Robust model predictive control of processes with hard constraints

Finite horizon robust model predictive control with terminal cost constraints

In this paper, we develop a finite horizon model predictive control algorithm which is robust to modelling uncertainties. A moving average system matrix is constructed to capture modelling uncertainties and facilitate the future output prediction. The paper is mainly focused on the step tracking problem. Using linear matrix inequality techniques, the design is converted into a semi-definite opt...

Robust Control through Signal Constraints with Application to Predictive Control Robust Control through Signal Constraints with Application to Predictive Control

Receding-horizon Stochastic Model Predictive Control with Hard Input Constraints and Joint State Chance Constraints

This article considers the stochastic optimal control of discrete-time linear systems subject to (possibly) unbounded stochastic disturbances, hard constraints on the manipulated variables, and joint chance constraints on the states. A tractable convex second-order cone program (SOCP) is derived for calculating the receding-horizon control law at each time step. Feedback is incorporated during ...

Robust Trajectory Free Model Predictive Control of Biped Robots with Adaptive Gait Length

This paper employs nonlinear disturbance observer (NDO) for robust trajectory-free Nonlinear Model Predictive Control (NMPC) of biped robots. The NDO is used to reject the additive disturbances caused by parameter uncertainties, unmodeled dynamics, joints friction, and external slow-varying forces acting on the biped robots. In contrary to the slow-varying disturbances, handling sudden pushing ...

Robust Nonlinear Model Predictive Control of Batch Processes

NMPC explicitly addresses constraints and nonlinearities during the feedback control of batch processes. This NMPC algorithm also explicitly takes parameter uncertainty into account in the state estimation and state feedback controller designs. An extended Kalman filter estimates the process noise co®ariance matrix from the parameter uncertainty description and employs a sequential integration ...