模型预测控制的一个主要优点是能显式并优化处理控制量和状态量的约束。为此，主要围绕非线性预测控制的算法、稳定性和鲁棒性、对偶问题和滚动时域估计的最新研究成果进行综述，并阐述了理论与应用方面有待进一步研究的几个主要问题。

We introduce in this paper a nonlinear model predictive control scheme for open-loop stable systems subject to input and state constraints. Closed-loop stability is guaranteed by an appropriate choice of the finite prediction horizon, independent of the specification of the desired control performance. In addition, this control scheme is likely to allow 'real time' implementation, because of its computational attractiveness. The theoretical results are demonstrated and discussed with a CSTR control application.

以2自由度1/4车模型为例在鲁棒控制理论的统一框架下讨论H2和H∞主动悬架的设计，并采用结构奇异值法和加权最坏RMS增益法对其鲁棒性能进行分析和比较。

We present in this paper a novel nonlinear model predictive control scheme that guarantees asymptotic closedloop stability. The scheme can be applied to both stable and unstable systems with input constraints. The objective functional to be minimized consists of an integral square error (ISE) part over a finite time horizon plus a quadratic terminal cost. The terminal state penalty matrix of the terminal cost term has to be chosen as the solution of an appropriate Lyapunov equation. Furthermore, the setup includes a terminal inequality constraint that forces the states at the end of the finite prediction horizon to lie within a prescribed terminal region. If the Jacobian linearization of the nonlinear system to be controlled is stabilizable, we prove that feasibility of the open-loop optimal control problem at time t=0 implies asymptotic stability of the closed-loop system. The size of the region of attraction is only restricted by the requirement for feasibility of the optimization problem due to the input and terminal inequality constraints and is thus maximal in some sense.