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nonlinear model predictive control of Hydrocyclone Separation Efficiency ⁎

IFAC-PapersOnLine 2024年第14期58卷 730-735页

作者： Stefan Jespersen Dennis Severin Hansen Mads Valentin Bram Mahsa Kashani Zhenyu Yang AAU Energy Aalborg University Esbjerg Denmark

As oil fields mature, an increasing volume of water is produced alongside the oil and gas due to the injection of water to maintain reservoir pressure. The control of de-oiling hydrocyclones in produced water treatment on offshore oil and gas facilities is typically based on the pressure drop ratio (PDR). While PDR relates to the flow split in the hydrocyclone and affects the separation efficiency, it is only an indirect way to control the steady-state de-oiling efficiency. When the separation facility is subjected to disturbances, e.g., changing inlet concentration or production volume, the separation efficiency changes dynamically. The PDR responds to changes in flow rate, but it cannot sense changes in inlet oil content. By deploying online oil-in-water monitors, the separation efficiency could, in principle, be measured and used for dynamic feedback. This work developed a plant model based on previously published models of PDR, separator water level, and hydrocyclone separation efficiency. A nonlinear model predictive controller is designed and placed in cascade with the existing PDR-based PI controller to optimize the hydrocyclone separation efficiency. The results indicate an increased separation efficiency and, thus, a potential reduction in discharged oil of approximately 12 percentage points.

关键词： De-oiling hydrocyclone separation efficiency nonlinear model predictive control produced water treatment

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nonlinear model predictive control based on hyper chaotic diagonal recurrent neural network

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Journal of Central South University 2022年第1期29卷 197-208页

作者： Samira Johari Mahdi Yaghoobi Hamid RKobravi Department of Control Mashhad BranchIslamic Azad UniversityMashhad ***Iran Department of Biomedical Engineering Mashhad BranchIslamic Azad UniversityMashhad ***Iran

nonlinear model predictive controllers(NMPC)can predict the future behavior of the under-controlled system using a nonlinear predictive ***,an array of hyper chaotic diagonal recurrent neural network(HCDRNN)was proposed for modeling and predicting the behavior of the under-controller nonlinear system in a moving forward *** order to improve the convergence of the parameters of the HCDRNN to improve system’s modeling,the extent of chaos is adjusted using a logistic map in the hidden layer.A novel NMPC based on the HCDRNN array(HCDRNN-NMPC)was proposed that the control signal with the help of an improved gradient descent method was *** controller was used to control a continuous stirred tank reactor(CSTR)with hard-nonlinearities and input constraints,in the presence of uncertainties including external *** results of the simulations show the superior performance of the proposed method in trajectory tracking and disturbance *** convergence and neglectable prediction error of the neural network(NN),guaranteed stability and high tracking performance are the most significant advantages of the proposed scheme.

关键词： nonlinear model predictive control diagonal recurrent neural network chaos theory continuous stirred tank reactor

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Accurate Pseudospectral Optimization of nonlinear model predictive control for High-Performance Motion Planning

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第2期8卷 1034-1045页

作者： Gao, Feng Han, Yu Li, Shengbo Eben Xu, Shaobing Dang, Dongfang Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Shanghai Jiao Tong Univ Sichuan Res Inst Chengdu 610200 Peoples R China Tsinghua Univ Sch Vehicle & Mobil State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA Res & Dev Ctr Guangzhou Automobile Grp Guangzhou 511434 Peoples R China

nonlinear model predictive control (NMPC) is an effective method for motion planning of automated vehicles, but the high computational load of numerical optimization limits its practical application. This paper designs an NMPC based motion planner and presents two techniques, named adaptive Lagrange discretization and hybrid obstacle avoidance constraints, to accelerate the numerical optimization by reducing the optimization variables and simplifying the non-convex constraints. Given the high nonlinearity of vehicle dynamics, the Lagrange interpolation is adopted to convert the state equation of vehicle dynamics and the objective function to ensure a preset accuracy but with less discretization points. An adaptive strategy is then designed to adjust the order of Lagrange polynomials based on the distribution of discretization error. Moreover, a hybrid strategy is presented to construct the constraints for obstacle avoidance by combing the elliptic and linear time-varying methods together. It can ensure driving safety and also make a good balance between computing load and accuracy. The performance of these techniques on accelerating the NMPC based motion planner is validated and analyzed by comparative numerical simulations and experimental tests under various scenarios. Compared with traditional methods, the results show that these techniques improve accuracy and efficiency by 74% and 60%, respectively.

