Among the multitude of modern control methods, model predictive control (MPC) is one of the most successful [1]–[4]. As noted in “Summary,” this success is largely due to the ability of MPC to respect constraints on controls and enforce constraints on outputs, both of which are difficult to handle with linear control methods, such as linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG), and nonlinear control methods, such as feedback linearization and sliding mode control. Although MPC is computationally intensive, it is more broadly applicable than Hamilton–Jacobi–Bellman-based control and more suitable for feedback control than the minimum principle. In many cases, the constrained optimization problem for receding-horizon optimization is convex, which facilitates computational efficiency [5].

中文翻译：

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预测成本自适应控制：持久性、一致性和紧急性的数值研究


在众多现代控制方法中，模型预测控制（MPC）是最成功的方法之一[1]-[4]。正如“总结”中所指出的，这种成功很大程度上归功于 MPC 能够尊重控制约束并强制输出约束，这两者都很难用线性控制方法来处理，例如线性二次调节器 (LQR) 和线性二次高斯 (LQG) 和非线性控制方法，例如反馈线性化和滑模控制。尽管MPC计算量大，但它比基于Hamilton-Jacobi-Bellman的控制更广泛适用，并且比最小原理更适合反馈控制。在许多情况下，后退优化的约束优化问题是凸的，这有利于计算效率[5]。