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The achievement of suitable toroidal-current-density profiles in tokamak plasmas plays an important role in enabling high fusion gain and noninductive sustainment of the plasma current for steady-state operation with improved magnetohydrodynamic stability. The evolution in time of the current profile is related to the evolution of the poloidal magnetic flux, which is modeled in normalized cylindrical coordinates using a partial differential equation (PDE) usually referred to as the magnetic flux diffusion equation. The dynamics of the plasma current density profile can be modified by the total plasma current and the power of the noninductive current drive. These two actuators, which are constrained not only in value and rate but also in their initial and final values, are used to drive the current profile as close as possible to a desired target profile at a specific final time. To solve this constrained finite-time open-loop PDE optimal control problem, model reduction based on proper orthogonal decomposition is combined with sequential quadratic programming in an iterative fashion. The use of a low-dimensional dynamical model dramatically reduces the computational effort and, therefore, the time required to solve the optimization problem, which is critical for a potential implementation of a real-time receding-horizon control strategy.