Model predictive control (MPC) is a control strategy that can handle state and control multi-variables at same time. To use the MPC using direct methods for solving the a dynamic optimization problem, one needs, for example, to transform the optimization problem into a nonlinear programming (NLP) problem by dividing the prediction horizon into equal time intervals. In this work, we suggest a tool and procedures for helping to choose a ‘compromise’ number of time intervals with a needed accuracy, objective cost, number of turned NLP iterations and computational time. On the other hand, we offer a simplified nonlinear program to ensure the convergence of a class of finite optimal control problem by modifying the state box constraints. In particular, a special type of box constraints were used to the constrained optimal control problem to enforce the state trajectories to reach the desired stationary point. These box constraints are characterized by some parameters that are easily optimized by our proposed nonlinear program. Our proposed tools are tested using two case studies; nonlinear continuous stirred tank reactor (CSTR) and nonlinear batch reactor.

模型预测控制（MPC）是一种可以同时处理状态和控制多变量的控制策略。为了使用通过直接方法来解决动态优化问题的MPC，例如，需要通过将预测范围划分为相等的时间间隔，将优化问题转换为非线性规划（NLP）问题。在这项工作中，我们建议使用一种工具和程序来帮助选择具有所需准确性，目标成本，NLP迭代次数和计算时间的“折衷”时间间隔数。另一方面，我们提供了一个简化的非线性程序，通过修改状态框约束来确保一类有限最优控制问题的收敛性。特别是，一种特殊类型的盒子约束用于约束最优控制问题，以强制状态轨迹达到所需的平稳点。这些箱形约束的特征在于一些参数，这些参数可以通过我们提出的非线性程序轻松优化。我们使用两个案例研究了我们提出的工具。非线性连续搅拌釜反应器（CSTR）和非线性间歇反应器。