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Mediterranean Conference on control and Automation (MED)

作者： Fatos Gashi Khalil Abuibaid Martin Ruskowski Achim Wagner German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems RPTU Kaiserslautern-Landau Kaiserslautern Germany

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

We introduce an alternative approach towards optimal proportional integral derivative (PID) control, consisting of model predictive control (MPC) based reference generation. To this end, we have integrated the reference as part of optimization variables of the resulting problem, where a deliberate sequence of errors is induced to obtain an optimal PID control action. In addition, the desired behavior of the PID controller is achieved without the need for internal modification of the PID gains. To better highlight the ability of coping with poor PID tuning, several test cases consisting of progressively degraded PID gains are presented. Validation of the proposed strategy is displayed by comprehensive simulations using two different plants.

关键词： PI control Automation PD control Tuning Optimization Predictive control

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Non-intrusive Time-POD for Optimal control of a Fixed-Bed Reactor for CO 2 Methanation

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IFAC-PapersOnLine 2021年第3期54卷 122-127页

作者： Jens Bremer Jan Heiland Peter Benner Kai Sundmacher Max Planck Institute Magdeburg Dpt. Process Systems Engineering Sandtorstraße 2 39106 Magdeburg Germany and Otto von Guericke University Magdeburg Chair for Process Systems Engineering Universitätsplatz 2 39106 Magdeburg (Sundmacher) Max Planck Institute Magdeburg Dpt. Computational Methods in Systems and Control Theory and Otto von Guericke University Magdeburg Fakultät für Mathematik

The optimization of a controlled process in a simulation without access to the model itself is a common scenario and very relevant to many chemical engineering applications. A general approach is to apply a black-box optimization algorithm to a parameterized control scheme. The success then depends on the quality of the parametrization that should be low-dimensional though rich enough to express the salient features. This work proposes using solution snapshots to extract dominant modes of the temporal dynamics of a process and use them for low-dimensional parametrizations of control functions. The approach is called proper orthogonal decomposition in time (time-POD). We provide theoretical reasoning and illustrate the performance for the optimal control of a methanation reactor.

关键词： Optimal control Model order reduction Proper orthogonal decomposition Fixed-bed reactor Carbon dioxide methanation Nonlinear programming Black-box model

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From stirring to mixing: artificial intelligence in the process industry

From stirring to mixing: artificial intelligence in the proc...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Valentin Khaydarov Sebastian Heinze Markus Graube Andreas Knüpfer Maximilian Knespel Silke Merkelbach Leon Urbas Process-To-Order Lab Technische Universität Dresden Dresden Germany Chair Process Control Systems/Process System Engineering Technische Universität Dresden Dresden Germany Center for Information Services and HPC (ZIH) Technische Universität Dresden Dresden Germany

ISBN: (数字)9781728189567

ISBN: (纸本)9781728189574

The introduction of AI methods in production or production-related environments meets with resistance from operators due to their lack of relevant experience and their responsibility for plant safety. To overcome these inhibitions one requires prototypical implementations, which offer considerable benefits, meet the highest requirements for reliability, are accepted by the operating personnel, and get support from those in charge. As a result, AI technologies must be embedded into the complex IT/OT infrastructure of the companies. Traceability, maintainability and longevity must also be guaranteed. As a first step towards this, we present a concept of the demonstrator and its first results, which should make AI comprehensible by visualizing challenges and exploring possibilities in the process industry.

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Hierarchical Distributed Model Predictive control based on Dual Decomposition and Quadratic Approximation

Hierarchical Distributed Model Predictive Control based on D...

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Mediterranean Conference on control and Automation (MED)

作者： Vassilios Yfantis Nigora Gafur Achim Wagner Martin Ruskowski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a dual decomposition-based distributed optimization algorithm and applies it to distributed model predictive control (DMPC) problems. The considered DMPC problems are coupled through shared limited resources. Lagrangian duality can be used to decompose an MPC problem, so that each subsystem can compute its individual resource utilization, without sharing information, such as dynamics or constraints, with the other subsystems. The feasibility of the central problem is ensured by the coordination of the subproblems through dual variables which can be interpreted as prices on the shared limited resources. The proposed coordination algorithm makes efficient use of information collected from previous iterations by performing a quadratic approximation of the dual function of the central MPC problem. Aggressive update steps of the dual variables are prevented through a covariance-based step size constraint. The nonsmoothness encountered in dual optimization problems is addressed by the construction of cutting planes, similar to bundle methods for nonsmooth optimization. The cutting planes ensure that the updated dual variables do not lie outside the range of validity of the dual approximation. The proposed algorithm is evaluated on a two-tank system and compared to the standard subgradient method. The results show that the rate of convergence towards the centralized solution can be significantly improved while still preserving privacy between the subsystems through limited information exchange.

