A Contraction-constrained Model Predictive Control for Multi-timescale Nonlinear Processes
Many chemical processes exhibit diverse timescale dynamics with a strong coupling between timescale sensitive variables. Model predictive control with a non-uniformly spaced optimisation horizon is an effective approach to multi-timescale control and offers opportunities for reduced computational complexity. In such an approach the fast, moderate and slow dynamics can be included in the optimisation problem by implementing smaller time intervals earlier in the prediction horizon and increasingly larger intervals towards the end of the prediction. In this paper, a reference-flexible condition is developed based on the contraction theory to provide a stability guarantee for a nonlinear system under non-uniform prediction horizons.
Code (0)
등록된 구현이 없습니다.
Tasks
Model Predictive ControlPredictionSimilar Papers 제목 키워드 기반
A Contraction-constrained Model Predictive Control for Nonlinear Processes using Disturbance Forecasts
Model predictive control (MPC) has become the most widely used advanced control method in process industry. In many cases, forecasts of the disturbances are available, e.g., predicted renewable power generation based on …
Model Predictive ControlRobust contraction-based model predictive control for nonlinear systems
Model Predictive Control (MPC) is a widely known control method that has proved to be particularly effective in multivariable and constrained control. Closed-loop stability and recursive feasibility can be guaranteed by …
Model Predictive ControlMulti-Timescale Model Predictive Control for Slow-Fast Systems
Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse applications. While model fidelity is crucial in MPC, solving the corresponding o…
Computational EfficiencyConnecting macroscopic dynamics with microscopic properties in active microtubule network contraction
The cellular cytoskeleton is an active material, driven out of equilibrium by molecular motor proteins. It is not understood how the collective behaviors of cytoskeletal networks emerge from the properties of the network…
Learning Multi-Timescale Interventions under Safety and Resource Constraints
Many sequential decision problems offer qualitatively different ways of influencing the environment: some interventions act immediately, whereas others induce persistent effects that continue to shape future states long …
Reinforcement Learning