In this paper we propose a novel quadratic model predictive control technique that constrains the number of active inputs at each control horizon instant. This problem is known as sparse control. We use an iterative convex optimization procedure to solve the corresponding optimization problem subject to sparsity constraints defined by means of the ℓ₀-norm. We also derive a sufficient condition on the minimum number of active of inputs that guarantees the exponential stability of the closed-loop system. A simulation example illustrates the benefits of the control design method proposed in the paper
Simulations for the quadratically-constrained model predictive control (qc-MPC) with power system li...
General Predictive Control (GPC) is a modern method for process control which is appropriate for man...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
© IFAC. In this paper we propose a novel quadratic model predictive control technique that constrain...
© 2017 IEEE. This note addresses the problem of feedback control with a constrained number of active...
The control based on online optimization, popularly known as model predictive control (MPC), has lon...
International audienceA technique is presented to solve the linear quadraticoptimal control problem ...
An active set algorithm tailored to quadratically constrained quadratic programming in model predict...
This paper describes a model predictive control (MPC) approach for discrete-time linear systems with...
We propose an algorithm for the effective solution of quadratic programming (QP) problems arising fr...
Explicit solutions to constrained linear model predictive control problems can be obtained by solvin...
We study stability of Model Predictive Control (MPC) with a quadratic cost function for LTI systems ...
A model predictive control (MPC) scheme is deployed via the quadratic dissipativity constraint (QDC)...
Feedback min-max model predictive control based on a quadratic cost function is addressed in this pa...
Algorithms for solving multiparametric quadratic programming (mp-QP) were proposed in Bemporad et al...
Simulations for the quadratically-constrained model predictive control (qc-MPC) with power system li...
General Predictive Control (GPC) is a modern method for process control which is appropriate for man...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...
© IFAC. In this paper we propose a novel quadratic model predictive control technique that constrain...
© 2017 IEEE. This note addresses the problem of feedback control with a constrained number of active...
The control based on online optimization, popularly known as model predictive control (MPC), has lon...
International audienceA technique is presented to solve the linear quadraticoptimal control problem ...
An active set algorithm tailored to quadratically constrained quadratic programming in model predict...
This paper describes a model predictive control (MPC) approach for discrete-time linear systems with...
We propose an algorithm for the effective solution of quadratic programming (QP) problems arising fr...
Explicit solutions to constrained linear model predictive control problems can be obtained by solvin...
We study stability of Model Predictive Control (MPC) with a quadratic cost function for LTI systems ...
A model predictive control (MPC) scheme is deployed via the quadratic dissipativity constraint (QDC)...
Feedback min-max model predictive control based on a quadratic cost function is addressed in this pa...
Algorithms for solving multiparametric quadratic programming (mp-QP) were proposed in Bemporad et al...
Simulations for the quadratically-constrained model predictive control (qc-MPC) with power system li...
General Predictive Control (GPC) is a modern method for process control which is appropriate for man...
A method of solving the online optimization in model predictive control (MPC) of input-constrained l...