Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
On linear programming and robust modelpredictive control using impulse-responses
Systems & Control Letters
Robust constrained model predictive control using linear matrix inequalities
Automatica (Journal of IFAC)
Worst-case formulations of model predictive control for systems with bounded parameters
Automatica (Journal of IFAC)
Optimization over state feedback policies for robust control with constraints
Automatica (Journal of IFAC)
Characterization of the solution to a constrained H∞ optimal control problem
Automatica (Journal of IFAC)
| Hi-index | 22.14 |
An efficient optimization procedure is proposed for computing a receding horizon control law for linear systems with linearly constrained control inputs and additive disturbances. The procedure uses an active set approach to solve the dynamic programming problem associated with the min-max optimization of an H"~ performance index. The active constraint set is determined at each sampling instant using first-order necessary conditions for optimality. The computational complexity of each iteration of the algorithm depends linearly on the prediction horizon length. We discuss convergence, closed loop stability and bounds on the disturbance l^2-gain in closed loop operation.