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Data-Driven modeling and control of Cyclic Variability of an Engine Operating in Low Temperature Combustion Modes

IFAC-PapersOnLine 2021年第20期54卷 834-839页

作者： Sadaf Batool Jeffrey D. Naber Mahdi Shahbakhti Michigan Technological University Houghton MI 49931 USA University of Alberta Edmonton AB Canada

Combustion cyclic variability in an internal combustion engine leads to cyclic variations in the engine torque output and emissions. Combustion cyclic variability is often characterized by coefficient of variation of indicated mean effective pressure (COV IMEP ) that is used as an indicator of combustion stability. These cyclic variations are inevitable and cannot be completely eliminated but can be controlled to allow stable engine operation. This work focuses on control oriented modeling of COV IMEP to limit engine cyclic variations in low temperature combustion (LTC) modes. COV IMEP is generally stochastic in nature; thus, a data-driven approach is used to develop a predictive model of COV IMEP for Homogeneous Charge Compression Ignition (HCCI) and Reactivity controlled Compression Ignition (RRCI) modes. This work presents the development of a cycle-by-cycle model predictive controller for a 2.0 liter multi-mode LTC engine. Physics-based control-oriented models for combustion phasing (CA50) and IMEP are augmented with the new data-driven COV IMEP model to limit the cyclic variations below 3% for HCCI and RCCI modes. These models are then used to design closed-loop non-linear model predictive controllers to control CA50 and IMEP while constraining COV IMEP to ensure stable engine operation for varying load conditions.

关键词： Combustion Stability Machine Learning non-linear model predictive control Homogeneous Charge Compression Ignition (HCCI) Reactivity controlled Compression Ignition (RCCI)

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Visual predictive control of Robotic Arms with Overlapping Workspace 16

Visual Predictive Control of Robotic Arms with Overlapping W...

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16th International Conference on Informatics in control, Automation and Robotics (ICINCO)

作者： Le Flecher, E. Durand-Petiteville, A. Cadenat, V Sentenac, T. CNRS LAAS 7 Ave Colonel Roche F-31400 Toulouse France Univ Toulouse UPS F-31400 Toulouse France Fed Univ Pernambuco UFPE Mech Dept Recife PE Brazil

ISBN: (纸本)9789897583803

This paper deals with multi-arms fruits picking in orchards. More specifically, the goal is to control the arms to approach the fruits position. To achieve this task a VPC strategy has been designed to take into account the dynamic of the environment as well as the various constraints inherent to the mechanical system, visual servoing manipulation and shared workspace. Our solution has been evaluated in simulation using on PR2 arms model. Different models of visual features prediction have been tested and the entire VPC strategy has been run on various cases. The obtained results show the interest and the efficiency of this strategy to perform a fruit picking task.

关键词： Agricultural Robotics Multi-arms non-linear model predictive control Image based Visual Servoing

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A non-linear model predictive controller with obstacle avoidance for a space robot

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ADVANCES IN SPACE RESEARCH 2016年第8期57卷 1737-1746页

作者： Wang, Mingming Luo, Jianjun Walter, Ulrich Tech Univ Munich Inst Astronaut D-85748 Garching Germany Northwestern Polytech Univ Sci & Technol Aerosp Flight Dynam Lab Xian 710072 Peoples R China

This study investigates the use of the non-linear model predictive control (NMPC) strategy for a kinematically redundant space robot to approach an un-cooperative target in complex space environment. Collision avoidance, traditionally treated as a high level planning problem, can be effectively translated into control constraints as part of the NMPC. The objective of this paper is to evaluate the performance of the predictive controller in a constrained workspace and to investigate the feasibility of imposing additional constraints into the NMPC. In this paper, we reformulated the issue of the space robot motion control by using NMPC with predefined objectives under input, output and obstacle constraints over a receding horizon. An on-line quadratic programming (QP) procedure is employed to obtain the constrained optimal control decisions in real-time. This study has been implemented for a 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a 6 DOF free-floating spacecraft via simulation studies. Real-time trajectory tracking and collision avoidance particularly demonstrate the effectiveness and potential of the proposed NMPC strategy for the space robot. (C) 2015 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： non-linear model predictive control Obstacle avoidance Space robot

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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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A Parameterized NMPC Scheme for Embedded control of Semi-active Suspension System

A Parameterized NMPC Scheme for Embedded Control of Semi-act...

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6th International-Federation-of-Automatic-control (IFAC) Conference on nonlinear-model-predictive-control (NMPC)

作者： Rathai, Karthik Murali Madhavan Alamir, Mazen Sename, Olivier Tang, Rattena Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France

This paper proposes a parameterized NMPC scheme for control of semi-active suspension system for a quarter car vehicle model. The proposed controller is compared against a linearization based MPC to verify its performance under real-time (RT) embedded conditions. The linearization based MPC method linearizes the non-linear dynamical system/constraints at the current operating point and a linear MPC problem is solved by means of a quadratic program (QP) solver to obtain the optimal control input. The proposed parameterized NMPC method finitely parameterizes the control input and for each parameterized input, the nonlinear dynamical system is numerically simulated and the optimal input is elicited from the simulations which minimizes the objective function and satisfies the system constraints. The methods were considered on the basis of RT implementable scenario and practical viability for the target system. The methods were successfully tested by means of hardware in the loop (HIL) simulations under RT conditions for given control objective on dSPACE MicroAutoBox II hardware. The results from HIL simulations exudes better performance of parameterized NMPC against linearization based MPC in terms of computational efficiency and RT feasibility of the controller. (C) 2018, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Automotive control non-linear model predictive control Real-time control Vertical Dynamics

