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Iterated non-linear model predictive control based on tubes and contractive constraints

ISA TRANSACTIONS 2016年 62卷 120-128页

作者： Murillo, M. Sanchez, G. Giovanini, L. Natl Sci & Tech Res Council CONICET Res Inst Signals Syst & Computat Intelligence Sin Ciudad Univ UNL4 Piso FICHS3000 Santa Fe Argentina

This paper presents a predictive control algorithm for non-linear systems based on successive linearizations of the non-linear dynamic around a given trajectory. A linear time varying model is obtained and the non-convex constrained optimization problem is transformed into a sequence of locally convex ones. The robustness of the proposed algorithm is addressed adding a convex contractive constraint To account for linearization errors and to obtain more accurate results an inner iteration loop is added to the algorithm. A simple methodology to obtain an outer bounding-tube for state trajectories is also presented. The convergence of the iterative process and the stability of the closed-loop system are analyzed. The simulation results show the effectiveness of the proposed algorithm in controlling a quadcopter type unmanned aerial vehicle. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Contractive constraint Iterative process non-linear model predictive control Robust model predictive control

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model predictive control of an active ankle-foot orthosis with non-linear actuation constraints

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control ENGINEERING PRACTICE 2023年第1期136卷

作者： DeBoer, Benjamin Hosseini, Ali Rossa, Carlos Ontario Tech Univ Fac Engn & Appl Sci Oshawa ON Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON Canada

The goal of active ankle-foot orthoses (AAFO) is to assist the user in recreating a nominal gait motion. Previously proposed control systems for AAFOs have been reactive, with controllers based on the current tracking error. However, the optimal AAFO controller must ensure adequate trajectory tracking while minimizing the amount of assistance provided to the user. To this end, a model predictive controller (MPC) can be considered to determine the optimal control sequence for a given trajectory. The MPC minimizes the control effort, thereby determining the minimal assistance required for a user to regain a natural gait. This work outlines the formulation of a non-linear MPC for an AAFO and its combination with an extended state observer (ESO) for tracking of an *** this work, a MPC controller is proposed for optimal control of an AAFO, a linear plant subject to non-linear actuation constraints. A novel method is expressed to determine the initial control horizon selection in combination with variable step Newton-based optimization to enable real-time implementation. The MPC control effort is then combined with the estimated disturbance from an ESO to accurately track the desired *** and experimental results prove the tracking capabilities of the proposed method. Compared to a tuned proportional-derivative controller, the simulated controller reduced the trajectory tracking error by up to 65.1% and 15.3% in an undisturbed and disturbed environment, respectively. The MPC and ESO combination allows the controller to identify the optimal control action for accurate trajectory tracking. Concurrently, the ESO rejects both external and internal disturbances, with superior tracking capabilities in both undisturbed and heavily disturbed systems, making it the optimal choice for AAFO control.

关键词： non-linear model predictive control Active disturbance rejection Active ankle-foot orthosis

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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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Derivative-free Estimator Based non-linear model predictive control of a Boiler-Turbine Unit

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IFAC Proceedings Volumes 2013年第32期46卷 660-665页

作者： S. Kapil Arasu J. Prakash Vinay Prasad Department of Instrumentation Engineering Madras Institute of Technology Campus Anna University Chennai-44.**India Department of Chemical and Materials Engineering University of Alberta Canada

In this work, the authors have proposed a constrained nonlinear model predictive control (NMPC) scheme for a boiler-turbine system. The proposed control scheme relies on the non-linear state space model proposed by Bell and Astrom, 1987 for prediction. A derivative-free Kalman filter has been designed to estimate the state variables of the boiler-turbine unit. These estimated values have been used as an initial condition for predicting the future states and outputs in the NMPC formulation. In order to account for model-plant mismatch and to achieve offset-free control, innovation based correction of predicted states and outputs as suggested by Ricker, 1990 has been incorporated in the proposed NMPC formulation. The extensive simulation studies show that the proposed control scheme effectively handles the input constraints of the boiler-turbine unit and meets the required electrical demand without requiring careful selection of operating points.

