An Approach to Line Balancing on Virtual Supervisor Induction Method and Intelligent Agents

This approach develops a method for solving the line-balancing problem, which is based on two stages. The works in a first stage is to identify the task of workstation, the assignment of the tasks to stations on the line and the recognized balance delay. In this stage we propose the induction VS method, which allows further identify the exact position between pieces, machine into a workstation and also between extern workstation, as well as intracellular and intercellular part. This way each task is identified and measured. In the second stage is to carry out a macro-approach to choose the resource to perform each of them. The hybrid intelligent agent architecture is proposed for this second stage, which has consideration of machining sequence. The integration between both technologies allows us to develop new hybrid architecture capable to reduce the computational time in the deliberative layers fundamentally. Finally, a reconfigurable testbed has been proposed for future experiments and results to evaluate this new balancing method. Some previous computational experiments provide that the proposed approach is efficient to solve practical transfer line design for balancing problem

A dynamic optimization framework for integration of design, control and scheduling of multi-product chemical processes under disturbance and uncertainty

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.compchemeng.2017.05.007 © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/A novel dynamic optimization framework is presented for integration of design, control, and scheduling for multi-product processes in the presence of disturbances and parameter uncertainty. This framework proposes an iterative algorithm that decomposes the overall problem into flexibility and feasibility analyses. The flexibility problem is solved under a critical (worst-case) set of disturbance and uncertainty realizations, whereas the feasibility problem evaluates the dynamic feasibility of each realization, and updates the critical set accordingly. The algorithm terminates when a robust solution is found, which is feasible under all identified scenarios. To account for the importance of grade transitions in multiproduct processes, the proposed framework integrates scheduling into the dynamic model by the use of flexible finite elements. This framework is applied to a multi-product continuous stirred-tank reactor (CSTR) system subject to disturbance and parameter uncertainty. The proposed method is shown to return robust solutions that are of higher quality than the traditional sequential method. The results indicate that scheduling decisions are affected by design and control decisions, thus motivating the need for integration of these three aspects.Natural Sciences & Engineering Council of Canada (NSERC)Ontario Graduate Scholarship (OGS

Dynamics analysis and integrated design of real-time control systems

Real-time control systems are widely deployed in many applications. Theory and practice for the design and deployment of real-time control systems have evolved significantly. From the design perspective, control strategy development has been the focus of the research in the control community. In order to develop good control strategies, process modelling and analysis have been investigated for decades, and stability analysis and model-based control have been heavily studied in the literature. From the implementation perspective, real-time control systems require timeliness and predictable timing behaviour in addition to logical correctness, and a real-time control system may behave very differently with different software implementations of the control strategies on a digital controller, which typically has limited computing resources. Most current research activities on software implementations concentrate on various scheduling methodologies to ensure the schedulability of multiple control tasks in constrained environments. Recently, more and more real-time control systems are implemented over data networks, leading to increasing interest worldwide in the design and implementation of networked control systems (NCS). Major research activities in NCS include control-oriented and scheduling-oriented investigations. In spite of significant progress in the research and development of real-time control systems, major difficulties exist in the state of the art. A key issue is the lack of integrated design for control development and its software implementation. For control design, the model-based control technique, the current focus of control research, does not work when a good process model is not available or is too complicated for control design. For control implementation on digital controllers running multiple tasks, the system schedulability is essential but is not enough; the ultimate objective of satisfactory quality-of-control (QoC) performance has not been addressed directly. For networked control, the majority of the control-oriented investigations are based on two unrealistic assumptions about the network induced delay. The scheduling-oriented research focuses on schedulability and does not directly link to the overall QoC of the system. General solutions with direct QoC consideration from the network perspective to the challenging problems of network delay and packet dropout in NCS have not been found in the literature. This thesis addresses the design and implementation of real-time control systems with regard to dynamics analysis and integrated design. Three related areas have been investigated, namely control development for controllers, control implementation and scheduling on controllers, and real-time control in networked environments. Seven research problems are identified from these areas for investigation in this thesis, and accordingly seven major contributions have been claimed. Timing behaviour, quality of control, and integrated design for real-time control systems are highlighted throughout this thesis. In control design, a model-free control technique, pattern predictive control, is developed for complex reactive distillation processes. Alleviating the requirement of accurate process models, the developed control technique integrates pattern recognition, fuzzy logic, non-linear transformation, and predictive control into a unified framework to solve complex problems. Characterising the QoC indirectly with control latency and jitter, scheduling strategies for multiple control tasks are proposed to minimise the latency and/or jitter. Also, a hierarchical, QoC driven, and event-triggering feedback scheduling architecture is developed with plug-ins of either the earliest-deadline-first or fixed priority scheduling. Linking to the QoC directly, the architecture minimises the use of computing resources without sacrifice of the system QoC. It considers the control requirements, but does not rely on the control design. For real-time NCS, the dynamics of the network delay are analysed first, and the nonuniform distribution and multi-fractal nature of the delay are revealed. These results do not support two fundamental assumptions used in existing NCS literature. Then, considering the control requirements, solutions are provided to the challenging NCS problems from the network perspective. To compensate for the network delay, a real-time queuing protocol is developed to smooth out the time-varying delay and thus to achieve more predictable behaviour of packet transmissions. For control packet dropout, simple yet effective compensators are proposed. Finally, combining the queuing protocol, the packet loss compensation, the configuration of the worst-case communication delay, and the control design, an integrated design framework is developed for real-time NCS. With this framework, the network delay is limited to within a single control period, leading to simplified system analysis and improved QoC