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Distributed nonlinear MPC of Multi-Agent Systems With Data Compression and Random Delays

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IEEE TRANSACTIONS ON AUTOMATIC control 2016年第3期61卷 817-822页

作者： El-Ferik, Sami Siddiqui, Bilal A. Lewis, Frank L. King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran Saudi Arabia DHA Suffa Univ Dept Mech Engn Karachi Pakistan Univ Texas Arlington Res Inst Arlington TX 76019 USA

This note proposes an Input to State practically Stable (ISpS) formulation of distributed nonlinear model predictive controller (NMPC) for formation control of constrained autonomous vehicles in presence of communication bandwidth limitation and transmission delays. Planned trajectories are compressed using neural networks resulting in considerable reduction of data packet size, while being robust to propagation delays and uncertainty in neighbors' trajectories. Collision avoidance is achieved by means of spatially filtered potential field. Analytical results proving ISpS and generalized small gain conditions are presented for both strongly-and weakly-connected networks, and illustrated by simulations.

关键词： Collision avoidance distributed nonlinear control formation control multi-agent systems neural network non-linear model predictive control

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Decoupling Neuro-Fuzzy model predictive controllers Applied to Quadruple Tanks 8

Decoupling Neuro-Fuzzy Model Predictive Controllers Applied ...

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8th IEEE International Conference on Intelligent Systems (IS)

作者： Ahmed, Sevil Ganchev, Ivan Taneva, Albena Petrov, Michail Tech Univ Sofia Plovdiv Branch Control Syst Dept 25Tsanko Dyustabanov St Plovdiv 4000 Bulgaria

ISBN: (纸本)9781509013531

This paper is focused on the design of an algorithm for decoupling multivariable systems based on generalized predictive control and its application to control of a quadruple tank system. The quadruple-tank process is representative of a multivariable control problem that may be found in a chemical engineering process control application. To handle the nonlinearities, a Takagi-Sugeno neuro-fuzzy system is suggested to model the plant dynamics at relevant operating regions. The developed model is used as a predictive model for a decoupling model predictive control algorithm.

关键词： decoupling fuzzy-neural model predictive control non-linear model predictive control Takagi-Sugeno fuzzy models quadruple tanks

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Runge-Kutta model-based adaptive predictive control mechanism for non-linear processes

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2013年第2期35卷 166-180页

作者： Iplikci, Serdar Pamukkale Univ Dept Elect & Elect Engn Denizli Turkey

This paper proposes a novel non-linear model predictive control mechanism for non-linear systems. The idea behind the mechanism is that the so-called Runge-Kutta model of a continuous-time non-linear system can be regarded as an approximate discrete model and employed in a generalized predictive control loop for prediction and derivative calculation purposes. Additionally, the Runge-Kutta model of the system is used for state estimation in the extended Kalman filter framework and online parameter adaptation. The proposed method has been tested on two different non-linear systems. Simulation results have revealed the effectiveness of the proposed method for different cases.

关键词： Extended Kalman filter non-linear model predictive control optimal control parameter estimation

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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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Generalized nonlinear Optimal predictive control using Iterative State-space Trajectories: Applications to Autonomous Flight of UAVs

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2015年第2期13卷 361-370页

作者： Murillo, Marina H. Limache, Alejandro C. Rojas Fredini, Pablo S. Giovanini, Leonardo L. Consejo Nacl Invest Cient & Tecn Res Ctr Computat Methods CIMEC Santa Fe Argentina Univ Nacl Litoral FICH Res Ctr Signals Syst & Computat Intelligence SINC Santa Fe Argentina

model predictive control (MPC) is a modern technique that, nowadays, encapsulates different optimal control techniques. For the case of non-linear dynamics, many possible variants can be developed which can lead to new control algorithms. In this manuscript a novel generic control system method is presented. This method can be applied to control, in an optimal way, different systems having non-linear dynamics. Particularly, in this paper, the proposed technique is presented in the context of developing a control system for autonomous flight of UAVs. This technique can be used for different types of aerial vehicles having any type of generic non-linear dynamics. The presented method is based on the use of iteratively defined optimal candidate state-space trajectories in global state-space. The method uses, a generalized linearization process which, opposite to standard methods, does not need to be predefined in a certain equilibrium state but instead it is performed along any arbitrary state. The technique allows the inclusion of constraints with ease. The presented technique is used as a centralized control system unit that is able to control the full aircraft dynamics without the need of decoupling the system in different reduced modes. The technique is tested by making a Cessna 172 airplane model to perform the following autonomous unmanned maneuvers: climbing at constant speed to a desired altitude, heading change to a desired flight direction, and, coordinate turn.

关键词： non-linear model predictive control optimization state-space trajectories prediction unmanned aerial vehicles

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