1,720,959 research outputs found
Hybrid Human in the Loop Model Predictive Control applied to Polder Management: Case study Ommoord
No full textIn the Netherlands, flood protection has always been a key issue to protect settlements against storm surges and floods. Around 60 % of the country is potentially affected by flood from the rivers and the sea. Most of the flood-susceptible areas are low-lying polders. Whereas flood protection has traditionally been focused on structural measures, nowadays the increasing availability of system-wide, real-time data acquisition, and disturbance forecasting systems enable the use of advanced new control methods. In polders, normally flooding is prevented by pumping stations that pump water out of the system. In drier periods, water can be let into the system using inlets. In the Netherlands there are still many inlets that are not automated but controlled by a human operator. As a result of this, the inlet configuration can only be changed when the human operator travels to the location of the inlet. The operator's work is to perform inspection, configuration, and maintenance tasks on all local water engineering structures. The operator is responsible for various pumping areas and is therefore not always available to change the configuration of the inlet. Previously, there was not much attention for inlet control because the pumping stations have enough capacity to pump the added water away. The surplus of water that is let in, has to be pumped out again, which is a waste of energy. Nowadays, energy consumption reduction receives increasing attention and a more sustainable control strategy for the inlets is preferred. Automating all inlets is a costly measure, while the operator has to be in the area anyways. Therefore, in this thesis it is investigated how the control strategy can improved while keeping the operator in the control loop, this method is denoted as Human in the Loop (HIL) control.Mechanical, Maritime and Materials EngineeringDelft Center for Systems and Control (DCSC
System Identification for Nonlinear Model Predictive Control of a Drainage Canal System
No full textMechanical, Maritime and Materials EngineeringDelft Center for Systems and Control (DCSC
Dynamic reverse Stackelberg games in infrastructure maintenance contract design
No full textInfrastructure managers outsource maintenance activities to maintenance service contractors. This gives rise to misalignment between a contractor's short-term objectives and an infrastructure manager's long-term objectives. Infrastructure managers have started to explore various forms of contracts in which they incentivize contractors to work in the infrastructure managers' best interest. In this master thesis, a game-theoretical approach is taken for the design of new contracts. In order to align their objectives, the infrastructure manager announces a policy to the contractor, in which he specifies which decisions he will make as a reaction to the contractor's decisions. This problem is formulated as a dynamic reverse Stackelberg game. The payment scheme the leader pays to the follower is considered the leader function. This payment scheme is dependent on the infrastructure's failure rate. The resulting game allows both parties to achieve the best possible result, while taking into account the hierarchical and the sequential aspects of an infrastructure maintenance contract. In the resulting dynamic reverse Stackelberg game, both players maximize their utility over their planning window by adapting to each other's decisions at each consecutive decision stage. The utility of the leader is dependent on the infrastructure's failure rate, that in turn is dependent on the infrastructure's deterioration dynamics and all maintenance actions. The infrastructure manager gives the contractor incentives to perform the maintenance actions that are optimal for the infrastructure manager, at stages that are optimal for the infrastructure manager. This results in a leader function for the infrastructure manager and a set of maintenance actions for the contractor that maximize each player's utility. Two scenarios have been examined: one with complete information and one with incomplete information. Two models have been constructed based on this game: a general model and a replacement threshold model that restricts the follower to apply a threshold to the condition of the infrastructure; replacements are then performed at the stage that the condition of the infrastructure passes a threshold. The first model achieves the best possible result, while the second model is more realistic and more computationally efficient. The models can be used by infrastructure managers to design contracts to optimize their utility.Mechanical, Maritime and Materials EngineeringDelft Center for Systems and Contro
Incentivizing renewables and reducing grid imbalances through market interaction: A forecasting and control approach
No full textAs the penetration of renewable energy sources (RESs) increases, so does the dependence of electricity production on weather and, in turn, the uncertainty in electricity generation, the volatility in electricity prices, and the imbalances between production and consumption. In this context, while RES integration does complicate grid balance and increase price volatility, it also opens up opportunities for flexible market agents to reduce grid imbalances. In particular, by using the nature of the interactions between electricity markets and grid balance, market agents can reduce grid imbalances while increasing their profit. However, despite this obvious win-win situation, traditional research in this field has focused on balancing mechanisms that do not always exploit these relations between electricity markets and grid balance.Team Bart De Schutte
Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks
