Model predictive control (MPC) is widely accepted as a generic multivariable controller with constraint handling. More recently, MPC has been extended to nonlinear model predictive control (NMPC) in order to realize high-performance control of highly nonlinear processes. In particular, NMPC allows incorporation of detailed process models (validated by off-line analysis) and also integrates with on-line optimization strategies consistent with higherlevel tasks, such as scheduling and planning. NMPC for tracking and so-called “economic” stage costs has been developed, and fundamental stability and robustness properties of NMPC have been analyzed. This perspective provides an overview of NMPC concepts and approaches, as well as the underlying optimization strategies that support the solution strategies. In addition, three challenging process case studies are presented to demonstrate the effectiveness of NMPC.

模型预测控制 (MPC) 作为具有约束处理的通用多变量控制器被广泛接受。最近，MPC 已扩展到非线性模型预测控制 (NMPC)，以实现对高度非线性过程的高性能控制。特别是，NMPC 允许合并详细的过程模型（通过离线分析验证），并且还与与更高级别任务（例如调度和计划）一致的在线优化策略集成。已经开发了用于跟踪和所谓的“经济”阶段成本的 NMPC，并分析了 NMPC 的基本稳定性和稳健性特性。该视角概述了 NMPC 概念和方法，以及支持解决方案策略的底层优化策略。此外，