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nonlinear model predictive control of a cutting process

NEUROCOMPUTING 2002年第1-4期43卷 107-126页

作者： Potocnik, P Grabec, I Univ Ljubljana Fac Mech Engn SI-1000 Ljubljana Slovenia

nonlinear model predictive control (MPC) of a simulated chaotic cutting process is presented. The nonlinear MPC combines a neural-network model and a genetic-algorithm-based optimizer. The control scheme comprises a process, a model, an optimizer, a controller and a corrector. Neural networks are used to build a nonlinear experimental model of the process which is applied to recursive prediction in MPC, A robust genetic-algorithm-based optimizer is used for the optimization of control trajectories, A neural-network-based controller is included in the control scheme for enhanced optimizer initialization and for autonomous control after the learning period. The nonlinear MPC is applied to control the simulated chaotic cutting process, The dynamics of a cutting process are very complex due to the nonlinear effects of high order involved. The control objective is to construct an on-line control system capable of improving the quality of the manufactured surface by preventing tool oscillations which result in the rough surface of the workpiece. A feedforward network is applied as an experimental model of the cutting process, and MPC strategy with tool support manipulation as a control variable is investigated. The results show considerable improvement of the manufacturing quality obtained by the proposed nonlinear model predictive control. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： nonlinear model predictive control neural networks genetic algorithms cutting process

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nonlinear model predictive control with relevance vector regression and particle swarm optimization

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控制理论与应用（英文版） 2013年第4期11卷 563-569页

作者： M.GERMIN NISHA G.N.PILLAI Department of Electrical Engineering Indian Institute of Technology Roorkee

In this paper, a nonlinear model predictive control strategy which utilizes a probabilistic sparse kernel learning technique called relevance vector regression （RVR） and particle swarm optimization with controllable random exploration velocity （PSO-CREV） is applied to a catalytic continuous stirred tank reactor （CSTR） process. An accurate reliable nonlinear model is first identified by RVR with a radial basis function （RBF） kernel and then the optimization of control sequence is speeded up by PSO-CREV. Additional stochastic behavior in PSO-CREV is omitted for faster convergence of nonlinear optimization. An improved system performance is guaranteed by an accurate sparse predictive model and an efficient and fast optimization algorithm. To compare the performance, model predictive control （MPC） using a deterministic sparse kernel learning technique called Least squares support vector machines （LS-SVM） regression is done on a CSTR. Relevance vector regression shows improved tracking performance with very less computation time which is much essential for real time control.

关键词： Relevance vector regression Least squares support vector machines nonlinear model predictive control Particle swarm optimization with controllable random exploration velocity

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nonlinear model predictive control of Mild Hybrid Powertrains With Electric Supercharging

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第9期70卷 8490-8504页

作者： Griefnow, Philip Jakoby, Moritz Doerschel, Lorenz Andert, Jakob FEV Europe GmbH Dept Vehicle Elect Elect D-52078 Aachen Germany Rhein Westfal TH Aachen Teaching & Res Area Mechatron Mobile Prop D-52074 Aachen Germany Rhein Westfal TH Aachen Inst Automat Control D-52074 Aachen Germany

Energy management plays a decisive role in the design of efficient 48V mild hybrid drives owing to the limited amounts of energy and power. In particular, strong nonlinearities of an electrified engine air path and the high interaction between the 48V mild hybrid powertrain and the electrical system pose a major challenge. Current research results show the fundamental potential of optimization-based energy management strategies. However there is a lack of optimization-based solutions that allow online direct control of the electrified air path and the electric motor. In this context, this study presents a nonlinear model predictive control (NMPC) approach for a 48V mild hybrid powertrain with an electric supercharger, which is able to simultaneously improve the response behavior and energy consumption. The control concept is developed on the basis of a detailed system description. The special features of optimization-based control for the degrees of freedom of the powertrain, i.e., the belt starter generator (BSG) and the electrified air path through a throttle, waste gate, and electric supercharger (eC), are addressed. After an analysis and verification of the control properties, the NMPC is evaluated in dynamic driving cycle simulations through a comparison with a state-of-the-art rule-based control strategy. The investigations show that the NMPC is able to control the system well even under transient situations with a high influence of disturbance variables. In addition, the NMPC allows a specific and fuel saving use of the 48V system while improving the driving dynamics at the same time.

