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Dynamic Leader Allocation in Multi-robot Systems Based on nonlinear model predictive control

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2020年第2期98卷 359-376页

作者： Tavares, Augusto de Holanda B. M. Madruga, Sarah Pontes Brito, Alisson V. Nascimento, Tiago P. Univ Fed Paraiba Dept Comp Syst Lab Syst Engn & Robot Joao Pessoa Paraiba Brazil

This paper presents an approach to the dynamic leader selection problem in autonomous non-holonomic mobile robot formations when the current leader enters a failure state. Our method is based on a tree structure coupled with a modified version of the nonlinear model predictive control (NMPC) that allows for behavior change at the controller level. An explanation of the control algorithm, behavior selection, and leader selection structure is given, after which the results of both simulations and experiments using a three robot formation are shown and discussed.

关键词： nonlinear model predictive control Behavior-based robotics Formation control Multi-robot systems

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Fast reduced multiple shooting methods for nonlinear model predictive control

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CHEMICAL ENGINEERING AND PROCESSING-PROCESS INTENSIFICATION 2007年第11期46卷 1200-1214页

作者： Schaefer, Andreas Kuehl, Peter Diehl, Moritz Schloeder, Johannes Bock, Hans Georg Heidelberg Univ Interdisciplinary Ctr Sci Computing IWR D-6900 Heidelberg Germany

nonlinear model predictive control (NMPC) has become an appealing control concept for chemical processes because it can directly take into account the multivariable character, nonlinearities and constraints on inputs and states. While linear MPC is frequently applied in the process industries, practical applications of NMPC are rare. One reason is the relatively large computation time still needed to solve the nonlinear constrained optimization problems inherent to NMPC schemes. For faster controller response, the use of an extended partially reduced SQP method within the direct multiple shooting framework is proposed. This method is implemented in the code MSOPT which allows for an accelerated calculation of the directional derivatives and thereby saves computation time. Furthermore, this method is adapted to the real-time iteration scheme, which leads to a tremendous reduction of the time needed to provide new controls. The new NMPC variant is compared with a previously introduced scheme based on the optimization code MUSCOD-II, where the extended reduction is not in use. Numerical results are presented for both NMPC schemes and a decentralized PI controller in closed loop with a simulation model of a highly nonlinear thermally coupled distillation column which separates a ternary mixture. (c) 2007 Published by Elsevier B.V.

关键词： nonlinear model predictive control real-time optimization direct multiple shooting coupled distillation column ternary mixture

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Neural network identification in nonlinear model predictive control for frequent and infrequent operating points using nonlinearity measure

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ISA TRANSACTIONS 2020年第0期97卷 216-229页

作者： Saki, Saman Fatehi, Alireza KN Toosi Univ Technol Ind Control Ctr Excellence APAC Res Grp Tehran Iran

In this paper, the problem of optimal system identification in nonlinear model predictive control (NMPC) for highly nonlinear dynamic processes is presented. Due to the short term changes in the operating point, the process may escape from its frequent operating points (FOP) to some infrequent operating points (IOP) for a short period. On the other hand, because the nonlinear model is identified using the operating data, it is mainly accurate for the FOP. Therefore, the NMPC causes tracking error or even instability in the IOP. To handle this problem, in this paper, we present a novel optimal identification algorithm, which is highly depended on the nonlinearity of the understudy plant, to train the nonlinear model of the NMPC. The nonlinear model is selected as a multi-layer perceptron neural network (MLP) which is trained to describe the nonlinear behaviour of the nonlinear dynamic system accurately in the FOP and while it has acceptable performance in the IOP. To validate the proposed algorithm, the well-known nonlinear dynamic pH neutralization process is chosen in both simulation and implementation parts. Finally, the simulation and implementation results prove the effectiveness of the proposed algorithm. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： nonlinear identification nonlinear model predictive control nonlinearity measure Volterra series Advanced process control

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Autonomous Autorotation of Unmanned Rotorcraft using nonlinear model predictive control

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2010年第1-4期57卷 351-369页

作者： Dalamagkidis, Konstantinos Valavanis, Kimon P. Piegl, Les A. Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA Univ Denver Dept Elect & Comp Engn Denver CO 80208 USA

Safe operations of unmanned rotorcraft hinge on successfully accommodating failures during flight, either via control reconfiguration or by terminating flight early in a controlled manner. This paper focuses on autorotation, a common maneuver used to bring helicopters safely to the ground even in the case of loss of power to the main rotor. A novel nonlinear model predictive controller augmented with a recurrent neural network is presented that is capable of performing an autonomous autorotation. Main advantages of the proposed approach are on-line, real-time trajectory optimization and reduced hardware requirements.

