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Shaping the MPC Cost Function for Superior Automated Glucose Control

IFAC-PapersOnLine 2016年第7期49卷 779-784页

作者： Lee, Joon Bok Gondhalekar, Ravi Dassau, Eyal Doyle, Francis J. Department of Chemical Engineering University of California Santa BarbaraCA United States John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA United States

The properties of an objective function are fundamental to the functionality of a model predictive controller (MPC). In automated glucose control, avoidance of hypo- and hyperglycemia introduces significant challenges in the design of this cost function. We present a new formulation of the cost function for an MPC based on clinical requirements, and validate the algorithm under in silico and advisory mode assessments. The proposed formulation exhibits significant improvements in avoiding hypoglycemia, compared to clinically validated controllers, and can also mitigate hyperglycemia, across a wide range of in silico scenarios as well as glucose responses seen in actual clinical settings. © 2016

关键词： Cost functions Controllers Costs Glucose Medical applications Model predictive control Predictive control systems Biomedical control Clinical settings constraints Glucose control Glucose response Model Adaptation Model predictive controllers objective functions

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Fault-Tolerant Low-Thrust Trajectory Design with Backups for Multiple Targets

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IFAC-PapersOnLine 2016年第17期49卷 391-396页

作者： Wachi, Akifumi Ozakiz, Naoya Nakasuka, Shinichi The University of Tokyo Japan

This paper presents a method to design robust trajectories against the possible faults of low-thrust engines. Recently, increasing number of space probes with low-thrust engines have been developed. Due to the insufficient reliability of low-thrust propulsion system, almost all probes with ion engines have experienced the failure of engines. Conventional methods to design the low-thrust trajectory have pursued fuel minimum solution to only one target celestial body which does not necessarily mean the maximization of mission achievement. In other words, it can enhance the mission accomplishment to intelligently change the trajectory when engine failures occur. In this research, the objective function is defined as expected scientific gain, and the optimal solution is searched by a proposed method. Resulting method, Bellman Rapidly-exploring Random Trees (Bellman RRT) are efficiently extended by applying the Bellman equation. Finally, the numerical simulation demonstrated that the Bellman RRT improved the mission achievement using real deep space exploration scenario. © 2016

关键词： Design Dynamic programming Fault tolerance Interplanetary flight Ion engines Probes Propulsion Space research Trajectories Backup Conventional methods Deep space exploration Low thrust propulsion Low thrust trajectory objective functions Rapidly exploring random trees Trajectory Planning

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Balancing Push and Pull Strategies within the Production System

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IFAC-PapersOnLine 2016年第2期49卷 66-71页

作者： Puchkova, Alena Le Romancer, Julien McFarlane, Duncan Institute for Manufacturing CambridgeCB3 0FS United Kingdom

This paper presents an approach for achieving a trade-off between pull and push production control principles. A mathematical model of the operation of a production system is suggested, which incorporates different options for inventory control strategies. The model covers several types of disruptions: resource breakdown, product quality loss and demand fluctuation. The optimal solution minimizes the total cost objective function and can be push, one of the classic pull control systems (kanbans, conwip, basestock) or any possible combination/superimposition of them. The approach is illustrated in an example, for which optimal pull/push combination is found and its evolution is shown under several scenarios with disruptions. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Inventory control Control systems Economic and social effects Models Optimization Production control Control strategies Demand fluctuations disruptions objective functions Production network Production system pull push

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Flexible Flow Shop with Storage: Complexity and Optimisation Methods

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IFAC-PapersOnLine 2016年第12期49卷 237-242页

作者： Fung, Joey Zinder, Yakov Singh, Gaurav School of Mathematical and Physical Sciences University of Technology Sydney Australia Technology Geoscience and Engineering BHP Billiton Australia

The paper is concerned with a two-stage flexible flow shop with a buffer under the assumption that all operations have the same processing time and that each job can be processed on a machine only if a certain amount of buffer space is allocated to this job. The amount of buffer space to be allocated differs from job to job. The buffer is shared by all machines and has limited size. The objective function is the makespan. The application area includes supply chains which involve loading and unloading operations, and problems arising in computer systems with shared memory. It is proven that in general, the problem is NP-hard in the strong sense, but in the case of two-machines, it is solvable in polynomial-time. For the general case, several integer linear programming models are presented and compared by means of computational experiments. The presented integer linear programming models implement various ideas such as symmetry breaking constraints, knapsack type cutting planes and the results of the analysis of the structure of optimal schedules. © 2016

