A recent efficient Model Predictive Control (MPC) strategy uses a univariate NewtonRaphson 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 NewtonRaphson method and which enables warm starting and early termination. Performance improvements are demonstrated over the NewtonRaphson method and alternative approaches based on quadratic programming or semidefinite programming. © 2010 Elsevier Ltd. All rights reserved
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but...
Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to rec...
This paper considers the problem of solving Quadratic Programs (QPs) in the context of robust Model ...
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...
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...
Search algorithms that reduce the time to solve the direct model predictive control (MPC) problem ar...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
Model predictive control (MPC) is one of the most widely spread advanced control schemes in industry...
Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in ...
© 2017 IEEE. We present PANOC, a new algorithm for solving optimal control problems arising in nonli...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
During the last two decades, Model Predictive Control (MPC) has established itself as an important f...
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but...
Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to rec...
This paper considers the problem of solving Quadratic Programs (QPs) in the context of robust Model ...
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...
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...
Search algorithms that reduce the time to solve the direct model predictive control (MPC) problem ar...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
Model predictive control (MPC) is one of the most widely spread advanced control schemes in industry...
Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in ...
© 2017 IEEE. We present PANOC, a new algorithm for solving optimal control problems arising in nonli...
In model predictive control (MPC), also called recedinghorizon control, the control input is obtaine...
During the last two decades, Model Predictive Control (MPC) has established itself as an important f...
Model predictive control (MPC) provides a useful means for controlling systems with constraints, but...
Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to rec...
This paper considers the problem of solving Quadratic Programs (QPs) in the context of robust Model ...