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Found 17 papers, 1 papers with code
Recent advancements on MPC for tracking: periodic and harmonic formulations
Furthermore, a suitably designed MPC controller guarantees asymptotic stability of the closed-loop system to the given reference as long as its optimization problem is feasible at the initial state of the system.
Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation
This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem.
Implementation of soft-constrained MPC for Tracking using its semi-banded problem structure
Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function.
Efficient implementation of MPC for tracking using ADMM by decoupling its semi-banded structure
Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even when abrupt changes in the reference are produced.
Harmonic model predictive control for tracking periodic references
Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations.
Efficient online update of model predictive control in embedded systems using first-order methods
Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point when considering its implementation in embedded systems.
Probabilistic Safety Regions Via Finite Families of Scalable Classifiers
The notion of scalable classifiers is then exploited to link the tuning of machine learning with error control.
Tractable robust MPC design based on nominal predictions
Many popular approaches in the field of robust model predictive control (MPC) are based on nominal predictions.
Real-time implementation of MPC for tracking in embedded systems: Application to a two-wheeled inverted pendulum
This article presents the real-time implementation of the model predictive control for tracking formulation to control a two-wheeled inverted pendulum robot.
Data-Driven Methods for Present and Future Pandemics: Monitoring, Modelling and Managing
We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena.
Restart of accelerated first order methods with linear convergence under a quadratic functional growth condition
Accelerated first order methods, also called fast gradient methods, are popular optimization methods in the field of convex optimization.
Optimization and Control
Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed.
Probabilistic interval predictor based on dissimilarity functions
The method relies on the use of dissimilarity functions to estimate the conditional probability density function of the outputs.
Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm
This article presents a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control (MPC) for tracking formulation in embedded systems.
Data-Driven Methods to Monitor, Model, Forecast and Control Covid-19 Pandemic: Leveraging Data Science, Epidemiology and Control Theory
We provide a SWOT analysis and a roadmap that goes from the access to data sources to the final decision-making step.
Open Data Resources for Fighting COVID-19
In an attempt to facilitate the rapid response to the study of the seasonal behaviour of Covid-19, we enumerate the main open resources in terms of weather and climate variables.
Probabilistic performance validation of deep learning-based robust NMPC controllers
Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailored implementations.
