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8th Annual IEEE Systems Conference (SysCon)

作者： Hafez, A. T. Iskandarani, M. Givigi, S. N. Yousefi, S. Beaulieu, A. Queens Univ Dept Elect & Comp Engn Kingston ON Canada Royal Mil Coll Canada Kingston ON Canada

ISBN: (纸本)9781479920860

A combination of decentralized linear model predictive control (LMPC) and Feedback linearization (FL) is implemented on a team of quadrotor aircraft in order to accomplish dynamic encirclement around a stationary target in real-time. Dynamic encirclement is defined as the situation in which a target is isolated and surrounded by an Unmanned-Aerial-Vehicle (UAV) team in order to maintain awareness and containment of it. In this paper, the problem of maintaining a desired radius of encirclement, individual angular velocities and angular separation between team members is considered. The nonlinear plant, representing each vehicle's autonomous behaviour, is obtained through system identification and then linearized using an FL technique. Due to complex flight variables and multi-vehicle cooperation, LMPC is the most suited controller for solving the problem of dynamic encirclement on real-world platforms. The contribution of this paper lay in the application of decentralized LMPC and FL to the problem of encirclement using an autonomous team of Qball-X4 quadrotors in real-time.

关键词： linear model predictive control Unmanned Aerial Vehicles Feedback linearization Autonomous robotics Cooperative robotics

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model predictive control for Tilt Recovery of an Omnidirectional Wheeled Humanoid Robot

Model Predictive Control for Tilt Recovery of an Omnidirecti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lafaye, Jury Collette, Cyrille Wieber, Pierre-Brice Aldebaran Robot Paris France Inria Grenoble Rhone Alpes Grenoble France

ISBN: (纸本)9781479969234

The goal of this paper is to present a real-time controller for an omnidirectional wheeled humanoid robot which can be strongly disturbed and tilt around its wheels. It is based on two linear model predictive controllers, managed by a tilt supervisor, which detects changes of the dynamic model caused by the tilt of the robot. Experimental results are proposed on the robot Pepper made by Aldebaran, showing good performance in term of stability an robustness.

关键词： Humanoid Robotics Mobile Robotics Omnidirectional Wheeled Robot Tilt Recovery Push Recovery linear model predictive control Quadratic Programming Aldebaran Pepper Robot

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Robust Implementation of the model predictive control for DC-DC Power Converters and Industrial control Applications

Robust Implementation of the Model Predictive Control for DC...

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作者： Albira, Mohamed Emhemed Oakland University

学位级别：Ph.D., Doctor of Philosophy

This dissertation addresses the development of the robustness and strength of the model predictive control algorithm subjected to input constraints for a plant system with and without parameters' uncertainties. In the beginning, the MPC control system was implemented for systems with no types of parameters' uncertainties. The proposed system models were stable and linear and all of its parameters were fully known. They were formulated in model state-space system format. The main objective of this control system design was to maintain a smooth and constant output signal that could easily track the assigned desired output signal. The technical process of this control design was to calculate the optimal solutions for the proposed plant system by optimizing the Quadratic programming problem (QP) subjected to linear inequality constraints. Therefore, the proposed control system successfully forced the outputs of the proposed systems to track the output reference signals in a fast response and with very small steady-state errors, even with the change in prediction horizon values. The second control system approach was for a system model which assumed its parameters’ uncertainties. In the other words, the parameters were not precisely known, but they were bounded in a minimum and maximum range. The parameters' uncertainties and the converter's switching behaviors made it act as a highly nonlinear system. Therefore, the Adaptive model predictive controller (AMPC) with the linear Parameter Varying (LPV) control algorithm was implemented to address these issues and to secure a sustainable output signal with on types of noise or degradations. In this algorithm, the LPV model was created out of a set of linear Time-Invariant (LTI) models, which are used to update the AMPC controller based on the feedback signals that come from the plant model and the change in the system parameters. Due to the changes in the plant model parameters over time, the AMPC was the perfect control app

关键词： Adaptive model predictive control Buck boost buck-boost converters control system design Dc-dc power electronics linear model predictive control linear parameter varying

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Optimization and control of extractive distillation for formic acid-water separation with maximum-boiling azeotrope

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COMPUTERS & CHEMICAL ENGINEERING 2023年 169卷

作者： Ge, Xiaolong Zhang, Ran Liu, Pengfei Liu, Botan Liu, Botong Tianjin Univ Sci & Technol Coll Chem Engn & Mat Sci Tianjin Key Lab Brine Chem Engn & Resource Ecoutil Tianjin 300457 Peoples R China Tsinghua Univ Dept Chem Engn 30 Shuangqing Rd Beijing 100084 Peoples R China Tianjin Univ State Key Lab Chem Engn Tianjin 300350 Peoples R China CNPC East China Design Inst Co Ltd Qingdao 266071 Peoples R China

It is urgent to develop sustainable chemical process through process intensification, especially for the energy -intensive distillation process. Formic acid-water forms maximum azeotrope which is purified by pressure -swing distillation in Kemira-Leonard (K-L) process and extraction plus distillation in BASF route. However, either process for this maximum azeotrope separation is energy-intensive. In the present work, by selecting the effective heavy entrainer, extractive distillation and extractive dividing wall column was firstly synthesized and optimized with genetic algorithm for formic acid-water separation. Based on optimal design, the energy con-sumption and CO2 emission among these proposed and existent processes were compared, and extractive distillation shows its strength for achieving high efficiency and sustainability. Then multi-loop PI control scheme and linear model predictive control based on temperature and temperature difference were developed, respec-tively. Dynamic response validates the control performance superiority of advanced controllers, especially for controlled variables with large overshoot, oscillations and settling time.

