A method of increasing robustness of a Model Predictive Control controller controlling a process, the process having at least two manipulated variables, at least two controlled variables, and at least one active operation constraint, the method comprising: (a) using said MPC controller to control said process, wherein said process is performed in a plant; (b) providing to the MPC controller a user defined robustness factor, said factor comprising a value of a tuneable parameter; and (c) at each control cycle: identifying an ideal case optimum value for the process; characterized by calculating a modified objective function incorporating an economic objective giveaway tolerance, using the user defined robustness factor; calculating steady-state target values for the at least two manipulated variables and the at least two controlled variables using the modified objective function, such that the steady-state target values are reduced from cycle to cycle; detecting whether the calculated steady-state target values indicate movement of the at least two manipulated variables in a weak direction; adjusting the at least one active operation constraint according to the user defined robustness factor, the adjustment avoiding MPC controller movement of the at least two manipulated variables along the weak direction; and calculating a dynamic move plan for implementing the targets using the user defined robustness factor, the dynamic move plan adjusting a rate of movement of the at least two manipulated variables towards the calculated steady state target values. (a) using said MPC controller to control said process, wherein said process is performed in a plant; (b) providing to the MPC controller a user defined robustness factor, said factor comprising a value of a tuneable parameter; and (c) at each control cycle: identifying an ideal case optimum value for the process; characterized by calculating a modified objective function incorporating an economic objective giveaway tolerance, using the user defined robustness factor; calculating steady-state target values for the at least two manipulated variables and the at least two controlled variables using the modified objective function, such that the steady-state target values are reduced from cycle to cycle; detecting whether the calculated steady-state target values indicate movement of the at least two manipulated variables in a weak direction; adjusting the at least one active operation constraint according to the user defined robustness factor, the adjustment avoiding MPC controller movement of the at least two manipulated variables along the weak direction; and calculating a dynamic move plan for implementing the targets using the user defined robustness factor, the dynamic move plan adjusting a rate of movement of the at least two manipulated variables towards the calculated steady state target values. identifying an ideal case optimum value for the process; characterized by calculating a modified objective function incorporating an economic objective giveaway tolerance, using the user defined robustness factor; calculating steady-state target values for the at least two manipulated variables and the at least two controlled variables using the modified objective function, such that the steady-state target values are reduced from cycle to cycle; detecting whether the calculated steady-state target values indicate movement of the at least two manipulated variables in a weak direction; adjusting the at least one active operation constraint according to the user defined robustness factor, the adjustment avoiding MPC controller movement of the at least two manipulated variables along the weak direction; and calculating a dynamic move plan for implementing the targets using the user defined robustness factor, the dynamic move plan adjusting a rate of movement of the at least two manipulated variables towards the calculated steady state target values.