Add to ORKGA stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems with arbitrary time-invariant probabilistic uncertainties and additive Gaussian process noise. Closed-loop stability of the SMPC approach is established by appropriate selection of the cost function. Polynomial chaos is used for uncertainty propagation through system dynamics. The performance of the SMPC approach is demonstrated using the Van de Vusse reactions
In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario g...
This paper presents a stochastic model predictive control approach for nonlinear systems subject to ...
In the thesis, two different model predictive control (MPC) strategies are investigated for linear s...
A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems ...
Abstract — A stochastic model predictive control (SMPC) approach is presented for discrete-time line...
The presence of uncertainty in model predictive control (MPC) has been accounted for using two types...
As the complexity and scale of chemical processes has increased, engineers have desired a process co...
This paper investigates stochastic stabilization procedures based on quadratic and piecewise linear ...
The work in this thesis focuses on the development of a Stochastic Model Predictive Control (SMPC) a...
Abstract: In this paper we address the problem of designing receding horizon control algorithms for ...
In this paper, the problem of stability, recursive feasibility and convergence conditions of stochas...
A control strategy based on a mean-variance objective and expected value constraints is proposed for...
Abstract. This paper proposes the use of Sequential Monte Carlo (SMC) as the computational engine fo...
Abstract: This paper proposes the use of Sequential Monte Carlo (SMC) as the computational engine fo...
A numerically tractable Stochastic Model Predictive Control (SMPC) strategy using Conditional Value ...
In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario g...
This paper presents a stochastic model predictive control approach for nonlinear systems subject to ...
In the thesis, two different model predictive control (MPC) strategies are investigated for linear s...
A stochastic model predictive control (SMPC) approach is presented for discrete-time linear systems ...
Abstract — A stochastic model predictive control (SMPC) approach is presented for discrete-time line...
The presence of uncertainty in model predictive control (MPC) has been accounted for using two types...
As the complexity and scale of chemical processes has increased, engineers have desired a process co...
This paper investigates stochastic stabilization procedures based on quadratic and piecewise linear ...
The work in this thesis focuses on the development of a Stochastic Model Predictive Control (SMPC) a...
Abstract: In this paper we address the problem of designing receding horizon control algorithms for ...
In this paper, the problem of stability, recursive feasibility and convergence conditions of stochas...
A control strategy based on a mean-variance objective and expected value constraints is proposed for...
Abstract. This paper proposes the use of Sequential Monte Carlo (SMC) as the computational engine fo...
Abstract: This paper proposes the use of Sequential Monte Carlo (SMC) as the computational engine fo...
A numerically tractable Stochastic Model Predictive Control (SMPC) strategy using Conditional Value ...
In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario g...
This paper presents a stochastic model predictive control approach for nonlinear systems subject to ...
In the thesis, two different model predictive control (MPC) strategies are investigated for linear s...