关键词： Mechanical power transmission Mathematical model Torque Batteries Fuels Vehicle dynamics Optimization 48V Electrified Engine Air Path Electric Compressor Energy Management Mild Hybrid nonlinear model predictive control Powertrain control Supercharger

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nonlinear model predictive control for dividing wall columns

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AICHE JOURNAL 2023年第6期69卷 e18062-e18062页

作者： Qian, Xing Lin, Kuan-Han Jia, Shengkun Biegler, Lorenz T. Huang, Kejin Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China Carnegie Mellon Univ Chem Engn Dept Pittsburgh PA 15213 USA Tianjin Univ State Key Lab Chem Engn Collaborat Innovat Ctr Chem Sci & Engn Tianjin Sch Chem Engn & TechnolChem Engn Res Ctr Beiyangyuan Campus Yaguan Rd 135 Tianjin 300350 Peoples R China

Dividing wall columns (DWCs) are practical, effective, and promising among distillation process intensification technologies. nonlinear model predictive control (NMPC) schemes are developed in this study to control the three-product DWCs. As these systems are intensely interactive and highly nonlinear, NMPC may be more suitable than the traditional PI control. The model is established based on Python and Pyomo platforms. As the original mathematical model of the column section is ill-posed, index reduction is used to avoid a high-index differential-algebraic equation (DAE) system. The well-posed index-1 system after index reduction is employed for the steady-state simulation and dynamic control in this study. Case studies with three DWC configurations to separate the mixture of ethanol (A), n-propanol (B), and n-butanol (C) show that the NMPC performs very well with small maximum deviations and short settling times. This demonstrates that the NMPC is a feasible and very effective scheme to control three-product DWCs.

关键词： distillation dividing wall column nonlinear model predictive control

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nonlinear model predictive control FOR PROCESSES WITH COMPLEX DYNAMICS: A PARAMETERISATION APPROACH USING LAGUERRE FUNCTIONS

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2020年第1期30卷 35-46页

作者： Lawrynczuk, Maciej Warsaw Univ Technol Inst Control & Computat Engn Ul Nowowiejska 15-19 PL-00665 Warsaw Poland

Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.

关键词： process control nonlinear model predictive control Laguerre functions linearisation

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nonlinear model predictive control for trajectory tracking of quadrotors using Lyapunov techniques

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Science China(Information Sciences) 2022年第6期65卷 269-270页

作者： Dong WANG Quan PAN Jinwen HU Chunhui ZHAO School of Automation Northwestern Polytechnical University

Dear editor,In recent years, the research on trajectory tracking and collision avoidance control of quadrotor unmanned aerial vehicles(UAVs) has gained considerable attention [1, 2]. Since quadrotor UAVs are used exte... 详细信息

关键词： trajectory tracking obstacle avoidance input constraints commercial quadrotors nonlinear model predictive control

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nonlinear model predictive control of a discrete-cycle gasoline-controlled auto ignition engine model: Simulative analysis

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INTERNATIONAL JOURNAL OF ENGINE RESEARCH 2019年第10期20卷 1025-1036页

作者： Nuss, Eugen Wick, Maximilian Andert, Jakob De Schutter, Jochem Diehl, Moritz Abel, Dirk Albin, Thivaharan Rheinisch Westfalische TH Aachen Inst Automat Control Campus Blvd 30 D-52074 Aachen Nordrhein Westf Germany Rheinisch Westfalische TH Aachen Mechatron Syst Combust Engines Aachen Germany Albert Ludwigs Univ Freiburg Dept Microsyst Engn Syst Control & Optimizat Lab Freiburg Germany Swiss Fed Inst Technol Inst Dynam Syst & Control Zurich Switzerland

Gasoline-controlled auto ignition is a promising technology capable of reducing both fuel consumption and emissions at the same time. There are, however, challenges to overcome in order to make practical use of it. One area of research addresses methods that guarantee stable combustion as gasoline-controlled auto ignition is very sensitive to disturbances. This article investigates the capability of nonlinear model predictive control to ensure stable combustion while maintaining efficient operation. For this purpose, a suitable gasoline-controlled auto ignition model is selected and identified using measurement data of a single-cylinder test bed. Building upon this model, a controller based on nonlinear model predictive control is derived and analyzed by means of simulation. The investigation shows that the control manages to follow prescribed set points, also for late combustion, and indicates promising results with respect to real-time computation constraints.

