1. A computer-implemented method for controlling a batch-type manufacturing process with a finite duration, the method comprising: receiving dependent variable data and manipulated variable data associated with the batch-type manufacturing process, the dependent variable data including measured past and present values of a first set of process parameters observed by one or more sensors, the manipulated variable data including measured past and present values of a second set of process parameters measured from a plurality of process tools, wherein the first set of process parameters, representative of dependent variables, and the second set of process parameters, representative of manipulated variables, are X-type variables in the batch-type manufacturing process; determining, using a multivariate model of the manufacturing process, one or more multivariate statistics based on at least the dependent variable data and the manipulated variable data, wherein each multivariate statistic, which comprises a Hotelling value, a residual standard deviation value, a principal component score or a partial least squares component score, measures a deviation of the batch-type manufacturing process from a multivariate space of normal process behavior; determining future values of the manipulated variables by optimizing an objective function that comprises J=θ Y (Y SP −Y pred ) 2 +θ MV (E MV ) 2 +θ DModX (E DModX ) 2 +θ T2 (E T2 ) 2 +θ t (E t ) 2 , wherein (i) Y SP represents at least one setpoint or target value for Y-type yield variables representative of yield or quality at the end of the finite duration of the batch-type manufacturing process, (ii) Y pred represents at least one predicted value for the yield variables, (iii) E MV represents an amount of deviation in the manipulated variable data from a desired trajectory subject to a penalty weight θ MV , (iv) E DModX represents an amount of the residual standard deviation value subject to a penalty weight θ DModX , (v) E T2 represents an amount of the Hotelling value subject to a penalty weight θ T2 , (vi) E t represents an amount of the principal component score or the partial least squares component score subject to a penalty weight θ t , (vii) and θ Y represents a penalty weight; adjusting at least one of the second set of process parameters based on the future values of the manipulated variables.