关键词： Privacy Heuristic algorithms Prediction algorithms Approximation algorithms Dynamic scheduling Resource management Optimization

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A Hierarchical Dual Decomposition-based Distributed Optimization Algorithm combining Quasi-Newton Steps and Bundle Methods

A Hierarchical Dual Decomposition-based Distributed Optimiza...

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Mediterranean Conference on control and Automation (MED)

作者： Vassilios Yfantis Martin Ruskowski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a hierarchical distributed optimization algorithm based on quasi-Newton update steps. Separable convex optimization problems are decoupled through dual decomposition and solved in a distributed fashion by coordinating the solutions of the subproblems through dual variables. The proposed algorithm updates the dual variables by approximating the Hessian of the dual function through collected subgradient information, analogously to quasi-Newton methods. As the dual maximization problem is generally nonsmooth, a smooth approximation might show poor performance. To this end cutting planes, analogous to bundle methods, are constructed that take the nonsmoothness of the dual function into account and lead to a better convergence behavior near the optimum. The proposed algorithm is evaluated on a large set of benchmark problems and compared to the subgradient method and to the bundle trust method for nonsmooth optimization.

关键词： Automation Benchmark testing Approximation algorithms Convex functions Behavioral sciences Optimization Convergence

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Dynamic path planning and reactive scheduling for a robotic manipulator using nonlinear model predictive control

Dynamic path planning and reactive scheduling for a robotic ...

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Mediterranean Conference on control and Automation (MED)

作者： Nigora Gafur Leo Weber Vassilios Yfantis Achim Wagner Martin Ruskwoski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Operation of robotic manipulators is limited to structured environments and well-defined tasks due to an offline path planning. However, flexible production processes and human-robot collaboration necessitates a real time path planning to allow for replanning a path in changing environments. In this work, we investigate established planning algorithms for their applicability to dynamic path planning problems. We further compare these methods with our approach based on model predictive control. We consider a single manipulator with six degrees of freedom in static and dynamic environments. We investigate three experimental setups and show the advantages of the proposed MPC-ELS approach over more traditional path planning algorithms in terms of several metrics, such as path-length, execution time or trajectory smoothness. In addition, we propose a scheduling algorithm for object allocation to determine an optimal sequence for pick and place tasks with regard to minimum execution time.

关键词： Measurement Scheduling algorithms Heuristic algorithms Production Prediction algorithms Dynamic scheduling Trajectory

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Evolutionary Algorithm-based Optimal Parametrization of Multi-objective Mixed-integer Linear Programming Scheduling Models

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IFAC-PapersOnLine 2023年第2期56卷 5382-5387页

作者： Vassilios Yfantis Alexander Babskiy Bastian Dörig Thorsten Winterer Achim Wagner Martin Ruskowski Chair of Machine Tools and Control Systems Department of Mechanical and Process Engineering RPTU Kaiserslautern-Landau Kaiserslautern D-67663 Germany flexis AG Stuttgart D-70565 Germany Innovative Factory Systems German Research Center for Artificial Intelligence (DFKI) Kaiserslautern D-67663 Germany

Modern manufacturing systems strive to optimize many different key performance indicators. Additionally, mathematical programming-based scheduling models enable the explicit considerations of constraints. However, due to the ever-increasing demand for customized products it is not guaranteed that all constraints can be met at the same time. An approach to circumvent this problem of infeasibility is to replace the constraints by soft-constraints, i.e., to penalize their violation in the objective function. The presence of multiple competing objectives and soft-constraints gives rise to multi-objective optimization problems, where a decision maker has to weigh and balance the different objectives and soft-constraints according to his/her preferences and priorities. Ideally the values of all objectives and soft-constraints should lie within the same order of magnitude, making it easy to weigh them against each other. However, for heterogeneous objectives and soft-constraints with different scales and units this might be challenging. This paper presents an evolutionary algorithm for the optimal parametrization of multi-objective mixed-integer linear programming-based scheduling models. The goal of the evolutionary algorithm is to compute weights for the different terms of the objective which lead to a balanced influence on the overall objective at the optimum. Furthermore, the algorithm is extended by introducing an a priori weighing of the objectives in the fitness function of the evolutionary algorithm. The method is demonstrated on a scheduling problem in which a given set of orders has to be allocated to machines within a manufacturing environment.