关键词： Homogeneous charge compression ignition gasoline-controlled auto ignition combustion control nonlinear model predictive control model predictive control controlled auto ignition

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nonlinear model predictive control Strategy for Limit Management of Aero-Engines

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2022年第3期39卷 427-438页

作者： Du, Xian Ma, Yan-Hua Sun, Xi-Ming Dalian Univ Technol Sch Microelect 2 Linggong Rd Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn 2 Linggong Rd Dalian 116024 Peoples R China

A Min-Max structure combined with several linear limit controllers are utilized for conventional aero-engines safety control, which is complex and conservative, therefore a new nonlinear model predictive control strategy is proposed that owns the ability to manage time-varying input/output limits during acceleration/deceleration transient states of aero-engines. Penalty function items are constructed according to the characteristics of input/output limits, and are incorporated in the cost function of online receding horizon optimization for limit management of aero-engines. Considering the large computational burden of nonlinear component-level aero-engine model directly used as prediction model, a piecewise linear model is built to describe the steady-state and dynamic properties of aero-engines and its design process is explained in detail. Further, computational efforts are investigated as control horizon, prediction horizon are varied in the improved nonlinear predictive controller. Particularly, when penalty function items are activated, control performance and computational burden are examined, thus providing guidelines for key parameters tuning. Simulations show two clear results. One is that computational efforts become larger for bigger control horizon or prediction horizon, but it depends more on control horizon than prediction horizon. The other is that the proposed method can handle time-varying input/output limits effectively without resulting in a great increase of computational burden.

关键词： aero-engine control nonlinear model predictive control limit management penalty function piecewise linear model computational efforts

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nonlinear model predictive control Applied to a Generic Ocean-Wave Energy Extractor

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JOURNAL OF OFFSHORE MECHANICS AND ARCTIC ENGINEERING-TRANSACTIONS OF THE ASME 2014年第4期136卷 041901-1-041901-12页

作者： Tom, Nathan Yeung, Ronald W. Univ Calif Berkeley Dept Mech Engn Ocean Engn Major Field Grp Berkeley CA 94720 USA Univ Calif Berkeley Dept Mech Engn Computat Marine Mech Lab CMML Amer Bur ShippingInaugural Chair Ocean Engn Berkeley CA 94720 USA

This paper evaluates the theoretical application of nonlinear model predictive control (NMPC) to a model-scale point absorber for wave energy conversion. The NMPC strategy will be evaluated against a passive system, which utilizes no controller, using a performance metric based on the absorbed energy. The NMPC strategy was setup as a nonlinear optimization problem utilizing the interior point optimizer (IPOPT) package to obtain a time-varying optimal generator damping from the power-take-off (PTO) unit. This formulation is different from previous investigations in model predictive control, as the current methodology only allows the PTO unit to behave as a generator, thereby unable to return energy to the waves. Each strategy was simulated in the time domain for regular and irregular waves, the latter taken from a modified Pierson-Moskowitz spectrum. In regular waves, the performance advantages over a passive system appear at frequencies near resonance while at the lower and higher frequencies they become nearly equivalent. For irregular waves, the NMPC strategy leads to greater energy absorption than the passive system, though strongly dependent on the prediction horizon. It was found that the ideal NMPC strategy required a generator that could be turned on and off instantaneously, leading to sequences where the generator can be inactive for up to 50% of the wave period.

关键词： Generator Performance metrics Power output Wave Period TIME-DOMAIN nonlinear model predictive control Wave energy conversion Irregular waves extraction device dynamos

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nonlinear model predictive control approach in design of Adaptive Cruise control with automated switching to cruise control

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control ENGINEERING PRACTICE 2014年第1期26卷 160-177页

作者： Shakouri, Payman Ordys, Andrzej Kingston Univ London Sch Mech & Automot Engn London SW15 3DW England

In this paper the nonlinear model predictive control (NMPC) is used in designing of Adaptive Cruise control (ACC) and Cruise control (CC) systems. An algorithm is proposed to carry out automatic switching between ACC and CC, depending on the situation in front of the vehicle. Also, an algorithm based on MPC equation is devised to obtain the prediction of future reference trajectories corresponding to desired speed and distance. NMPC equation used in this paper is developed based on state-dependent representation of linear models corresponding to the modes of the operation: accelerating-throttle is active and braking-brake is active. The developed automated ACC system is tested in simulation against different scenarios proving good performance of the system. Furthermore, the results of proposed control algorithm based on NMPC methods are compared with a different ACC structure. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Vehicle dynamic Adaptive Cruise control model predictive control nonlinear model predictive control State-dependent linear model

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