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...

Optimal Sub-References for Setpoint Tracking: A Multi-level MPC Approach

We propose a novel method to improve the convergence performance of model predictive control (MPC) for setpoint tracking, by introducing sub-references within a multilevel MPC structure. In some cases, MPC is implemented with a short prediction horizon due to limited on-line computation capacity, which could lead to deteriorated dynamic...

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...

Adaptive Parameterized Control for Coordinated traffic Management Using Reinforcement Learning

Traffic control is essential to reduce congestion in both urban and freeway traffic networks. These control measures include ramp metering and variable speed limits for freeways, and traffic signal control for urban traffic. However, current traffic control methods are either too simple to respond to complex traffic environment, or too...

Grammatical-Evolution-based parameterized Model Predictive Control for urban traffic networks

While Model Predictive Control (MPC) is a promising approach for network-wide control of urban traffic, the computational complexity of the, often nonlinear, online optimization procedure is too high for real-time implementations. In order to make MPC computationally efficient, this paper introduces a parameterized MPC (PMPC) approach for...

Hierarchical Integration of Model Predictive and Fuzzy Logic Control for Combined Coverage and Target-Oriented Search-and-Rescue via Robots with Imperfect Sensors

Search-and-rescue (SaR) in unknown environments is a crucial task with life-threatening risks. SaR requires precise, optimal, and fast decisions to be made. Robots are promising candidates expected to execute various SaR tasks autonomously. While humans use heuristics to effectively deal with uncertainties of SaR, optimisation of multiple...

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...

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...

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...

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...