Efficient predictive model-based and fuzzy control for green urban mobility

In this thesis, we develop efficient predictive model-based control approaches, including model-predictive control (MPC) andmodel-based fuzzy control, for application in urban traffic networks with the aim of reducing a combination of the total time spent by the vehicles within the network and the total emissions. The thesis includes three main parts, where in the first part the main focus is on accurate approaches for estimating the macroscopic traffic variables, such as the temporal-spatial averages, from a microscopic point-of-view. The second part includes efficient approaches for solving the optimization problem of the nonlinear MPC controller. The third and last part of the thesis proposes an adaptive and predictivemodel-based type-2 fuzzy control scheme that can be implementedwithin amulti-agent control architecture.TRAIL Thesis Series T2017/6, the Netherlands TRAIL Research SchoolDelft Center for Systems and Contro

Evolving Fuzzy logic Systems for creative personalized Socially Assistive Robots

Socially Assistive Robots (SARs) are increasingly used in dementia and elderly care. In order to provide effective assistance, SARs need to be personalized to individual patients and account for stimulating their divergent thinking in creative ways. Rule-based fuzzy logic systems provide effective methods for automated decision-making of SARs. However, expanding and modifying the rules of fuzzy logic systems to account for the evolving needs, preferences, and medical conditions of patients can be tedious and costly. In this paper, we introduce EFS4SAR, a novel Evolving Fuzzy logic System for Socially Assistive Robots that supports autonomous evolution of the fuzzy rules that steer the behavior of the SAR. EFS4SAR combines traditional rule-based fuzzy logic systems with evolutionary algorithms, which model the process of evolution in nature and have shown to result in creative behaviors. We evaluate EFS4SAR via computer simulations on both synthetic and real-world data. The results show that the fuzzy rules evolved over time are not only personalized with respect to the personal preferences and therapeutic needs of the patients, but they also meet the following criteria for creativity of SARs: originality and effectiveness of the therapeutic tasks proposed to the patients. Compared to existing evolving fuzzy systems, EFS4SAR achieves similar effectiveness with higher degree of originality.Control & OperationsControl & Simulatio

Dynamic mathematical models of theory of mind for socially assistive robots

Interactive machines should establish and maintain meaningful social interactions with humans. Thus, they need to understand and predict the mental states and actions of humans. Based on Theory of Mind (ToM), in order to understand and interact with each other, humans develop cognitive models of one another. Our main goal is to provide a mathematical framework based on ToM to improve the understanding of interactive machines regarding the perception, cognition, and decision-making of humans. Most state-of-the-art models of behavioral theories based on machine learning are focused on input-output black-box representations. Thus, they lack transparency and generalizability, and exhaustive training procedures are needed to personalize them for various humans. Moreover, these models lack dynamics, i.e., they do not mathematically describe the evolution of the mental states and actions of humans in time. Following a systems-and-control-theoretic point-of-view, we represent for the first time the perception, cognition, and decision-making of humans via a dynamic, mathematical framework by introducing a novel formalization and an extension to Fuzzy Cognitive Maps (FCMs). The resulting models are given in a general state-space representation, which can be used by interactive machines within known model-based state estimation and control methods. In a case study, the resulting models were identified and validated for 21 participants, in scenarios where predicting the intentions and behavior of the participants required understanding the dynamics of their mental procedures. The results of these experiments show that our model is capable of incorporating the dynamics to estimate the intentions and predict the behavior of the participants, with an accuracy of, respectively, 81.55% and 66.06%. Moreover, we compared our model with a state-of-the-art formalization of human cognition, which was made dynamic using our introduced FCM framework. Our model, which in addition to the elements of the state-of-the-art model included emotions, personality traits, and biases (thus providing a more transparent insight about the mental procedures of the participants) showed 6.25% and 2.45% more accuracy in, respectively, estimating the intentions and predicting the behavior of the participants.Control & SimulationControl & Operation

An algorithm for estimating the generalized fundamental traffic variables from point measurements using initial conditions

Fundamental macroscopic traffic variables (flow, density, and average speed) have been defined in two ways: classical (defined as either temporal or spatial averages) and generalized (defined as temporal-spatial averages). In the available literature, estimation of the generalized variables is still missing. This paper proposes a new efficient sequential algorithm for estimating the generalized traffic variables using point measurements. The algorithm takes into account those vehicles that stay between two consecutive measurement points for more than one sampling cycle and that are not detected during these sampling cycles. The algorithm is introduced for single-lane roads first, and is extended to multi-lane roads. For evaluation of the proposed approach, Next Generation SIMulation (NGSIM) data, which provides detailed information on trajectories of the vehicles on a segment of the interstate freeway I-80 in San Francisco, California is used. The simulation results illustrate the excellent performance of the sequential procedure compared with other approaches.Delft Center for Systems and ControlTeam DeSchutte

A novel bi-level temporally-distributed MPC approach: An application to green urban mobility

Model predictive control (MPC) has been widely used for traffic management, such as for minimizing the total time spent or the total emissions of vehicles. When long-term green urban mobility is considered including e.g. a constraint on the total yearly emissions, the optimization horizon of the MPC problem is significantly larger than the control sampling time, and thus the number of the variables that should be optimized per control time step becomes very large. For systems with dynamics that involve nonlinear, non-convex, and non-smooth functions, including urban traffic networks, this results in optimization problems that are computationally intractable in real time. In this paper, we propose a novel bi-level temporal distribution of such complex MPC optimization problems, and we develop two mathematically linked short-term and long-term MPC formulations with small and large control sampling times that will be solved together instead of the original complex optimization problem. The resulting bi-level control architecture is used to solve the two MPC formulations online for real-time control of urban traffic networks with the objective of long-term green mobility. In order to assess the performance of the bi-level control architecture, we perform a case study where a rough version of the model of the urban traffic flow, S-model, is used by the long-term MPC level to estimate the states of the urban traffic networks, and a detailed version of the model is used by the short-term MPC level. The results of the simulations prove the effectiveness (with respect to the objective of control, as well as computational efficiency) of the proposed bi-level MPC approach, compared to state-of-the-art control approaches.Control & SimulationTransport and PlanningTeam Bart De SchutterDelft Center for Systems and Contro

Combined MPC and reinforcement learning for traffic signal control in urban traffic networks

In general, the performance of model-based controllers cannot be guaranteed under model uncertainties or disturbances, while learning-based controllers require an extensively sufficient training process to perform well. These issues especially hold for large-scale nonlinear systems such as urban traffic networks. In this paper, a new framework is proposed by combining model predictive control (MPC) and reinforcement learning (RL) to provide desired performance for urban traffic networks even during the learning process, despite model uncertainties and disturbances. MPC and RL complement each other very well, since MPC provides a sub-optimal and constraint-satisfying control input while RL provides adaptive control laws and can handle uncertainties and disturbances. The resulting combined framework is applied for traffic signal control (TSC) of an urban traffic network. A case study is carried out to compare the performance of the proposed framework and other baseline controllers. Results show that the proposed combined framework outperforms conventional control methods under system uncertainties, in terms of reducing traffic congestion. 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 SchutterControl & SimulationDelft Center for Systems and Contro