关键词： Autonomous autorotation Unmanned rotorcraft nonlinear model predictive control

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Industrial application of a nonlinear model predictive control to polymerization reactors

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control ENGINEERING PRACTICE 2001年第8期9卷 819-828页

作者： Seki, H Ogawa, M Ooyama, S Akamatsu, K Ohshima, M Yang, W Mitsubishi Chem Corp Yokohama Res Ctr Aoba Ku Yokohama Kanagawa 2278502 Japan Mitsubishi Chem Corp Mizushima Plant Kurashiki Okayama 7128054 Japan Kyoto Univ Dept Chem Engn Sakyo Ku Kyoto 6068501 Japan

A nonlinear model predictive control has been developed and applied to an industrial polypropylene semi-batch reactor and a high density polyethylene continuous stirred tank reactor. The control system consists of two-tier algorithms: at the first tier, an LQI is formulated based on a successively linearized nonlinear first principles process model. At the second tier, actual control actions are determined in consideration of process constraints by solving a QP problem, which is formulated by linearizing the nonlinear model around the LQI trajectory. A simple state estimator, which is capable of providing offset-free estimates of the outputs at steady states, has been designed for each application. In the semi-batch reactor process, the controller was able to maximize the monomer feed while satisfying the heat removal constraint. In the high density polyethylene process, the performance of the grade transition control was greatly improved. (C) 2001 Elsevier Science Ltd. All rights reserved.

关键词： industrial control applications nonlinear model predictive control polymerization reactor control batch reactor control

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Enhanced Active Safety Through Integrated Autonomous Drifting and Direct Yaw Moment control via nonlinear model predictive control

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第2期9卷 4172-4190页

作者： Stano, Pietro Tavernini, Davide Montanaro, Umberto Tufo, Manuela Fiengo, Giovanni Novella, Luigi Sorniotti, Aldo Univ Surrey Ctr Automot Engn Guildford GU2 7XH England Univ Sannio Dept Engn I-82100 Benevento Italy Kineton Srl Kineton R&D I-80146 Naples Italy Politecn Torino Dept Mech & Aerosp Engn I-10129 Turin Italy

The introduction of active safety systems and advanced driver assistance systems has enhanced the control authority over the vehicle dynamics through specialized actuators, enabling, for instance, independent wheel torque control. During emergency situations, these systems step in to aid the driver by limiting vehicle response to a stable and controllable range of low longitudinal tire slips and slip angles. This approach makes vehicle behavior predictable and manageable for the average human driver;however, it is conservative in case of driving automation. In fact, past research has shown that exceeding the operational boundaries of conventional active safety systems enables trajectories that are otherwise unattainable. This paper presents a nonlinear model predictive controller (NMPC) for path tracking (PT), which integrates steering, front-to-total longitudinal tire force distribution, and direct yaw moment actuation, and can operate beyond the limit of handling, e.g., to induce drift, if this is beneficial to PT. Simulation results of emergency conditions in an intersection scenario highlight that the proposed solution provides significant safety improvements, when compared to the concurrent operation of PT algorithms and the current generation of vehicle stability controllers.

关键词： Force Wheels Safety Tires Torque Vehicle dynamics Roads Automated vehicles autonomous drifting active safety control allocation nonlinear model predictive control

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nonlinear model predictive control based on K-step control invariant sets

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EUROPEAN JOURNAL OF control 2024年 80卷

作者： Zhao, Zhixin Girard, Antoine Olaru, Sorin Univ Paris Saclay CNRS CentraleSupelec Lab Signaux & Syst 3 Rue Joliot Curie F-91190 Gif Sur Yvette France

One of the fundamental issues in nonlinear model predictive control (NMPC) is to be able to guarantee the recursive feasibility of the underlying receding horizon optimization. In other terms, the primary condition for a safe NMPC design is to ensure that the closed-loop solution remains indefinitely within the feasible set of the optimization problem. This issue can be addressed by introducing a terminal constraint described in terms of a control invariant set. However, the control invariant sets of nonlinear systems are often impractical to use or even to construct due to their complexity. The K-step control invariant sets are representing generalizations of the classical one-step control invariant sets and prove to retain the useful properties for MPC design, but often with simpler representations, and thus greater applicability. In this paper, a novel NMPC scheme based on K-step control invariant sets is proposed. We employ symbolic control techniques to compute a K-step control invariant set and build the NMPC framework by integrating this set as a terminal constraint, thereby ensuring recursive feasibility.

