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An autonomous emergency braking strategy based on non-linear model predictive deceleration control

IET INTELLIGENT TRANSPORT SYSTEMS 2023年第3期17卷 562-574页

作者： Mu, Hongyuan Li, Zebin Sun, Guozheng Li, Liang Zhao, Yongtao Mei, Mingming Tsinghua Univ State Key Lab Automot Safety & Energy Beijing 100084 Peoples R China Dongfeng Motor Corp Tech Ctr Dept Intelligent Software Ctr Wuhan Peoples R China China Univ Geosci Sch Engn & Technol Beijing Peoples R China

Surrogate safety measures (SSM) are used to assess the risk for autonomous emergency braking system (AEBS). Developing appropriate SSM and accurately executing the braking request are the key issues. Time-to-collision (TTC) is a typical time-based SSM with limitations. By analyzing the braking process, this paper proposes a new SSM based on deceleration rate to avoid collision (DRAC). As the brake actuator, vehicle electronic stability control (ESC) system has many problems, such as large overshoot and pressure fluctuation. Considering the model of hydraulic control unit (HCU) and vehicle, a deceleration controller based on non-linear model predictive control (NMPC) is proposed. Based on this, a layered AEBS architecture is proposed. The upper-layer AEBS controller calculates the expected deceleration based on modified DRAC (MDRAC), and transmits it to the lower-layer NMPC deceleration controller. Finally, the simulation and experimental tests are carried out. The results show that the system has a fast and stable response. In addition, the performance of the proposed AEBS strategy is tested according to the Euro-NCAP test protocol. Comparing the results with the TTC method, the proposed method can improve the stability of the distance margin by more than 0.55 m, which ensures the safety and improves the stability of the vehicle.

关键词： autonomous emergency braking deceleration control deceleration rate to avoid collision non-linear model predictive control

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An Assistive Strategy for Compliantly Actuated Exoskeletons using non-linear model predictive control Method

An Assistive Strategy for Compliantly Actuated Exoskeletons ...

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Iranian Conference on Electrical Engineering

作者： S. M. Tahamipour-Z S. K. Hosseini Sani A. Akbarzadeh I. Kardan Electrical Engineering Department Ferdowsi University of Mashhad Mashhad Iran Mechanical Engineering Department Ferdowsi University of Mashhad Mashhad Iran

Assistive exoskeletons are a category of exoskeleton robots designed to provide extra powers and energies for elderlies or healthy subjects. The control algorithms of these robots, termed as assistive strategies, should control the robot in way that a portion of the user's required torques is provided by the robot actuators. This paper uses non-linear model predictive control (NMPC) method to propose a new assistive strategy for compliantly actuated exoskeletons. The user's required torques are estimated by using an identified model of the human-exoskeleton system. The NMPC method is then used to control the series elastic actuators of the robot to provide a user-selected portion of the estimated torques. The successful performance of the proposed assistive strategy is verified through multiple simulations. The force tracking performance of the NMPC method is also compared with a PID controller. The results clearly show the outperformance of the NMPC method in providing lower tracking errors and less control efforts.

关键词： Assistive control non-linear model predictive control Series elastic actuators

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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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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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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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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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Multi-rate model predictive control of a chemical reactor based on three neural models

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BIOCHEMICAL ENGINEERING JOURNAL 2007年第1期37卷 86-97页

作者： Yu, D. W. Yu, D. L. Liverpool John Moores Univ Sch Engn Control Syst Res Grp Liverpool L3 3AF Merseyside England NE Univ Qinhuangdao Automat Dept Qinhuangdao 066004 Peoples R China

This paper describes a multi-rate model predictive control for a chemical reactor rig. Three multi-input single-output neural network models are developed with different sampling times;each for one reactor output. A multi-rate control strategy is proposed based on the multi-rate models, which enables reduced dimension optimisations to be used in majority of searching for optimal controls. In addition, some practical control techniques are also applied. Effectiveness of the proposed method is evaluated by both simulations and on-line control. A comparison with three decentralised PID controllers is presented. (C) 2007 Elsevier B.V. All rights reserved.

关键词： multi-rate control non-linear model predictive control adaptive neural networks chemical reactors on-line implementation

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Optimal dosing of cancer chemotherapy using model predictive control and moving horizon state/parameter estimation

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2012年第3期108卷 973-983页

作者： Chen, Tao Kirkby, Norman F. Jena, Raj Univ Surrey Div Civil Chem & Environm Engn Guildford GU2 7XH Surrey England Cambridge Univ Hosp NHS Fdn Trust Ctr Oncol Cambridge CB2 2QQ England

model predictive control (MPC), originally developed in the community of industrial process control, is a potentially effective approach to optimal scheduling of cancer therapy. The basis of MPC is usually a state-space model (a system of ordinary differential equations), whereby existing studies usually assume that the entire states can be directly measured. This paper aims to demonstrate that when the system states are not fully measurable, in conjunction with model parameter discrepancy, MPC is still a useful method for cancer treatment. This aim is achieved through the application of moving horizon estimation (MHE), an optimisation-based method to jointly estimate the system states and parameters. The effectiveness of the MPC-MHE scheme is illustrated through scheduling the dose of tamoxifen for simulated tumour-bearing patients, and the impact of estimation horizon and magnitude of parameter discrepancy is also investigated. (C) 2012 Elsevier Ireland Ltd. All rights reserved.

关键词： Cancer therapy Dynamic system Moving horizon estimation non-linear model predictive control Pharmacodynamics Pharmacokinetics

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Adaptive cruise control with stop&go function using the state-dependent nonlinear model predictive control approach

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ISA TRANSACTIONS 2012年第5期51卷 622-631页

作者： Shakouri, Payman Ordys, Andrzej Askari, Mohamad R. Kingston Univ London Sch Mech & Automot Engn Fac Sci Engn & Comp London SW15 3DW England

In the design of adaptive cruise control (ACC) system two separate control loops - an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position - are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements. (C) 2012 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Vehicle dynamic Adaptive cruise control non-linear model predictive control State-dependent linear model

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nonlinear model predictive control of a dual-independent variable valve timing engine with electronic throttle control

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2011年第D9期225卷 1221-1234页

作者： Lee, T-K Filipi, Z. S. Univ Michigan Dept Mech Engn Ann Arbor MI 48109 USA

Significant excursions of engine variables occur during fast transients because of slow actuator responses and system dynamics. This creates adverse effects on dynamic performance and often causes emissions penalties. The challenge is particularly pronounced in engines with an increased number of actuators. In this paper, non-linear model predictive control (NMPC) is introduced to improve the dynamic response of a flexible engine system. NMPC combines advantages of both feed forward and feedback control while considering their constraints. The length of control horizon and prediction horizon are determined to achieve the dead-beat-like optimal control during transients and ensure smooth responses. The NMPC significantly improves the engine torque response and minimizes the excursions of in-cylinder variables under highly transient operation by adjusting each actuator control input simultaneously to achieve the control objectives.

关键词： non-linear model predictive control engine control control oriented model variable valve timing electronic throttle control

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