No full textData-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the traffic dynamics of the network regions are first transformed into MFAPC data models, and then, the derived MFAPC data models instead of mathematical traffic models serve as the prediction models in the distributed control design. The formulated control problem is finally solved with an alternating direction method of multipliers (ADMM)-based approach. The simulation results for the traffic network of Linfen, Shanxi, China, show the feasibility and effectiveness of the proposed method.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Delft Center for Systems and ControlTeam Bart De Schutte
Timetable Scheduling for Passenger-Centric Urban Rail Networks: Model Predictive Control based on a Novel Absorption Model
No full textTimetable scheduling plays a key role in daily operations of urban rail transit systems, as it determines the quality of service provided to passengers. In order to develop efficient timetable scheduling methods, it is necessary to develop a proper model to integrate timetable-related and passenger-related factors in urban rail network efficiently. In this paper, a novel passenger absorption model for passenger- centric urban rail networks is established. The model explicitly integrates time-varying passenger origin-destination demands and the departure frequency of each line for real-time timetable scheduling. Then, a model predictive control (MPC) method for the timetable scheduling problem is proposed based on the developed model. The resulting MPC optimization problem can be formulated as a mixed-integer programming (MILP) problem, which can be solved efficiently by using the existing MILP solvers. The effectiveness of the absorption model and the corresponding MILP-based MPC approach is illustrated through the case study based on two Beijing subway lines. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Bart De SchutterTeam Azita DabiriDelft Center for Systems and Contro
Modeling, robust and distributed model predictive control for freeway networks
No full textIn Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of microscopic traffic models. These macroscopic traffic models can be divided into homogeneous, single-class models and heterogeneous, multi-class models. In general, multi-class models are more accurate than single-class models, without increasing the computational complexity significantly. In MPC a more accurate model in general implies a better prediction of the controlled system, providing the controller more accurate information for determining the control actions. Therefore, developing and using multi-class traffic models is one way to improve the effectiveness of MPC. Apart from the above characteristics of traffic models, other factors such as uncertainties in external inputs and model parameters can also affect the accuracy of predictions. Thus another way for improving the effectiveness of MPC is to take into account the effects of these uncertainties and to develop robust MPC approaches for handling these uncertainties. Apart from improving the effectiveness of MPC, making MPC feasible for large-scale traffic networks is also important, due to the rapid increase of the computational complexity of the MPC optimization problem with the size of the controlled system. For large-scale systems, Distributed Model Predictive Control (DMPC) is often considered for making the control approach computationally feasible. Moreover, robust DMPC can be developed for ensuring both feasibility and robustness
Adaptive distributed control of uncertain multi-agent systems in the power-chained form
No full textPower-chained form systems are a generalization of strict-feedback and pure-feedback systems since integrators with positive odd-powers can appear in the dynamics (chain of positive-odd power integrators) and they are extremely challenging to deal with, as their linearized dynamics might possess uncontrollable modes whose eigenvalues are in the right-hand-side plane, making standard feedback linearization or standard backstepping methodologies fail. The adding-one-power-integrator technique was proposed to handle power-chained formsystems. Progress made for power-chained formsystems includes employing universal approximators to handle completely unknown nonlinearities. However, state-of-the-art results on power-chained form systems are mainly focused on the single-agent case since a direct extension of the existing design to a distributed setting is not very meaningful on account of the facts that: i) the control gain of each virtual control is incorporated into the next virtual control law iteratively, possibly leading to high-gain issues; ii) state-of-the-art results rely on the assumption that the agents’ control directions are known a priori and are available for control design; iii) universal approximators often used in the adding-one-power-integrator procedure inevitably increase the complexity in the sense that extra adaptive parameters have to be updated (i.e. extra nonlinear differential equations need to be solved numerically), thus making their distributed implementation difficult.Team DeSchutte
Coordinated model predictive control of synchromodal freight transport systems
No full textTRAIL Thesis Series T2016/9, the Netherlands TRAIL Research SchoolTeam Bart De Schutte
Traffic management optimization of railway networks
No full textThis thesis adopts optimization approaches to tackle the traffic management problem for railway networks, aiming at achieving better performance of railway operations, in terms of punctuality, reliability, non-discrimination, capacity utilization, and energy efficiency. Specifically, the following four aspects are considered: - Non-discriminatory traffic control; - Traffic control cooperating with a preventive maintenance plan; - Traffic control integrating with train control; - Distributed optimization of traffic control for large networks.TRAIL Thesis Series no. T2019/10, The Netherlands TRAIL Research SchoolTransport Engineering and Logistic
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