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21st IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Ting Liu, Zhilin Zhang, Xiaoyue Ma, Yingkai Harbin Engn Univ Coll Intelligent Syst Sci & Engn 145 NanTong St Harbin 150001 Heilongjiang Peoples R China

ISBN: (纸本)9798350388084;9798350388077

This paper proposes a structural reliability-based position mooring control scheme subject to unknown environment disturbance, system uncertainties and control input constraints. Structural reliability of mooring cables is proposed as a breaking strength evaluating index to derive the mathematical model of position mooring system. A nonlinear extended state observer is constructed to estimate position, velocity and total system uncertainties, it is independent of an accurate system model and prior knowledge. Considering the constraints of structural reliability, control input and control increment, a structural reliability-based nonlinear model predictive controller is proposed to solve control input saturation. It is proved that the proposed controller can maintain the structural reliability and heading at the desired value. It is able to fully use the mooring system for positioning while keeping the cable safe, so that the vessel can realize the safe and energy-efficient area keeping. Comparative simulations are performed to demonstrate the effectiveness of the proposed position mooring control scheme.

关键词： Position Mooring nonlinear extended State Observer nonlinear model predictive controller Structural Reliability

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Disturbance Observer based Lateral and Longitudinal NMPC for Agricultural Harvester Trajectory Tracking Control 14

Disturbance Observer based Lateral and Longitudinal NMPC for...

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14th Asian Control Conference (ASCC)

作者： Chen, Gang Gao, Qian Xu, Hongzu Zhang, Ying Wang, Mingbang Chen, Jian Lv, Chengxing Qingdao Univ Technol Sch Informat & Control Engn Qingdao Peoples R China Qingdao High Tech Ind Dev Co Ltd Qingdao Peoples R China

ISBN: (纸本)9798331540845;9789887581598

In this paper, a disturbance observer based nonlinear model predictive controller is proposed for autonomous agricultural harvester with complex environments. Firtstly, a disturbance observer based radial basis function neural network technical is designed to handle external disturbances, such as friction of tires. Then, a nonlinear model predictive controller with pseudo-Huber function is used to handle the trajectory tracking control issues of the agricultural harvesters. The simulation results show that the proposed controller has good tracking accuracy and robustness.

关键词： Trajectory Tracking nonlinear model predictive controller Disturbances observer

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predictive CONTROL OF THE KINOVA GEN3 ROBOTIC MANIPULATOR USING A nonlinear model

PREDICTIVE CONTROL OF THE KINOVA GEN3 ROBOTIC MANIPULATOR US...

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ASME International Mechanical Engineering Congress and Exposition (IMECE)

作者： Tereda, Amanuel Abrdo Yi, Sun North Carolina Agr & Tech State Univ Dept Mech Engn Greensboro NC 27411 USA

ISBN: (纸本)9780791887639

In contemporary manufacturing processes, reliable but efficient pick-and-place robots are frequently used. The automation and optimization of the pick and place procedures utilizing various path-planning approaches thereby support the expansion of application areas. Yet, the design of a controller faces significant difficulties due to the nonlinearities inherent in robotic manipulators and the unpredictable nature of the ambient factors. In place of the classic model predictive control (MPC), this paper presents the application of the nonlinear model predictive controller (NLMPC) as an acceptable control mechanism for real-time optimization and robust stability of the KINOVA Gen3 robotic arm. The developed NLMPC-based method ensures that the robotic arm does not run into obstacles in the workplace or with itself while reaching, gripping, selecting, and placing the necessary items. To acquire the control input trajectory, the optimization in NLMPC is solved repeatedly. When input constraints are available, the modeled system tracks reference trajectories to achieve the aim of recognizing and organizing distinct objects. After the NLMPC is successfully developed, a simulation environment is built and finally brought to life by combining all the processes into one using a MATLAB Stateflow chart.

关键词： Automation optimization path-planning nonlinear model predictive controller KINOVA Gen3 stateflow

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An energy-efficient dynamic positioning controller for high sea conditions

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APPLIED OCEAN RESEARCH 2022年 129卷

作者： Alagili, Osama Khan, Mohammad Aminul Islam Ahmed, Salim Imtiaz, Syed Zaman, Hasanat Islam, Mohammed Mem Univ Newfoundland Fac Engn & Appl Sci St John NF A1B 3X5 Canada CNR 1 Arctic Ave St John NF A1B 3T5 Canada

In constantly changing high sea environments characterized by large waves, turbulent surface currents, and high wind gusts, a dynamic positioning (DP) system with enhanced positioning capabilities is becoming essential. The controller in a DP system calculates the force needed by the propellers and thrusters to counteract environmental forces and keep the vessel at its desired position. In addition to maintaining the position, minimizing the thruster demand is another control objective pursued by a type of controllers, termed as energy-efficient DP or the so-called "Green DP". In line with this objectives, the present work proposes a controller that minimizes thruster demand while maintaining position constraints. This novel energy-efficient controller (i.e., like Green-DP) exploits the structure of the economic nonlinear model predictive controller (ENMPC) and adopts "green"objectives and performance metrics, including thruster energy efficiency. The MATLAB/Simulink simulator and toolbox add-ons were used in a simulation environment to demonstrate the performance of the proposed energy efficient DP, called Green-NMPC. The controller was tested for moderate to high sea wave conditions. The controller reduced up to 50% thruster demand in sway direction compared to NMPC while maintaining the vessel positioning objectives.