关键词： Derivative free Kalman Filter Boiler-turbine system non-linear model predictive control

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Development of a DDP-based trajectory generation method considering the manipulability measure for 6-DOF collaborative robots

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2025年第3期12卷 98-114页

作者： Lee, Jaesoon Cho, Baek-Kyu Korea Inst Robot & Technol Convergence Human Ctr Robot R&D Div Pohang 37553 South Korea Kookmin Univ Sch Mech Engn Seoul 136702 South Korea

This paper presents a trajectory generation method for 6-degrees-of-freedom (DOF) collaborative robots using the differential dynamic programming algorithm, integrating the manipulability measure (MM) to effectively avoid singularities. Traditional trajectory generation methods often neglect the robot's dynamics, leading to non-optimized trajectories and potential singularity issues. Our approach integrates MM into the differential dynamic programming algorithm, ensuring consideration of the robot's dynamics while effectively avoiding singularities. The proposed algorithm is applied to the 6-DOF collaborative robot RB1-500e and validated on the actual robot. The results demonstrate that the proposed method significantly improves trajectory generation and singularity avoidance compared to traditional methods. This study offers a robust solution that enhances both efficiency and safety in dynamic trajectory planning for collaborative robots.

关键词： collaborative robot non-linear model predictive control trajectory generation manipulability measure optimal control singularity avoidance

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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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Dynamic Obstacle Avoidance in Cobots with Multiple Random Moving Obstacles 10

Dynamic Obstacle Avoidance in Cobots with Multiple Random Mo...

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10th International Conference on control, Automation and Robotic (ICCAR)

作者： Kodandaramu, Laasya Srikantan, Maalavika Sanoj, Jacob V. Narayan, Kaushik Tripathi, Shikha PES Univ Dept Elect & Commun Engn Bengaluru India

ISBN: (纸本)9798350373172;9798350373189

Traditional industrial robots are generally massive, heavy, and dangerous and are therefore generally isolated from humans. Collaborative robots or COBOTs are designed to collaborate and work with humans in a shared workspace. These are typically smaller and lighter than traditional industrial robots and are therefore more flexible and easier to deploy in a variety of settings. One of the most crucial problems that need to be addressed for cobots is Dynamic Obstacle Avoidance as they will be working in an environment where there will be constant human movement. This paper presents an efficient solution to this problem employing a non-linear model predictive controller (NMPC) for real-time path planning. It has been used to obtain the most optimum trajectory while avoiding dynamic obstacles. The KINOVA Gen3 manipulator has been simulated with static and dynamic obstacle modelling. The NMPC controls the arm to minimize differences between the system's current state and the desired goal position while avoiding obstacles. A detailed visualization of the system motion has also been included. The work has been tested and the results have been shown for 4 different cases - reference case with no obstacles, case 1 with three obstacles, case 2 with four obstacles and case 3 with a simulated human-like obstacle to mimic a typical cobot scenario. Detailed error analysis including end-effector and implementation errors has also been shown for each of the 4 cases and is promising.

关键词： Cobots non-linear model predictive control Manipulator MATLAB Kinova Gen3 dynamic obstacles

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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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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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Greenhouse gases mitigation: global externalities and short-termism

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ENVIRONMENT AND DEVELOPMENT ECONOMICS 2023年第3期28卷 230-241页

作者： Di Bartolomeo, Giovanni Fard, Behnaz Minooei Semmler, Willi Sapienza Univ Rome Dept Econ & Law Rome Italy Univ Antwerp Dept Econ Antwerp Belgium Ca Foscari Univ Venice Dept Econ Venice Italy New Sch Social Res New York NY 10011 USA Univ Bielefeld Bielefeld Germany

Policies designed to control greenhouse gases imply domestic tradeoffs and international externalities, which lead to both domestic and international conflicts, influencing their feasibility and implementations. Our paper investigates two quantitative aspects within this debate. We intend to quantify the impact of: (a) the internalization of international externalities;and (b) the damage associated with a short-term view of climate policies. In this respect, we adopt the innovative (in this field) idea of model predictive control to formalize moving-horizon policy strategies and, thus, to build counterfactuals characterized by a different horizon for all policymakers.

关键词： climate policy CO2 concentration global warming non-linear model predictive control short-termism

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