct an Efficiency-Oriented MPC Type II. These two well-designed efficiency-oriented controllers were compared with other control strategies over a Continuous Stirred Tank Reactor (CSTR) application. The simulation results show that the proposed control strategy can generate superior closed-loop process performance, for example, and the Efficiency-Oriented MPC Type I can obtain 7.11% higher profits than those of other control strategies;the effectiveness of the efficiency-oriented MPC was, thereby, demonstrated.

关键词： non-linear model predictive control optimal control optimization intelligent system

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End-Effector Stabilization of a 10-DOF Mobile Manipulator using nonlinear model predictive control

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IFAC-PapersOnLine 2020年第2期53卷 9772-9777页

作者： Mostafa Osman Mohamed W. Mehrez Shiyi Yang Soo Jeon William Melek Mechanical and Mechatronics Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada

关键词： Mobile manipulator task-space control non-linear model predictive control

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On robustness of constrained non-linear H_∞ predictive controllers with disturbances

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2010年第2期41卷 203-212页

作者： He, De-Feng Ji, Hai-Bo Zheng, Tao Univ Sci & Technol China Dept Automat Hefei 230026 Anhui Peoples R China

This article considers the robustness problem of H-infinity model predictive controllers for constrained non-linear discrete-time systems subject to disturbances, which are dependent on the system state and input. The notions of input-to-state stability and finite L-2-gain of non-linear systems are introduced and exploited to investigate the robustness properties of this predictive controller under the state and input constraints and the disturbance. Moreover, this robustness of the controller is extended to the case of suboptimality of the solution. With its feasibility at initial time, the feasibility of the online optimisation problem is guaranteed for all times in the presence of disturbances and constraints. Finally, an example is employed to illustrate the proposed results.

关键词： non-linear model predictive control input-to-state stability finite L-2-gain non-linear H-infinity control suboptimal control

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Transient and disturbed propeller load compensation in hybrid propulsion using propeller estimator and predictive control

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART M-JOURNAL OF ENGINEERING FOR THE MARITIME ENVIRONMENT 2022年第1期236卷 86-102页

作者： Papalambrou, George Planakis, Nikolaos Kyrtatos, Nikolaos Natl Tech Univ Athens Sch Naval Architecture & Marine Engn Lab Marine Engn Zografou Campus Athens 15772 Greece

We address the design of a predictive power-split management system for the transient and fluctuating propeller load sharing. modeling of the power system was carried out based on experimental data gathered from the hybrid plant and on first principles for the diesel engine behavior and battery charging. Propulsion plant and wave disturbance models are employed to simulate realistic marine load application that affects the powerplant operation. A propeller observer is developed to provide the propeller law parameter to the controller, in order to perform load estimation and prediction based on shaft speed elevation. A nonlinear model predictive controller (NMPC) is designed for the optimal transient power-split problem of a hybrid diesel-electric marine propulsion plant. The NMPC scheme directly controls the torque output of the diesel engine and the electric motor generator ensuring that certain constraints concerning the system overloading are met, avoiding fast accelerations, and load fluctuations of the diesel engine that affect engine performance. The control system was experimentally tested in real-time operation in a physical testbed. Results showed that controller rejected successfully load disturbances and maintained the desired rotational speed of the powertrain as well as the desirable state of charge in battery within the power plant limits, achieving smooth power transitions and compensation of power fluctuations of the diesel engine.

关键词： Propulsion power-split control energy management system propeller load estimation non-linear model predictive control hybrid propulsion hybrid vessel vessel and engine in waves

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control of a complex multistep process for the production of mesalazine

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JOURNAL OF PROCESS control 2023年第1期122卷 59-68页

作者： Castillo, Ismael Rehrl, Jakob Sagmeister, Peter Lebl, Rene Kruisz, Julia Celikovic, Selma Sipek, Martin Kirschneck, Dirk Horn, Martin Sacher, Stephan Cantillo, David Williams, Jason D. Khinast, Johannes G. Kappe, Oliver Graz Univ Technol Inst Automat & Control Inffeldgasse 21b A-8010 Graz Austria Res Ctr Pharmaceut Engn GmbH Inffeldgasse 13-2 A-8010 Graz Austria Karl Franzens Univ Graz Inst Chem Heinrichstr A-8010 Graz Austria Evon GmbH Wollsdorf 154 A-8181 St Ruprecht Austria Microinnova Engn GmbH Europapk 1 A-8412 Allerheiligen Austria Graz Univ Technol Inst Proc & Particle Engn Inffeldgasse 13-3 A-8010 Graz Austria

This paper presents the application of Data Driven modelling (DDM) and non-linear model predictive control (NMPC) for the control implementation of a continuous reactor for the production of the Active Pharmaceutical Ingredient (API), Mesalazine. The contribution of this work is to present the overall control architecture, the step-by-step controller design based on DDM, i.e. Neuro-Fuzzy/Local linear model Tree models (LoLiMoT) and NMPC. We demonstrate the advantages of DDM and NMPC, in the presence of non-linear, distributed parameter, multi-variable systems as a suitable, powerful and practical way to approach complex process control challenges. Compared to conventional concepts, the inherent optimization structure of NMPC allows to obtain the desired behaviour of the multi-variable non-linear plant considering the physical constraints of operation regions. The control concept has been implemented in the real system on one central computer, Xamcontrol (Evon),1 with all measurement devices and actuators centrally interconnected.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Multi-step continuous flow process Synthesis of active pharmaceutical ingredient Data driven models LoLiMoT identification non-linear model predictive control