关键词： Optimization Vehicle dynamics Planning Collision avoidance Dynamics Numerical models Computational modeling Autonomous driving motion planning nonlinear model predictive control numerical optimization

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Insulin infusion rate control using information theoretic-based nonlinear model predictive control for type 1 diabetes patients

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2023年第5期45卷 815-827页

作者： Birjandi, Sahar Zadeh Sani, Seyed Kamal Hosseini Pariz, Naser Ferdowsi Univ Mashhad FUM Dept Elect Engn Fac Engn Azadi Sq Mashhad *** Khorasan E Raza Iran

It has been proven that model predictive control (MPC) is an efficient method for closed-loop insulin delivery in clinical studies. This paper aims to design an observer-based fractional-order nonlinear MPC for type 1 diabetes mellitus (T1DM) patients. It is assumed that the proposed model is nonlinear and contains parametric uncertainty. To estimate unknown states, optimal non-fragile H-infinity observer is designed for Lipschitz nonlinear fractional-order systems including parametric uncertainty and the existence of input disturbance. The min-max optimization-based robust fractional model predictive control (RFMPC) has been presented in the following for insulin delivery. Since sensor noise of continuous monitoring of interstitial glucose concentration is considered non-Gaussian, the performance of the proposed controller is improved under non-Gaussian measurement noise by selecting a proper cost function based on generalized correntropy, and as a contrast, the performance of the mean square error (MSE)-based controller is simulated. According to the results, not only is the performance of the proposed controller better under non-Gaussian situations but also effectively reaches the set point in the case of disturbance and uncertainty and provides higher control accuracy and robustness compared with the MSE-based MPC.

关键词： nonlinear model predictive control fractional order Bergman's minimal model fractional-order observer generalized correntropy criterion

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Neighboring extremal nonlinear model predictive control of a rigid body on SO(3)

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ROBOTICA 2023年第4期41卷 1313-1334页

作者： Bagherzadeh, Shiva Karimpour, Hossein Keshmiri, Mehdi Isfahan Univ Technol Dept Mech Engn Esfahan Iran Univ Isfahan Fac Engn Dept Mech Engn Esfahan Iran

The issue of implementing nonlinear model predictive control (NMPC) on mechanical systems evolving on special orthogonal group (SO(3)) is taken into consideration in the first place. Necessary conditions of optimality are extracted based on Lie group variational integrators, leading to a two-point boundary value problem (TPBVP) which is solved using sensitivity derivatives and indirect shooting methods. Fast Newton-like methods referred to as fast solvers which are commonly used to solve the TPBVP are established based on the repetition of a nonlinear process. The numerical schemes employed to alleviate the computation burden consist of eliminating some constraint-related but non-essential terms in the trend of sensitivity derivatives calculation and for solving the TPBVP equations. As another claim, assuming that a first attempt to resolve the NMPC problem is accessible, the problem subjected to some changes in its initial conditions (due to some re-planning schemes) can be resolved cost-effectively based on it. Instead of solving the whole optimization process from the scratch, the optimal control inputs and states of the system are updated based on the neighboring extremal (NE) method. For this purpose, two approaches are considered: applying NE method on the first solution that leads to a neighboring optimal solution, or assisting this latter by updating the NMPC-related optimization using exact TPBVP equations at some predefined intermediate steps. It is shown through an example that the first method is not accurate enough due to error accumulations. In contrast, the second method preserves the accuracy while reducing the computation time significantly.

关键词： nonlinear model predictive control neighboring extremal Lie group variational integrators sensitivity derivatives computation time

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Robust multistage nonlinear model predictive control on an autonomous marine surface vehicle

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JOURNAL OF MARINE SCIENCE AND TECHNOLOGY 2023年第2期28卷 370-386页

作者： Jiang, Xiaoyong Peng, Mengle Li, Zhongyi Huang, Langyue Xu, Shutian Yang, Ke-ji Zhejiang Univ Sci & Technol Sch Mech & Energy Engn Hangzhou 310023 Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China

Due to the need for a higher safety level, autonomous marine surface vehicles have been increasingly applied for various applications in recent years. nonlinear model predictive control (NMPC) has increasingly become a more preferred control technique. However, it has still limited use in industry due to its high computational load which is mainly caused by its complex Newton numerical method and Hessian matrix evaluations. In this study, we have developed a robust multistage NMPC algorithm to accurately control an autonomous marine surface vehicle when model uncertainties or environmental disturbances are present. The robust multistage NMPC is compared with the standard NMPC and perfect information NMPC. Advanced symbolic computation approach has been used to implement all three NMPC programs, where the complex gradient vector, Hessian matrix, and nonlinear dynamic optimization problem are derived analytically and evaluated with machine accuracy. The simulation results reveal that the robust multistage NMPC approach is successfully able to better control an autonomous marine surface vehicle than the standard NMPC method, and impressively is almost as good as the perfect information NMPC when model uncertainties or environmental disturbances are present. Furthermore, it has been demonstrated that the robust NMPC is also very effective for a large scaled-up prototype system.