关键词： nonlinear model predictive control Symbolic control Safety

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nonlinear model predictive control for trajectory-planning and tracking based on tilting technology to achieve vehicle obstacle avoidance

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VEHICLE SYSTEM DYNAMICS 2024年第12期62卷 3276-3296页

作者： Sun, Jiajia Yao, Jialing Jia, Yunyi Yao, Feifan Shi, Wenku Nanjing Forestry Univ Coll Automobile & Traff Engn Nanjing Peoples R China Clemson Univ Dept Automot Engn Greenville SC USA IBM Global Serv Dalian Co Ltd Shanghai Branch Shanghai Peoples R China Jilin Univ State Key Lab Automot Simulat & Control Changchun Peoples R China

High-speed autonomous vehicles can effectively improve the performance of obstacle-avoidance trajectory-planning and tracking control through integration with existing electronic control systems. However, relatively little research has been conducted in this field. This paper proposes a novel methodology of obstacle-avoidance trajectory-planning and tracking control based on active front-wheel steering integrated with tilting technology that can tilt the vehicle body toward the inside of the curve via active suspension when turning. The controller is designed using hierarchical control, in which the upper layer uses the point-mass vehicle model to design the trajectory-planning algorithm based on model predictive control. The lower layer uses the nonlinear vehicle model to design obstacle-avoidance tracking nonlinear model predictive control based on active steering integrated with tilt control. Then, the constrained nonlinear model predictive control problem is transformed into a constrained nonlinear programming problem, which is solved by sequential quadratic programming. Finally, simulations were performed using the CarSim/Simulink co-simulation platform. Two other hierarchical obstacle-avoidance tracking nonlinear model predictive controllers were designed as comparison objects. The simulation results show that the planning trajectory of the proposed integrated controller is closest to the obstacle. This controller effectively improves the vehicle obstacle-avoidance trajectory-tracking performance, handling stability, and maneuverability.

关键词： Autonomous vehicles nonlinear model predictive control trajectory-planning tilting technology CarSim/Simulink co-simulation

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nonlinear model predictive control of an Autonomous Power System Based on Hydrocarbon Reforming and High Temperature Fuel Cell

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ENERGIES 2021年第5期14卷 1371页

作者： Kafetzis, Alexandros Ziogou, Chrysovalantou Papadopoulou, Simira Voutetakis, Spyridon Seferlis, Panos Ctr Res & Technol Hellas Chem Proc & Energy Resources Inst Thessaloniki 57001 Greece Aristotle Univ Thessaloniki Dept Mech Engn Thessaloniki 54124 Greece Int Hellen Univ Dept Ind Engn & Management Thessaloniki 57001 Greece

The integration and control of energy systems for power generation consists of multiple heterogeneous subsystems, such as chemical, electrochemical, and thermal, and contains challenges that arise from the multi-way interactions due to complex dynamic responses among the involved subsystems. The main motivation of this work is to design the control system for an autonomous automated and sustainable system that meets a certain power demand profile. A systematic methodology for the integration and control of a hybrid system that converts liquefied petroleum gas (LPG) to hydrogen, which is subsequently used to generate electrical power in a high-temperature fuel cell that charges a Li-Ion battery unit, is presented. An advanced nonlinear model predictive control (NMPC) framework is implemented to achieve this goal. The operational objective is the satisfaction of power demand while maintaining operation within a safe region and ensuring thermal and chemical balance. The proposed NMPC framework based on experimentally validated models is evaluated through simulation for realistic operation scenarios that involve static and dynamic variations of the power load.

关键词： high temperature polymer electrolyte membrane fuel cell power system LPG reforming nonlinear model predictive control

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A graph search and neural network approach to adaptive nonlinear model predictive control

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年第0期55卷 250-268页

作者： Reese, Brandon M. Collins, Emmanuel G., Jr. Florida State Univ Tallahassee Florida A&M Univ Dept Mech Engn Tallahassee FL 32310 USA Florida State Univ Tallahassee Florida A&M Univ Ctr Intelligent Syst Control & Robot Dept Mech Engn Tallahassee FL 32310 USA

Systems with a priori unknown and time-varying dynamic behavior pose a significant challenge in the field of nonlinear model predictive control (NMPC). When both the identification of the nonlinear system and the optimization of control inputs are done robustly and efficiently, NMPC may be applied to control such systems. This paper considers stable systems and presents a novel method for adaptive NMPC, called Adaptive Sampling Based model predictive control (Adaptive SBMPC), that combines a radial basis function neural network identification algorithm with a nonlinear optimization method based on graph search. Unlike other NMPC methods, it does not rely on linearizing the system or gradient based optimization. Instead, it discretizes the input space to the model via pseudo-random sampling and feeds the sampled inputs through the nonlinear model, producing a searchable graph. For this discretization, an optimal path is found using Lifelong Planning A*, an efficient graph search method. Adaptive SBMPC is used in simulation to identify and control a simple plant with clearly visualized nonlinear behavior. In these simulations, both fixed and time-varying dynamic systems are considered. Results are compared with an adaptive version of Neural GPC, an existing NMPC algorithm based on Newton-Raphson optimization and a back propagation neural network model. When the cost function exhibits many local minima, Adaptive SBMPC is successful in finding a low-cost solution that appears close globally optimal while Neural GPC converges to a solution that is only locally optimal. This paper presents the method, soundness and completeness theory, and two simulated NMPC examples. The first is a transparent single-input single-output example, and the second considers a more complex power plant combustion process with two inputs and three outputs. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： nonlinear model predictive control Adaptive control Neural network Graph search nonlinear optimization

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