A recent efficient Model Predictive Control (MPC) strategy uses a univariate Newton-Raphson procedure to solve a dual problem, but is not amenable to warm starting or early termination. By solving a primal problem, the current note proposes a strategy which is more efficient than the Newton-Raphson method and which enables warm starting and early termination. Performance improvements are demonstrated over the Newton-Raphson method and alternative approaches based on quadratic programming or semidefinite programming.This article is not currently available in ORA, but you may be able to access the article via the publisher copy link on this record page
Model Predictive Control (MPC) usually refers to a class of control algorithms in which a dynamic pr...
Explicit model predictive control (MPC) addresses the problem of removing one of the main drawbacks ...
Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to rec...
A recent efficient Model Predictive Control (MPC) strategy uses a univariate Newton-Raphson procedur...
A model predictive control (MPC) strategy based on augmented autonomous predictions enables a highly...
Search algorithms that reduce the time to solve the direct model predictive control (MPC) problem ar...
Model Predictive Control (MPC) is an application of control that is highly popular due to its sensib...
Abstract: In Model Predictive Control (MPC), an optimization problem has to be solved at each sampli...
A significantly important part of model predictive control (MPC) with constraints is a solution of a...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
Model predictive control (MPC) is one of the most widely spread advanced control schemes in industry...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in ...
Due to the ability to handle constraints systematically and predict system evolution with models, mo...
During the last two decades, Model Predictive Control (MPC) has established itself as an important f...
Model Predictive Control (MPC) usually refers to a class of control algorithms in which a dynamic pr...
Explicit model predictive control (MPC) addresses the problem of removing one of the main drawbacks ...
Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to rec...
A recent efficient Model Predictive Control (MPC) strategy uses a univariate Newton-Raphson procedur...
A model predictive control (MPC) strategy based on augmented autonomous predictions enables a highly...
Search algorithms that reduce the time to solve the direct model predictive control (MPC) problem ar...
Model Predictive Control (MPC) is an application of control that is highly popular due to its sensib...
Abstract: In Model Predictive Control (MPC), an optimization problem has to be solved at each sampli...
A significantly important part of model predictive control (MPC) with constraints is a solution of a...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
Model predictive control (MPC) is one of the most widely spread advanced control schemes in industry...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in ...
Due to the ability to handle constraints systematically and predict system evolution with models, mo...
During the last two decades, Model Predictive Control (MPC) has established itself as an important f...
Model Predictive Control (MPC) usually refers to a class of control algorithms in which a dynamic pr...
Explicit model predictive control (MPC) addresses the problem of removing one of the main drawbacks ...
Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to rec...