What is stochastic model predictive control?
Table of Contents
What is stochastic model predictive control?
Stochastic model predictive control (SMPC) provides a probabilistic framework for MPC of systems with stochastic uncertainty. Estimation of stochastic disturbances as well as the impact of estimation quality of stochastic disturbances on the SMPC performance are discussed.
What is the main idea behind model predictive control?
The basic idea behind the min/max MPC approach is to modify the on-line “min” optimization to a “min-max” problem, minimizing the worst case of the objective function, maximized over all possible plants from the uncertainty set.
How do you create a predictive control model?
How to Design Model Predictive Controllers
- Choose the sampling time for a model predictive controller.
- Choose prediction and control horizons.
- Choose constraints.
- Choose weights.
- Estimate current plant states.
What is robust model predictive control?
The main goal of robust MPC is to devise an optimization-based control synthesis method that accounts for the intricate interactions of the uncertainty with the system, constraints, and performance criteria in a theoretically rigorous and computationally tractable way.
Is MPC reinforcement learning?
Reinforcement Learning (RL) has demonstrated a huge potential in learning optimal policies without any prior knowledge of the process to be controlled. Model Predictive Control (MPC) is a popular control technique which is able to deal with nonlinear dynamics and state and input constraints.
What is Tube MPC?
Tube MPC is an approximate solution strategy in which a robust controller, designed offline, keeps the system in an invariant tube around a desired nominal trajectory, generated online. Naturally, this decomposition is suboptimal, especially for systems with changing objectives or operating conditions.
What is OPC Toolbox?
OPC Toolbox™ provides access to live and historical OPC data directly from MATLAB® and Simulink®. You can read, write, and log OPC data from devices, such as distributed control systems, supervisory control and data acquisition systems, and programmable logic controllers.