关键词： Integer programming Job shop scheduling Machine shop practice Operations research Polynomial approximation Scheduling Structural optimization Supply chains Unloading Application area Computational experiment Flexible flow shop Integer linear programming models Loading and unloading operations objective functions Optimisation method Symmetry breaking constraints

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Parallelizing Knowledge Mining in a Cognitive Agent for Autonomous Decision Making

Parallelizing Knowledge Mining in a Cognitive Agent for Auto...

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Computing Conference

作者： Tanvir Atahary Tarek Taha Scott Douglass Electrical and Computer Engineering University of Dayton Dayton OH 45469 USA Human Effective Directories Air Force Research Laboratory Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9781509054442

Cognitive computing can enable automatic decision making, thus enabling the design of more intelligent and autonomous systems. An autonomous cognitive decision support architecture, Cognitively Enhanced Complex Event Processing (CECEP), was developed at United States Air Force Research Laboratory to enable agent-based decision-making. CECEP has several components, which allow cognitive agents to make automatic decision without any human interaction. One of the key components, Cognitive Domain Ontologies (CDO) are a knowledge repository for CECEP agents. The knowledge mining process from CDOs in the CECEP architecture is analogous to solving a constraint-satisfaction problem (CSP). Cognitive agents are more capable when they are able to process or mine larger volume of information to make more informed and intelligent decisions. This means that the CDOs need to process large knowledge bases. Cognitive agents need to consider environmental inputs and make decisions at runtime. Hence CDOs with large knowledge bases need to be accelerated for real time cognitive agents. This study introduces an accelerated CDO solver with multiple optimizations for generating and ranking solutions for an autonomous cognitive agent. As CDOs typically generate all equally weighted valid solutions, it is impossible to find the best one from this solution set without imposing some relative importance of certain criteria. This study implemented several optimization conditions through objective functions and processed them as Multiple Constraint Optimization Problems (MCOPs) to rank solutions on a GPGPU. It achieved a speedup of 85-100 times over existing solvers for ranking the solutions.

关键词： Cognitive Agent Knowledge Mining Multiple Constraint Optimization objective functions

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Coordinated Non-Cooperative Distributed Model Predictive Control for Decoupled Systems Using Graphs

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IFAC-PapersOnLine 2016年第22期49卷 216-221页

作者： Alrifaee, Bassam Heßeler, Frank-Josef Abel, Dirk Institute of Automatic Control Department of Mechanical Engineering RWTH Aachen University Aachen52074 Germany

This paper proposes a novel strategy for Non-Cooperative Distributed Model Predictive Control (Non-Coop. DMPC) of Networked Control Systems (NCS) consisting of dynamically decoupled agents where the coupling is achieved in the objective function or in the constraints. Moreover, the coupling is considered to be time-variant. We call this strategy Priority-Based Non-Coop. DMPC (PB-Non-Coop. DMPC). It is based on assigning priorities to the agents and the use of the coupling graph. Each PB-Non-Coop. DMPC is associated with a different agent and computes the local control inputs based only on its states and that of its neighbors. PB-Non-Coop. DMPC satisfies the prediction consistency property and reduces the overall computation time in comparison with existing Non-Coop. DMPC strategies in literature, thereby improves the overall behavior of NCS. We compare PB-Non-Coop. DMPC with centralized MPC as well as with another Non-Coop. DMPC strategy from literature. © 2016

关键词： Model predictive control Networked control systems Predictive control systems Computation time Consistency property Decoupled system Distributed Model predictive Control Novel strategies objective functions Priority Time variant

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Two-Station Single Track Scheduling Problem

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IFAC-PapersOnLine 2016年第12期49卷 231-236页

作者： Zinder, Yakov Lazarev, Alexander A. Musatova, Elena G. Tarasov, Ilia A. Khusnullin, Nail F. University of Technology Sydney Australia International Laboratory of Decision Choice and Analysis National Research University Higher School of Economics Lomonosov Moscow State University Institute of Physics and Technology State University Institute of control sciences Moscow United States Institute of control sciences Moscow United States Lomonosov Moscow State University Institute of control sciences Moscow United States

