scholarly journals Lyapunov-based stochastic nonlinear model predictive control: Shaping the state probability distribution functions

2016 ◽  
Author(s):  
Edward A. Buehler ◽  
Joel A. Paulson ◽  
Ali Mesbah
Keyword(s):  
Probability Distribution ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Distribution Functions ◽  
The State ◽  
Nonlinear Model Predictive Control ◽  
State Probability ◽  
Probability Distribution Functions

scholarly journals Experimental Validation of a Guaranteed Nonlinear Model Predictive Control

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 248
Author(s):  
Mohamed Fnadi ◽  
Julien Alexandre dit Sandretto
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Interval Analysis ◽  
Optimization Procedure ◽  
The State ◽  
Nonlinear Model Predictive Control ◽  
Control Input ◽  
Time Step ◽  
Prediction Horizon

This paper combines the interval analysis tools with the nonlinear model predictive control (NMPC). The NMPC strategy is formulated based on an uncertain dynamic model expressed as nonlinear ordinary differential equations (ODEs). All the dynamic parameters are identified in a guaranteed way considering the various uncertainties on the embedded sensors and the system’s design. The NMPC problem is solved at each time step using validated simulation and interval analysis methods to compute the optimal and safe control inputs over a finite prediction horizon. This approach considers several constraints which are crucial for the system’s safety and stability, namely the state and the control limits. The proposed controller consists of two steps: filtering and branching procedures enabling to find the input intervals that fulfill the state constraints and ensure the convergence to the reference set. Then, the optimization procedure allows for computing the optimal and punctual control input that must be sent to the system’s actuators for the pendulum stabilization. The validated NMPC capabilities are illustrated through several simulations under the DynIbex library and experiments using an inverted pendulum.

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