关键词： Dynamic positioning Control algorithms Autonomous ships nonlinear model predictive controller

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predictive kinetic energy management for an add-on driver assistance eco-driving of heavy vehicles

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第13期14卷 1824-1834页

作者： Yoon, DoHyun Daniel Ayalew, Beshah Ivanco, Andrej Loiselle, Keith Clemson Univ Dept Automot Engn 4 Res Dr Greenville SC 29607 USA Allison Transmiss Inc One Allison Way Indianapolis IN 46222 USA

This study presents a radar-based predictive kinetic energy management (PKEM) framework that is applicable as an add-on driver assistance module for a heavy vehicle with an internal combustion engine powertrain. The proposed framework attempts to minimise fuel consumption by estimating the motion of the leading vehicle from radar information and optimising the inputs to the ego vehicle in a predictive manner. The PKEM framework consists of a driver-pedal pre-filter, an interacting multiple model radar-based filter and predictor of traffic object states, and a non-linear model predictive controller. The framework is integrated with established human-driver car-following models representing various driving styles and evaluated over a set of standardised driving cycles. The authors found that the energy-saving benefits can be as much as 23% over the baseline driver-only case with minimal compromises on travel time in urban environments, while the benefits are nearly negligible on the highway cycle. The results included also show the potential trade-offs in accommodating driver-desired inputs.

关键词： automobiles power transmission (mechanical) fuel economy hybrid electric vehicles energy consumption internal combustion engines road traffic road safety optimisation predictive control driver information systems road vehicles nonlinear model predictive controller established human-driver car-following models driving styles standardised driving cycles baseline driver-only case driver-desired inputs driver assistance eco-driving heavy vehicle radar-based predictive kinetic energy management framework driver assistance module internal combustion engine powertrain leading vehicle radar information ego vehicle predictive manner PKEM framework driver-pedal pre-filter interacting multiple model radar-based filter

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Ecological Adaptive Cruise Control With Optimal Lane Selection in Connected Vehicle Environments

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第11期21卷 4538-4549页

作者： Tajeddin, Sadegh Ekhtiari, Sanaz Faieghi, Mohammadreza Azad, Nasser L. Univ Waterloo Dept Syst Design Engn Waterloo ON N2L 3G1 Canada Zebra Technol Mississauga ON L5N 7J9 Canada Gen Motors Canada Markham ON L3R 4H8 Canada Univ Hlth Network Toronto Rehabil Inst Intelligent Assist Technol & Syst Lab Toronto ON M5G 2A2 Canada

Recent advances in transportation have enabled lane-specific measurements and lane-specific control. This paper makes use of such data to promote energy efficiency of vehicles. In particular, a Multi-Lane Adaptive Cruise controller (MLACC) is designed which determines the optimal velocity and laneto-drive in real-time. This cruise controller solves lane-specific optimization problems to compute an instantaneous trip cost for each lane and selects the lane that poses the lowest cost. The optimization tasks incorporate future route data and encompass multiple objectives including safety, energy efficiency and desired velocity tracking. Therefore, they can be treated as distinct nonlinear model predictive Control (NMPC) problems that have to be solved altogether in each sampling time. To handle the computational load of solving multiple NMPCs in real-time, an integration of Newton and Generalized Minimal Residual numerical methods is employed. The proposed MLACC is implemented for a 2013 Toyota Prius and a wide range of simulation studies are performed to examine the controller. Specifically, hardware-in-the-loop experiments are utilized to evaluate the real-time implementability of the controller. In addition, extensive model-in-the-loop simulations are carried out and the results are compared with driver-in-the-loop experiments. Simulation results indicated that speed profiles and lane changes suggested by MLACC yield up to 27% improvement in energy consumption compared to human drivers.

