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Found 31 papers, 0 papers with code
Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study
Data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming.
Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach
Predicting financial returns accurately poses a significant challenge due to the inherent uncertainty in financial time series data.
Model Predictive Control with adaptive resilience for Denial-of-Service Attacks mitigation on a Regulated Dam
In recent years, SCADA (Supervisory Control and Data Acquisition) systems have increasingly become the target of cyber attacks.
Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models
Efforts to combine these models often often stumble upon difficulties in finding a balance between accuracy and complexity.
Automatic dimensionality reduction of Twin-in-the-Loop Observers
Due to this reason, Bayesian Optimization will be used to solve a data-driven optimization problem to tune the filter.
Harnessing Uncertainty for a Separation Principle in Direct Data-Driven Predictive Control
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on an accurate model poses many limitations in real-world applications.
Hawkes-based cryptocurrency forecasting via Limit Order Book data
Accurately forecasting the direction of financial returns poses a formidable challenge, given the inherent unpredictability of financial time series.
In-context learning of state estimators
State estimation has a pivotal role in several applications, including but not limited to advanced control design.
Unraveling the Control Engineer's Craft with Neural Networks
In this paper, we present a sim2real, direct data-driven controller tuning approach, where the digital twin is used to generate input-output data and suitable controllers for several perturbations in its parameters.
Split-Boost Neural Networks
The calibration and training of a neural network is a complex and time-consuming procedure that requires significant computational resources to achieve satisfactory results.
Optimization tools for Twin-in-the-Loop vehicle control design: analysis and yaw-rate tracking case study
As the digital twin is assumed to be the best replica available of the real plant, the key issue in TiL-C becomes the tuning of the compensator, which must be performed relying on data only.
Joint vehicle state and parameters estimation via Twin-in-the-Loop observers
Vehicular control systems are required to be both extremely reliable and robust to different environmental conditions, e. g. load or tire-road friction.
Scenario-based model predictive control of water reservoir systems
The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives.
Meta-learning for model-reference data-driven control
Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages the data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design.
Model predictive control with dynamic move blocking
Model Predictive Control (MPC) has proven to be a powerful tool for the control of systems with constraints.
META-SMGO-$Δ$: similarity as a prior in black-box optimization
When solving global optimization problems in practice, one often ends up repeatedly solving problems that are similar to each others.
On the impact of regularization in data-driven predictive control
Model predictive control (MPC) is a control strategy widely used in industrial applications.
Experience in Engineering Complex Systems: Active Preference Learning with Multiple Outcomes and Certainty Levels
Black-box optimization refers to the optimization problem whose objective function and/or constraint sets are either unknown, inaccessible, or non-existent.
Uncertainty-aware data-driven predictive control in a stochastic setting
Data-Driven Predictive Control (DDPC) has been recently proposed as an effective alternative to traditional Model Predictive Control (MPC), in that the same constrained optimization problem can be addressed without the need to explicitly identify a full model of the plant.
Hourly operation of a regulated lake via Model Predictive Control
The optimal operation of regulated lakes is a challenging task involving conflicting objectives, ranging from controlling lake levels to avoid floods and low levels to water supply downstream.
The Twin-in-the-Loop approach for vehicle dynamics control
In vehicle dynamics control, engineering a suitable regulator is a long and costly process.
Data-driven design of explicit predictive controllers using model-based priors
In this paper, we propose a data-driven approach to derive explicit predictive control laws, without requiring any intermediate identification step.
Twin-in-the-loop state estimation for vehicle dynamics control: theory and experiments
In vehicle dynamics control, many variables of interest cannot be directly measured, as sensors might be costly, fragile, or even not available.
Data-driven predictive control in a stochastic setting: a unified framework
Data-driven predictive control (DDPC) has been recently proposed as an effective alternative to traditional model-predictive control (MPC) for its unique features of being time-efficient and unbiased with respect to the oracle solution.
On the design of regularized explicit predictive controllers from input-output data
The proposed explicit law is build upon a regularized implicit data-driven predictive control problem, so as to guarantee the uniqueness of the explicit predictive controller.
Learning explicit predictive controllers: theory and applications
In this paper, we deal with data-driven predictive control of linear time-invariant (LTI) systems.
Direct data-driven model-reference control with Lyapunov stability guarantees
In this work, we introduce a novel data-driven model-reference control design approach for unknown linear systems with fully measurable state.
Learning-based hierarchical control of water reservoir systems
The optimal control of a water reservoir systems represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives.
Virtual Reference Feedback Tuning with data-driven reference model selection
In control applications where finding a model of the plant is the most costly and time consuming task, Virtual Reference Feedback Tuning (VRFT) represents a valid - purely data-driven - alternative for the design of model reference controllers.
Direct data-driven control with embedded anti-windup compensation
Input saturation is an ubiquitous nonlinearity in control systems and arises from the fact that all actuators are subject to a maximum power, thereby resulting in a hard limitation on the allowable magnitude of the input effort.
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach
Continuous-time parametric models of dynamical systems are usually preferred given their physical interpretation.