关键词： Evolutionary Algorithm Multi-objective optimization Scheduling

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State-based control of process services within modular process plants

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Procedia CIRP 2018年 72卷 1088-1093页

作者： Henry Bloch Stephan Hensel Mario Hoernicke Katharina Stark Anna Menschner (neé Hahn) Alexander Fay Leon Urbas Torsten Knohl Jens Bernshausen Institute of Automation Technology Helmut-Schmidt-University Holstenhofweg 85 22043 Hamburg Germany Chair of Process Control Systems and Process Systems Engineering Group Technische Universität Dresden 01062 Dresden Germany ABB Corporate Research Opertions Management Wallstadter Straße 59 68526 Bayer AG 51386 Leverkusen Germany INVITE GmbH 51386 Leverkusen Germany

The modularization of process plants is an approach to cope with the changing requirements of the process industry, such as highly volatile markets and shorter product lifecycles. Each module offers services as encapsulated process functions to the superior control system for the orchestration. These services are controlled in a state-based way. The encapsulation of these services in a reasonable way is a difficult task, because aspects of process and automation engineering must be considered. This contribution introduces an approach for the encapsulation of process functions in services from an automation point of view. Furthermore, the operation modes of modules, services and field devices are discussed. Additionally, possible solutions for the orchestration based on recipe models are introduced. The developed concepts have been tested within a simulation environment.

关键词： Modular process automation Microservices Orchestration State-based control Operating modes Service-oriented architectures

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Optimal Load control and Scheduling through Distributed Mixed-integer Linear Programming

Optimal Load Control and Scheduling through Distributed Mixe...

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Mediterranean Conference on control and Automation (MED)

作者： Vassilios Yfantis William Motsch Nico Bach Achim Wagner Martin Ruskowski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems Technische Universit&#x00E4 t Kaiserslautern Kaiserslautern Germany Technologie-Initiative SmartFactory KL e.V. Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a mixed-integer linear programming-based optimization model for simultaneous optimal load control and scheduling of distributed systems coupled through their energy consumptions. The subsystems are able to adjust their energy consumption during the execution of a task and aim at minimizing their completion time and energy cost. The overall problem is solved in a distributed fashion, where each subsystem optimizes its individual operation without sharing sensitive information. To this end, dual decomposition is employed and a new algorithm to update the dual variables is presented. It relies on a transformation of the gradient of the quadratically approximated dual function and the subsequent solution of a regression problem. The proposed algorithm makes efficient use of information collected in previous iterations. The solution obtained from the distributed optimization of the subsystems is compared to both a decentral and a system-wide solution, showing that the distributed solution lies close to the global optimum of the process.

关键词： Energy consumption Costs Automation Approximation algorithms Mixed integer linear programming Task analysis Optimization

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Flexibility vs. security in linked enterprise data access control graphs

Flexibility vs. security in linked enterprise data access co...

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International Symposium on Information Assurance and Security (IAS)

作者： Markus Graube Patricia Ortiz Manuel Carnerero Oscar Lázaro Mikel Uriarte Leon Urbas Chair for Process Control Systems Engineering Technische Universität Dresden Germany R&D Innovalia Association S.A Bilbao Spain R&D Nextel S.A Bilbao Spain Technische Universitat Dresden Dresden Sachsen DE

Linked Data offers easy extensibility and interoperability of information spaces. This provides a great potential for industrial companies allowing to share information with partners in a virtual enterprise. Hence, together they can become faster and more flexible which results in an advantage in the market. However, there is still the barrier to protect own information with a fine grain. Access control graphs are an approach for this issue. Information is put into different views by executing infer mechanisms on role-based policy rules. Afterwards queries are automatically rewritten at runtime in order to match the generated views and provide only data from views that should be accessible by the authenticated role. This paper demonstrates the balance between flexibility and security using this approach. The amount and complexity of the policy rules are highly dependent on the information model used. However, a moderate restriction of the huge flexibility in the information modelling allows for few rules but those are powerful ones. Additionally, the approach allows can also be leveraged for consistency checking of Linked Data data structures. Thus, clients can rely on these information invariants and the information provider can rely on the fact that fine grained access is granted.

关键词： Irrigation Companies Semantics Servers Standards Maintenance engineering

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