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Found 18 papers, 1 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.
On the equivalence of direct and indirect data-driven predictive control approaches
Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model.
SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study
In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems.
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.
Harnessing the Final Control Error for Optimal Data-Driven Predictive Control
Model Predictive Control (MPC) is a powerful method for complex system regulation, but its reliance on accurate models poses many limitations in real-world applications.
In-context learning of state estimators
State estimation has a pivotal role in several applications, including but not limited to advanced control design.
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.
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.
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.
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.
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.
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.