关键词： Lane change adaptive cruise control intelligent transportation systems advanced driving assistant systems nonlinear model predictive controller real-time optimization generalized minimal residual

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On-line extreme learning algorithm based identification and non-linear model predictive controller for way-point tracking application of an autonomous underwater vehicle

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COGNITIVE COMPUTATION AND SYSTEMS 2019年第3期1卷 61-71页

作者： Rath, Biranchi Narayan Subudhi, Bidyadhar Natl Inst Technol Rourkela Dept Elect Engn Rourkela 769008 India Indian Inst Technol Goa Sch Elect Sci Goa Coll Engn Campus Ponda 403401 Goa India

In most of the surveillance applications of autonomous underwater vehicle (AUV), very often it is intended to follow the desired horizontal way-points, where some oceanography data need to be collected. In view of this, the motion planning algorithm using way-points is investigated in this study. The proposed work involves identification of dynamics of AUV and design of adaptive model predictive controllers which includes linear adaptive model predictive controller (LAMPC) and non-linear adaptive model predictive controller (NAMPC). Owing to the fast convergence rate and robustness property, on-line sequential extreme learning machine (OS-ELM) is employed for estimating the dynamics of AUV. To improve the OS-ELM modelling performance, Jaya optimisation algorithm is applied to optimise the hidden layer parameters. The desired surveillance region is formulated in terms of way-points using heading angle obtained from desired line-of-sight path. Simulations are performed using MATLAB by applying proposed NAMPC, LAMPC and a previously reported optimal controller, namely inverse optimal self-tuning PID (IOSPID) controller. Subsequently, real-time experimentation is performed using a prototype AUV in a swimming pool. From the simulation and experimental results, it is observed that the proposed controller exhibit efficient tracking performance in face of actuator constraints as compared to LAMPC and IOSPID controller.

关键词： position control path planning optimal control self-adjusting systems mobile robots three-term control identification neurocontrollers optimisation autonomous underwater vehicles adaptive control nonlinear control systems predictive control remotely operated vehicles regression analysis robot dynamics learning systems control system synthesis desired surveillance region LAMPC reported optimal controller inverse optimal self-tuning PID controller IOSPID controller way-point tracking application autonomous underwater vehicle surveillance applications global positioning system coordinates underactuated AUV heading dynamics adaptive model predictive controllers system identification fast convergence rate robustness property on-line sequential extreme learning machine OS-ELM neural network modelling performance Jaya optimisation algorithm forward-regression orthogonal least square nonlinear model predictive controller OS-ELM model surveying application line-of-sight path on-line extreme learning algorithm based identification horizontal way-points NAMPC error reduction ratio ERR criteria

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Liquid Level Tracking Control of Three-tank Systems 37

Liquid Level Tracking Control of Three-tank Systems

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37th Chinese Control Conference (CCC)

作者： Yu, Shuyou Zhou, Yu Feng, Yangyang Qu, Ting Chen, Hong Jilin Univ State Key Lab Automot Simulat & Control Changchun 130012 Jilin Peoples R China Jilin Univ Dept Control Sci & Engn Changchun 130012 Jilin Peoples R China

ISBN: (纸本)9789881563958

In this paper, a liquid level tracking controller which is composed of a feedforward controller and a feedback controller is proposed for three-tank systems. Firstly, the flat property of three-tank systems is verified and a feedforward controller is designed accordingly so as to track the ideal trajectories. Secondly, in order to eliminate the tracking errors introduced by model uncertainties or unknown disturbances, a nonlinear model predictive controller is designed in which a terminal equality constraint is added for ensuring asymptotic convergence. Finally, the control performance is confirmed by both simulation and experiment results.

关键词： Liquid Level Tracking Three-tank Systems Flat System nonlinear model predictive controller

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Liquid Level Tracking Control of Three-tank Systems

Liquid Level Tracking Control of Three-tank Systems

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第37届中国控制会议

作者： Shuyou Yu Yu Zhou Yangyang Feng Ting Qu Hong Chen State Key Laboratory of Automotive Simulation and Control Jilin University Department of Control Science and Engineering Jilin University

关键词： Liquid Level Tracking Three-tank Systems Flat System nonlinear model predictive controller

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nonlinear Incremental model predictive Control in In-wheels-motored Electric Vehicles

Nonlinear Incremental Model Predictive Control in In-wheels-...

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Chinese Automation Congress (CAC)

作者： Liu, Liang Shi, Ke Yuan, Xiaofang Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Guilin Univ Elect Technol Coll & Univ Key Lab Intelligent Integrated Automa Guilin 541004 Peoples R China

ISBN: (纸本)9781538635247

This paper focuses on yaw stability control of in wheel -motored electric vehicles (EVs), a nonlinear incremental model predictive control combines the error prediction control is proposed. By inputting a suitable reference model from driver action, the controller output of EV stability tracks the input. So this problem can be described as a tracking problem under constraints. To solve tracking problem, a nonlinear model predictive controller is designed. To solve the dynamics model error, an error predictor is designed. Finally, the nonlinear incremental MPC for EV is verified on simulation platform and simulation results of different steering condition show the benefits of the proposed control strategy.

关键词： electric vehicle nonlinear model predictive controller error predictor

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