关键词： Process intensification Sustainability Extractive distillation Process optimization linear model predictive control

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Exact Complexity Certification of an Early-Terminating Standard Primal Active-Set Method for Quadratic Programming

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IFAC-PapersOnLine 2020年第2期53卷 6509-6515页

作者： Daniel Arnström Daniel Axehill Division of Automatic Control Linköping University Sweden

In this paper we present a method to exactly certify the iteration complexity of a primal active-set algorithm for quadratic programs which is terminated early, given a specific multi-parametric quadratic program. The primal active-set algorithm’s real-time applicability is, hence, improved by early termination, increasing its computational efficiency, and by the proposed certification method, providing guarantees on worst-case behaviour. The certification method is illustrated on a multi-parametric quadratic program originating from model predictive control of an inverted pendulum, for which the relationship between allowed suboptimality and iterations needed by the primal active-set algorithm is presented.

关键词： Quadratic programming Active-set methods linear model predictive control Suboptimal control Complexity certification

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IMMUNE : control reallocation after surface failures using model predictive control

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IFAC Proceedings Volumes 2010年第15期43卷 446-451页

作者： L. Lafourcade Ch. Cumer C. Döll ONERA The French Aerospace Lab Systems Control and Flight Dynamics Department DCSD 2 av. Ed. Belin B.P. 4025 31055 Toulouse Cedex 4 France (Tel.: +33 (0)5.62.25.29.20 Fax: +33 (0)5.62.25.25.64) Institut Supérieur de l'Aéronautique et de l'Espace (ISAE) SUPAERO 10 av. Ed. Belin – B.P. 54032 – 31055 Toulouse Cedex 4 France

Abstract This paper describes how to exploit the linear model predictive control with quadratic programming to the well-known control surface reallocation problem once an actuator failure has been detected and isolated. Theoretical developments assume here an overactuated aircraft model, only submitted to input constraints. Implementation improvements are added to allow real on-board applications. Simulations prove the efficiency of the algorithm for various failure cases.

关键词： control reallocation linear model predictive control quadratic programming optimization.

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Numerical Methods for Large Scale Moving Horizon Estimation and control

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IFAC Proceedings Volumes 2004年第9期37卷 895-900页

作者： John Bagterp Jørgensen James B. Rawlings Sten Bay Jørgensen 2-control ApS Høffdingsvej 34 DK-2500 Valby Denmark Depanment of Chemical and Biological Engineering University of Wisconsin-Madison Madison W1 53706 Wisconsin USA CAPEC Depanment of Chemical Engineering Technical University of Denmark DK-2800 Lyngby Denmark

Both the linear moving horizon estimator and controller may be solved by solving a linear quadratic optimal control problem. A primal active set, a dual active set, and an interior point algorithm for solution of the linear quadratic optimal control problem are presented. The major computational effort in all these algorithms reduces to solution of certain unconstrained linear quadratic optimal control problems. A Riccati recursion procedure for efficient solution of such unconstrained problems is stated

关键词： linear model predictive control Quadratic Programming Numerical Methods

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Practice and Challenges in Chemical Process control Applications in Japan

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IFAC Proceedings Volumes 2008年第2期41卷 10608-10613页

作者： Morimasa Ogawa Manabu Kano Yamatake Corporation Kurashiki Okayama 712-8061 Japan (Tel: +81-86-448-2511 Kyoto University Nishikyo-ku Kyoto 615-8510 JAPAN (Tel: +81-75-383-2687

This paper surveys how the three central pillars of process control – PID control, conventional advanced control, and model predictive control – have been used and how they have contributed to production activity from the viewpoint of the process control section in the Japanese chemical industry. In addition to introducing practical methods and their application results, the authors point out challenging problems, which include the development of a general model-based control technique to enhance batch process control.

关键词： Process control Industrial application PID control Conventional advanced control linear model predictive control Non-linear model predictive control

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Soft Constraints for Robust MPC of Uncertain Systems

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IFAC Proceedings Volumes 2009年第11期42卷 225-230页

作者： Guru Prasath John Bagterp Jørgensen FLSmidth Private Limited. Automation Division Chennai India Informatics and Mathematical Modelling Technical University of Denmark DK-2800 Kgs. Lyngby Denmark

Abstract In this paper we develop a robust constrained predictive controller for linear systems. The controller is equipped with soft output constraints that are used in a novel way to have robustness against model plant mismatch. By simulation we compare the performance of the new robust constrained predictive controller to a nominal predictive controller. In the nominal case, the performance of the robust predictive controller is comparable to the performance of the nominal predictive controller. In the case of plant model mismatch, the robust predictive controller performs significantly better than the nominal predictive controller.

关键词： linear model predictive control Robust predictive control Soft Constraints

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Task Decomposition for MPC: A Computationally Efficient Approach for linear Time-Varying Systems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 4240-4245页

作者： Charlott Vallon Francesco Borrelli University of California at Berkeley Berkeley CA 94701 USA

A Task Decomposition method for iterative learning model predictive control (TDMPC) for linear time-varying systems is presented. We consider the availability of state-input trajectories which solve an original task τ 1, and design a feasible MPC policy for a new task, τ 2, using stored data from τ 1. Our approach applies to tasks τ2 which are composed of subtasks contained in τ 1. In this paper we formally define the task decomposition problem, and provide a feasibility proof for the resulting policy. The proposed algorithm reduces the computational burden for linear time-varying systems with piecewise convex constraints. Simulation results demonstrate the improved efficiency of the proposed method on a robotic path-planning task.

关键词： Data-based control Iterative Repetitive Learning control linear model predictive control Convex Optimization

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