关键词： Autonomous surface vehicles nonlinear model predictive control Symbolic computation Robust multistage NMPC model uncertainties

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Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models

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INTERNATIONAL JOURNAL OF ROBUST AND nonlinear control 2023年第16期33卷 9992-10009页

作者： Xie, Jing Bonassi, Fabio Farina, Marcello Scattolini, Riccardo Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy Politecn Milan Dipartimento Elettron Informaz & Bioingn Via Ponzio 34-5 I-20133 Milan Italy

This paper presents a robust model predictive control (MPC) scheme that provides offset-free setpoint tracking for systems described by neural nonlinear autoregressive exogenous (NNARX) models. To this end, a NNARX model that learns the dynamics of the plant from input-output data is augmented with an explicit integral action on the output tracking error. A robust tube-based MPC is finally designed, leveraging the unique structure of the model, to ensure robust offset-free convergence to constant reference signals even in case of plant-model mismatch. Numerical simulations on a water heating system show the effectiveness of the proposed control algorithm.

关键词： learning-based control neural networks nonlinear model predictive control offset-free tracking robust control

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nonlinear model predictive control of vanadium redox flow battery

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JOURNAL OF ENERGY STORAGE 2023年 62卷

作者： Skupin, Piotr Ambati, Seshagiri Rao Silesian Tech Univ Dept Automatic Control & Robot Ul Akademicka 16 PL-44100 Gliwice Poland Indian Inst Petr & Energy Dept Chem Engn Visakhapatnam 530003 Andhra Pradesh India

In this paper, nonlinear model predictive controller (NMPC) is designed for stack voltage control in vanadium redox flow battery (VRFB) and the closed-loop performance is evaluated under different conditions. The controller is designed by using a simplified model of the VRFB system. The flow rate of electrolytes is the manipulated variable and the charging or discharging current is a disturbance. The unmeasured state variables (vanadium ions concentrations) and state of charge of the VRFB are estimated by measuring open-circuit voltage. Numerical simulations show the effectiveness of the NMPC under different operating conditions: when VRFB is balanced and its simplified model is perfectly known, when VRFB is imbalanced and some of its model pa-rameters are not accurately known, and in presence of measurement noise. In all these cases the predictive controller ensures very good tracking performance and rejection of disturbances.

关键词： nonlinear model predictive control Vanadium redox flow battery Electrolyte imbalance Parameter uncertainties Closed-loop performance

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nonlinear model predictive control of crystal size in batch cooling crystallization processes

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JOURNAL OF PROCESS control 2023年第1期128卷

作者： Wang, Liangyong Zhu, Yaolong Gan, Chenyang Northeastern Univ State Key Lab Integrated Automation Proc Ind Shenyang 110819 Peoples R China

The paper proposes a model-based nonlinear model predictive control (NMPC) method for online control of crystal mean size and standard deviation in cooling crystallization process. Image analysis method using deep learning neural network and mathematical statistical method are performed to obtain the mean size and standard deviation of crystal population. The nonlinear prediction model for the NMPC is derived from the input and output data. The effectiveness of the proposed NMPC method is evaluated by the alum cooling crystallization experiments. Experimental results demonstrate the benefits of the proposed combination of image analysis and feedback control of the crystal mean size and standard deviation. The control performance of NMPC is superior to model-free path following control (PFC) method due to the prediction and optimization capabilities of NMPC. & COPY;2023 Elsevier Ltd. All rights reserved.

关键词： Cooling crystallization Crystal size Image analysis Deep learning Path following control nonlinear model predictive control

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nonlinear model predictive control of J2-perturbed impulsive transfer trajectories in long-range rendezvous missions

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AEROSPACE SCIENCE AND TECHNOLOGY 2023年 132卷

作者： Samsam, Sanaz Chhabra, Robin Carleton Univ Dept Mech & Aerosp Engn Ottawa ON K1S5B6 Canada

This paper develops a nonlinear model predictive control (NMPC) strategy for robust tracking of multi -impulse smooth transfer trajectories in J2-perturbed orbital environments. The reference trajectories are designed for long-range rendezvous of servicing satellites with orbiting targets. In the proposed NMPC, the control signals are velocity increments at the impulse times, and the prediction horizon is variable due to the impulsive nature of the reference trajectories. The reference control input and the impulse times are pre-specified by the reference trajectory. Then, the NMPC calculates the optimal correction control vector to track the trajectory with minimum deviation and control effort, during the time between two consecutive impulses. To arrive near the target at the end of the transfer, the optimization in the last horizon is modified to be free-final-time, with an added soft constraint to minimize the final distance between the servicer and the target satellite. To avoid singularities in the mean dynamics of the J2 -perturbed servicer, the equinoctial orbital elements are used in the process model. We also include the first-order long-periodic effects in this model. We investigate the robustness of the proposed NMPC in a simulation environment that additionally models: (i) the first-order short-periodic effects, (ii) a bounded uncertain acceleration to capture unmodelled dynamics, and (iii) burning time for satellite thrusters. Finally, an evolutionary optimization algorithm is implemented and embedded in the controller to decrease the computational complexity and to handle impulsive control inputs. Simulation results are provided to illustrate the tracking effectiveness of the developed controller.(c) 2022 Elsevier Masson SAS. All rights reserved.

关键词： On-orbit servicing Long-range rendezvous Multi-impulse maneuver nonlinear model predictive control Optimal tracking guidance Genetic algorithm

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