Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Model predictive control: theory and practice—a survey
Automatica (Journal of IFAC)
Interior point methods for optimal control of discrete time systems
Journal of Optimization Theory and Applications
Application of interior-point methods to model predictive control
Journal of Optimization Theory and Applications
A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability
Automatica (Journal of IFAC)
Survey paper: Set invariance in control
Automatica (Journal of IFAC)
Survey Constrained model predictive control: Stability and optimality
Automatica (Journal of IFAC)
Real-Time Nonlinear Optimization as a Generalized Equation
SIAM Journal on Control and Optimization
Extremum-seeking control of state-constrained nonlinear systems
Automatica (Journal of IFAC)
Hi-index | 22.15 |
A new formulation of nonlinear model predictive control (MPC) is developed by including a weighted barrier function in the control objective. While the barrier ensures that inequality constraints are strictly satisfied it also provides a smooth transition between points in the interior and those on the boundary of the constraint set. In addition, the resulting optimisation problem, to be solved at each control step, is effectively unconstrained and thus amenable to elegant optimisation techniques. The barrier must satisfy certain conditions in order that the state converges to the origin and we show how to construct such a barrier. Conventional MPC may be seen as a limiting case of the new class as the barrier weighting itself approaches zero. We pay particular attention to the novel approach of fixing the weighting parameter to some positive value-possibly large-and observe that this provides a degree of controller caution near constraint boundaries. We construct an ellipsoidal invariant set by exploiting the geometry of self-concordant functions and show nominal closed-loop stability for this class of controllers under full state feedback.