Stochastic Model Predictive Controller.
Research Area : Probability & Statistics, Stochastic Model Predictive Control
Nirlipta Mohanti
Guide : Dr. D. N. Sonawane
Year : 2018-2021
Mail : [email protected]
My Profile
Research Area : Probability & Statistics, Stochastic Model Predictive Control
Nirlipta Mohanti
Guide : Dr. D. N. Sonawane
Year : 2018-2021
Mail : [email protected]
My Profile
Abstract -
System uncertainty poses a key theoretical and practical challenges in the performance of Model Predictive Controller (MPC), which can be aggravated when system uncertainty increases due to the time-varying nature of system dynamics. In real-world systems, uncertainties are often considered to be of probabilistic nature. When the probabilistic descriptions of system uncertainties are available, they can be explicitly incorporated into an MPC formulation with the aim to achieve the robustness to uncertainties in a probabilistic sense.
This notion has led to stochastic Model Predictive Control (SMPC) that solves a stochastic optimal control problem in which the cost function and constraints are defined in terms of probability distribution of the system state. SMPC allows for systematically seeking tradeoffs between fulfilling the control objectives and guaranteeing a probabilistic constraint satisfaction due to uncertainty. In addition, the probabilistic framework of SMPC enables shaping the probability distribution of system states/outputs. The ability to regulate the probability distribution of system states/outputs is important for the safe and economic operation of the complex systems.
The work focuses on designing of Stochastic Model Predictive Control (SMPC) that systematically incorporating the probabilistic descriptions of uncertainties into a stochastic Optimal Control Problem (OCP).
System uncertainty poses a key theoretical and practical challenges in the performance of Model Predictive Controller (MPC), which can be aggravated when system uncertainty increases due to the time-varying nature of system dynamics. In real-world systems, uncertainties are often considered to be of probabilistic nature. When the probabilistic descriptions of system uncertainties are available, they can be explicitly incorporated into an MPC formulation with the aim to achieve the robustness to uncertainties in a probabilistic sense.
This notion has led to stochastic Model Predictive Control (SMPC) that solves a stochastic optimal control problem in which the cost function and constraints are defined in terms of probability distribution of the system state. SMPC allows for systematically seeking tradeoffs between fulfilling the control objectives and guaranteeing a probabilistic constraint satisfaction due to uncertainty. In addition, the probabilistic framework of SMPC enables shaping the probability distribution of system states/outputs. The ability to regulate the probability distribution of system states/outputs is important for the safe and economic operation of the complex systems.
The work focuses on designing of Stochastic Model Predictive Control (SMPC) that systematically incorporating the probabilistic descriptions of uncertainties into a stochastic Optimal Control Problem (OCP).