Single track segments are common in various railway networks, in particular in various supply chains. For such a segment, connecting two stations, the trains form two groups, depending on what station is the initial station for the journey between these two stations. Within a group the trains differ by their cost functions. It is assumed that the single track is sufficiently long so several trains can travel in the same direction simultaneously. The paper presents polynomial-time algorithms for different versions of this two-station train scheduling problem with a single railway track. The considered models differ from each other by their objective functions. © 2016

关键词： Scheduling Cost functions Operations research Polynomial approximation Railroads Supply chains objective functions Polynomial time algorithms Railway network Railway track Scheduling problem Single tracks Train scheduling

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Dual Control and Information Gain in Controlling Uncertain Processes

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IFAC-PapersOnLine 2016年第7期49卷 139-144页

作者： La, H.C. Potschka, A. Schlöder, J.P. Bock, H.G. INF 368 Im Neuenheimer Feld Heidelberg69120 Germany

In controlling uncertain processes, it is decisive to utilize information provided by measurements in order to estimate parameters and states. Nonlinear Model Predictive Control (NMPC) is a popular method to implement feedback control and deal with uncertainties. Conventional NMPC or nominal control, however, sometimes does not provide enough information for system estimation, leading to unsatisfactory performance. Dual control attempts to strike a balance between the two goals of enhancing system estimation and optimizing the nominal objective function. In this paper, we analyze the performance of these strategies through the interplay between the performance control task and the information gain task in connection with Optimal Experimental Design. Examples illustrate the conflict and agreement between the two tasks and explain why in some cases nominal control performs well. It is also observed that measurement noise provides excitation helping to improve the quality of estimates. © 2016

关键词： Parameter estimation Model predictive control Nonlinear systems Predictive control systems State estimation Uncertainty analysis Dual control Measurement Noise Nonlinear model predictive control objective functions Optimal experimental designs Parameter Performance control Uncertain process

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Modeling and optimization of biomass supply chains: A review and a critical look

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IFAC-PapersOnLine 2016年第12期49卷 604-615页

作者： Atashbar, N. Zandi Labadie, N. Prins, C. CS 42060 Troyes10004 France

Due to the effect of greenhouse gas emissions and increasing energy demand, consumption of more sustainable and renewable energies has increased. Biofuels derived from biomass can play a key-role nowadays as one of the main sources of renewable energies. Therefore, more and more researchers have been involved in modeling and optimizing biomass supply chains. This paper presents an overview of biomass supply chains. After an outline of the main definitions and a description of typical activities in biomass supply chain; a classification of optimization methods and models developed in this context is detailed. All selected papers are classified and discussed according to (1) objective functions, (2) decision levels and (3) solution methods. Finally, a critical look at current researches and possible new directions are presented. © 2016

关键词： Biomass Biofuels Gas emissions Greenhouse effect Greenhouse gases Logistics Models Optimization Supply chains Biomass supply chain Decision levels Energy demands Modeling and optimization objective functions Optimization method Renewable energies Solution methods

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The point to point trajectory planning based on the ant lion optimiser

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2016年第2期10卷 155-166页

作者： Guo, Jinchao Yan, Desheng Cao, Hong Jiang, Zhengke Zhengzhou Univ Light Ind Dept Elect & Informat Engn Zhengzhou Henan Province Peoples R China Henan Senyuan Elect Cooperat Weiwu Rd Changge City 461500 Henan Province Peoples R China

This paper proposes a new method to optimise trajectory for the robot arm. Quadrinomial and quintic polynomials are utilised in joint space to describe point to point trajectory. The ant lion optimiser (ALO) is introduced to ensure searching the global optimal. Dynamic weight is adopted to improve the search ability of the new algorithm. Considering a maximum pre-defined joint torque of the robot manipulator and the ability to avoid obstacles in the work space, the objective function is given for ALO to minimising motion space and time. The simulation is accomplished for different situation with or without obstacle in the workspace to verify the validity of the new algorithm. The different simulation results generated by ALO, GA and QPSO are given to inspect ALOs accuracy. The results show ALO has extremely strong ability to find the global optimal and even less time consumed compared with GA and QPSO.

关键词： ALO algorithm trajectory planning point-to-point trajectory objective functions

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