Fast l1 model predictive control based on sensitivity analysis strategy

Hamid Kalantari

Abstract:

This study proposes a new method based on sensitivity analysis to solve a series of sequential parametric linear
programmings (LPs) such as that those arise in l1 model predictive control l1 (MPC). The main idea is to find a relationship
between each of the two successive parametric LPs by using sensitivity analysis strategy. Tolerance analysis-based MPC (TA-l1
MPC) and sensitivity analysis-based MPC (SA-l1 MPC) are introduced for reducing computational complexity and runtime. TA-l1
MPC takes O(Nn2) operations per step time, where N and n are the prediction horizon and the number of states, respectively.
This approach is very faster than generic optimisation methods but it can be applied only for initial conditions that are near to
steady-state values. SA-l1 MPC has not any limitation in usage and it reduces the runtime significantly compared with common
solvers. Finally, numerical results indicate the potential of the proposed algorithms.