Optimization Algorithms for Integration of Design, Control, and Scheduling for Chemical Processes Subject to Disturbances and Uncertainty

Optimization of multiproduct processes is vital for process performance, especially during dynamic transitions between operating points. However, determining the optimal operating conditions can be a challenging problem, since many aspects must be considered, such as design, control, and scheduling. This problem is further complicated by process disturbances and parameter uncertainty, which are typically randomly distributed variables that traditional methods of optimization are not equipped to handle. Multi-scenario approaches that consider every possible realization are also impractical, as they quickly become computationally prohibitive for large-scale applications. Therefore, new methods are emerging for generating robust solutions without adding excessive complexity. This thesis focuses on the development of two of those optimization methods for the integration of design, control, and scheduling for multi-product processes in the presence of disturbances and parameter uncertainty. Firstly, a critical set method is presented, which decomposes the overall problem into flexibility and feasibility analyses. The flexibility problem is solved under a critical (worst-case) set of disturbance and uncertainty realizations, which is faster than considering the entire (non-critical) set. The feasibility problem evaluates the dynamic feasibility of the entire set, and updates the critical set accordingly, adding any realizations that are found to be infeasible. The algorithm terminates when a robust solution is found, which is feasible under all identified scenarios. To account for the importance of grade transitions in multiproduct processes, the proposed framework integrates scheduling into the dynamic model by the use of flexible finite elements. The critical set method is applied to two case studies, a continuous stirred-tank reactor (CSTR) and a plug flow reactor (PFR), both subject to process disturbance and parameter uncertainty. The proposed method is shown to return robust solutions that are of higher quality than the traditional sequential method, which determines the design, control, and scheduling independently. This work also considers the development of a back-off method for integration of design, control, and scheduling for multi-product systems subject to disturbances and parameter uncertainty. The key feature of this method is the consideration of stochastic random variables for the process disturbance and parameter uncertainty, while most works discretize these variables. This method employs Monte Carlo (MC) sampling to generate a large number of random realizations, and simulate the system to determine feasibility. Back-off terms are determined and incorporated into a new flexibility analysis to approximate the effect of stochastic uncertainty and disturbances. The back-off terms are refined through successive iterations, and the algorithm converges, terminating on a solution that is robust to a specified level of process variability. The back-off method is applied to a similar CSTR case study for which optimal design, control, and scheduling decisions are identified, subject to stochastic uncertainty and disturbance. Another scenario is analyzed, where the CSTR is controlled in open-loop, and the control actions are determined directly from the optimization. The back-off method successfully produces solutions in both scenarios, which are robust to specified levels of variability, and consider stochastic representations of process disturbance and parameter uncertainty. The results from the case studies indicate that there are interactions between optimal design, control, scheduling, disturbance, and uncertainty, thus motivating the need for integration of all these aspects using the methods described in this thesis. The solutions provided by the critical set method and the back-off can be compared, since the methods are applied to the same CSTR case study, aside from the differences in disturbance and uncertainty. The back-off method offers a slightly improved solution, though the critical set method demands much less computational time. Therefore, both methods have benefits and limitations, so the optimal method would depend on the available computational time, and the desired quality and robustness of the solution

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Measurements, Models, Systems and Design

531 s.

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors