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Found 9 papers, 0 papers with code
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.
Learning disturbance models for offset-free reference tracking
This work presents a nonlinear MPC framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which compensates for input disturbances and model-plant mismatch.
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.
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.
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
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.