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Flow and pressure control of underbalanced drilling operations using NMPC

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JOURNAL OF PROCESS control 2018年 68卷 73-85页

作者： Pedersen, Torbjorn Aarsnes, Ulf Jakob F. Godhavn, John-Morten Norwegian Univ Sci & Technol NTNU Trondheim Norway IRIS Oslo Norway DrillWell Drilling & Well Ctr Improved Recovery Stavanger Norway Statoil Res Ctr Trondheim Norway

This work evaluates the use of non-linear model predictive control (NMPC) for multivariate control of pressure and flow during underbalanced drilling (UBD) using a fit-for-purpose mechanistic model. Flow and pressure control are instrumental to the stability, safety, and successful drilling of a well. Failure to keep within the pressure envelope may have severe economic and environmental consequences. A relevant drilling scenario with different control objectives is evaluated to show the impact of the tuning and configuration of the control system. It is shown that with a sufficiently good model match, it is possible to achieve good performance with small deviations from the selected operational criteria, even when there are large changes in operational area. The multivariate control system manipulates choke pressure, pump flow, and separator set-points to simultaneously achieve both downhole and topside objectives. The optimization problem is solved within the relevant time dynamics of the system. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： non-linear model predictive control Underbalanced drilling Multiphase-flow Drilling automation Drift-flux model Simulation

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Online model-based optimization and control for the combined optimal operation and runaway prediction and prevention in (fed-)batch systems

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CHEMICAL ENGINEERING SCIENCE 2015年 138卷 760-771页

作者： Rossi, Francesco Copelli, Sabrina Colombo, Andrea Pirola, Carlo Manenti, Flavio Politecn Milan Dipartimento Chim Mat & Ingn Chim Giulio Natta I-20133 Milan Italy Purdue Univ Sch Chem Engn Forney Hall Chem Engn W Lafayette IN 47907 USA Univ Insubria Dip Sci & Alta Tecnol I-21100 Varese Italy Univ Milan Dipartimento Chim I-20133 Milan Italy

An advanced model-based strategy for the online optimization and/or optimal control of (fed-)batch systems, the BSMBO&C, is applied to discontinuous equipment that can be subject to runaway phenomena. The aim is that of showing that it is simultaneously possible to provide profitable online optimization and/or optimal control policies, predict possible future hazardous situations in advance and automatically decide whether and when to (optimally) stop a production cycle in order to prevent safety risks (unavoidable runaways). All these actions are performed online, thus allowing for the effect of any external perturbation. A fed-batch process based on a well-known example of runaway reaction, i.e. the oxidation of 2-octanol to 2-octanone with nitric acid in aqueous solution, is selected as a benchmark to test the real attainability of simultaneous process profitability and safety. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： non-linear model predictive control Real-time dynamic optimization Online runaway prediction and prevention Safe and profitable management of (fed-) batch processes

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Offset-Free nonzero Tracking for nonlinear Impulsive Systems With Application to Biomedical Processes

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IEEE ACCESS 2022年 10卷 19697-19709页

作者： Villa-Tamayo, Maria F. Alzate, Carlos M. Anelone, Anet J. N. Rivadeneira, Pablo S. Univ Nacl Colombia Fac Minas Grp GITA Medellin 050034 Colombia Univ Sydney Sch Math & Stat Camperdown NSW 2006 Australia

Impulsive control systems have shown strong potential to represent and address challenging problems, especially in the biomedical field. In recent research, these problems have been tackled with advances in linear impulsive control systems. However, many biomedical applications are better described by nonlinear impulsive models, and therefore, it is necessary to develop analysis tools and control strategies in this context. In the literature, the main properties of nonlinear impulsive control systems have been fully understood, but there is no major development of control strategies. Particularly, there is no substantiation of model predictive control (MPC) strategies maintaining convexity of the optimization problem and closed-loop stability, and there is no control strategy to reduce the offset problem when there are parameter variations, which is a common situation in biological processes. Therefore, the main novelties of this paper are: (i) an MPC formulation extended to nonlinear impulsive systems that addresses non-zero tracking, (ii) the sufficient and necessary conditions to guarantee the stability of the closed-loop system at an equilibrium target, (iii) a comprehensive description of an offset-free MPC to handle low to moderate plant-model mismatches, (iv) the conditions to guarantee offset-free control. Finally, the MPC and offset-free MPC are tested to address the drug administration problem in two biomedical applications: oncolytic virus therapy, to regulate tumor dynamics using doses of oncolytic, and type 1 diabetes treatment, to regulate glycemia using insulin injections. Satisfactory results were obtained in simulation scenarios including parameter variations in nonlinear models that represent the corresponding dynamics.

关键词： Biological system modeling predictive models Viruses (medical) control systems Integrated circuit modeling Stability analysis Insulin Impulsive control systems non-linear model predictive control offset-free control control for biomedical processes

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