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.

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.

将准无限时域非线性预测控制方法推广到更一般的情况，并给出了闭环约束系统的稳定性条件及最优解的存在条件。基于反馈线性化技术讨论了广义准无限时域非线性预测控制的实现及较大终端域的获取。该方法能显著减少在线优化所需的时间。

This paper formulates the active suspension control problem as disturbance attenuation problem with output and control constraints. The H∞ performance is used to measure ride comfort such that more general road disturbances can be considered, while timedomain hard constraints are captured using the concept of reachable sets and state-space ellipsoids. Hence, conflicting requirements are specified separately and handled in a nature way. In the framework of Linear Matrix Inequality (LMI) optimization, constrained H∞ active suspensions are designed on half-car models with and without considering actuator dynamics. Analysis and simulation results show a promising improvement on ride comfort, while keeping suspension strokes and control inputs within bounds and ensuring a firm contact of wheels to road.

融合H∞控制的鲁棒概念和预测控制的滚动优化原理，提出了一种全新的约束动态对策预测控制方法。对有状态和控制约束的不确定线性系统，证明了闭环系统的鲁棒稳定性并给出了鲁棒性条件。该方法同时具有H∞控制和预测控制的优点：鲁棒性和显式处理约束的能力。