Journal of Optimization Theory and Applications
An extension of Newton-type algorithms for nonlinear process control
Automatica (Journal of IFAC)
A Real-Time Iteration Scheme for Nonlinear Optimization in Optimal Feedback Control
SIAM Journal on Control and Optimization
The explicit linear quadratic regulator for constrained systems
Automatica (Journal of IFAC)
Input-to-state stability for discrete-time nonlinear systems
Automatica (Journal of IFAC)
Robust nonlinear model predictive controller design based on multi-scenario formulation
ACC'09 Proceedings of the 2009 conference on American Control Conference
Explicit output-feedback nonlinear predictive control based on black-box models
Engineering Applications of Artificial Intelligence
An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range
Automatica (Journal of IFAC)
Terrain Avoidance Nonlinear Model Predictive Control for Autonomous Rotorcraft
Journal of Intelligent and Robotic Systems
Hi-index | 22.15 |
Widespread application of dynamic optimization with fast optimization solvers leads to increased consideration of first-principles models for nonlinear model predictive control (NMPC). However, significant barriers to this optimization-based control strategy are feedback delays and consequent loss of performance and stability due to on-line computation. To overcome these barriers, recently proposed NMPC controllers based on nonlinear programming (NLP) sensitivity have reduced on-line computational costs and can lead to significantly improved performance. In this study, we extend this concept through a simple reformulation of the NMPC problem and propose the advanced-step NMPC controller. The main result of this extension is that the proposed controller enjoys the same nominal stability properties of the conventional NMPC controller without computational delay. In addition, we establish further robustness properties in a straightforward manner through input-to-state stability concepts. A case study example is presented to demonstrate the concepts.