运用区间算法的约束非线性系统鲁棒模型预测控制

针对一类具有输入和状态约束的干扰有界非线性系统,提出了基于区间分析的约束非线性鲁棒模型预测控制,以降低计算量并扩大系统吸引域.首先,在集合运算的基础上,利用区间运算和函数区间扩展,给出了一种计算效能更好、保守性更低的非线性系统鲁棒一步集计算方法;其次,构造重叠的多面体控制不变集序列并以此计算约束非线性系统的鲁棒多步集,并通过设计基于集合的在线优化策略,提出了基于鲁棒一步集的单步优化非线性模型预测控制,有效降低了非线性优化的在线计算量;最后,仿真实例验证了算法的有效性.     

基于鲁棒一步集的Tube不变集鲁棒模型预测控制

针对一类干扰有界的输入和状态受约束线性离散系统,提出了一种基于鲁棒一步集的Tube不 变集鲁棒模型预测控制方法.首先采用多面体不变集离线设计方法得到基于多面体不变集序列的扩 展终端约束集;然后为了扩大鲁棒模型预测控制的初始状态允许区域,并提高系统的鲁棒性,在扩展终端约束集的基础上,通过引入鲁棒一步集并借助Tube不变集控制策略,设计了基于鲁棒一步集的鲁棒模型预测控制方法,并给出了算法的存在性和稳定性证明. 该方法不仅极大地扩大了初始状态允许区域,而且对有界干扰具有有效的抑制作用,使得受扰系统收敛到以原点为中心的最小鲁棒正不变集内.最后仿真验证了算法的有效性.     

采用鲁棒容许集对有约束的不确定系统作鲁棒模型预测控制

针对输入和状态受约束的干扰有界多胞不确定线性系统,提出了基于鲁棒容许集的扩大吸引域鲁棒模型预测控制(RMPC)方法.首先给出了多面体不变集的鲁棒容许集计算方法,并推导了鲁棒容许集存在的充分必要条件.其次,为了拓展Tube不变集鲁棒模型预测控制算法的适用范围,讨论了干扰有界多胞不确定线性系统的Tube不变集控制策略.之后为了扩大约束系统吸引域,提出了干扰有界多胞不确定系统的鲁棒容许集模型预测控制策略.通过采用鲁棒容许集和Tube不变集RMPC,该方法不仅扩大了吸引域,而且降低了在线计算量;同时,采用基于最小鲁棒正不变集的Tube不变集策略保证了算法的鲁棒性.最后仿真结果验证了算法的有效性.     

一类离散不确定系统的Tube不变集离线鲁棒模型预测控制

针对一类输入和状态受限的离散线性不确定系统,提出了一种基于Tube不变集的离线鲁棒模型预测控制方法.首先针对输入和状态约束线性时不变标准系统,设计了改进的基于多面体不变集的离线模型预测控制算法,并证明了稳定性.其次对于存在未知有界干扰的实际不确定系统,引入了Tube不变集策略,通过设计对应标准模型的最优控制序列和状态轨迹,给出了实际不确定系统的离线Tube不变集控制策略,保证系统状态鲁棒渐近稳定,并收敛于终端干扰不变集.仿真结果验证了该控制方法的有效性.     

基于多面体不变集的变终端约束集RMPC

针对一类状态和输入受约束的多胞不确定线性时变系统,提出了一种基于多面体不变集的变终端约束集鲁棒模型预测控制算法.首先采用基于状态反馈增益的多面体不变集计算方法,给出了一种新的控制不变集序列构造方法,然后以控制不变集序列的并集作为终端约束集,结合在线优化和增益切换,实施变终端约束集双模鲁棒预测控制.该算法不仅有效地扩大了...     

约束分段仿射系统的鲁棒一步预测控制

针对一类具有附加有界扰动的离散时间约束分段仿射系统,提出了一种鲁棒低复杂性的模型预测控制方法,即鲁棒一步控制.首先,基于最大鲁棒正不变集,计算系统的最大鲁棒可稳定集并作为第一步预测状态的约束集,使得产生的滚动时域控制器可以在较小的预测时域内控制最大鲁棒可稳定集.然后,在最大鲁棒正不变集外,通过构建线性矩阵不等式来分析其鲁棒稳定性.两个步骤分别确保控制器的可行性和闭环系统的鲁棒稳定性.大量的数值例子表明,和已有的控制方法相比,所得的鲁棒一步控制器具有更低的复杂性.     

约束分段仿射系统的鲁棒最小时间控制

针对一类有界附加扰动的分段仿射系统,提出了一种离线低复杂性的鲁棒预测控制方法-鲁棒最小时间控制.首先计算系统的最大鲁棒正不变集和相关的局部稳定控制律,然后基于最大鲁棒正不变集通过多参数规划离线迭代计算系统的鲁棒一步集,得到的最小时间控制器覆盖最大鲁棒可稳定集.提出的鲁棒最小时间控制确保系统状态在最小时间内进入鲁棒正不变...     

一类时滞切换系统的鲁棒控制器设计

研究了一类具有外部干扰的线性时滞切换系统的鲁棒控制器的设计问题.利用完备性和Lyapunov函数方法设计出状态反馈的鲁棒控制器,并设计出切换策略,证明了闭环系统在给定的切换策略下在其平衡点处的渐近稳定性.仿真结果验证了结论的有效性.     

具有自适应噪声边界的Tube可达集鲁棒预测控制

针对过程噪声设定边界与真实噪声边界失配的有界干扰离散线性不确定系统,提出一种具有自适应噪声边界的Tube可达集鲁棒模型预测控制方法.首先,该算法引入基于MIT规则的自适应集员滤波在线估计系统状态和噪声边界.其次,基于估计值,通过迭代自适应集员滤波的时间更新部分计算出预测时域内闭环不确定系统状态的可达集.最后,用可达集代替不变集并根据Tube鲁棒模型预测控制策略,给出了实际不确定系统的控制律,确保系统状态鲁棒渐近稳定,并收敛于终端干扰不变集.仿真结果验证了该控制方法的有效性.     

非线性约束系统H∞鲁棒预测控制

针对一类具有状态约束和控制约束的离散非线性系统，本文采用有限维参数化方法提出了一种基于闭环优化的H∞鲁棒预测控制算法．这种算法把预测控制的滚动优化机制同微分对策理论和非线性H∞控制理论做了有机结合；在闭环优化中通过有限维参数化方法把控制变量参数化为多项式控制变量，并且引入被控系统的过渡平衡点．这样算法不但可以处理不确定系统，而且降低了在线闭环优化的计算复杂度．进一步，证明了算法的可行性和对有界不确定性系统的鲁棒稳定性．最后，用数值仿真验证了算法的有效性．     

A framework for multiple robust explicit MPC controllers for linear systems

This article presents a multi-mode explicit controller for constrained linear systems with bounded disturbances using a switching strategy based on Model Predictive Control (MPC). In the proposed approach, the system switches among several MPC controllers having different performance levels. The switching is done so as to achieve increasing levels of performance as time evolves, reaching the desired controller in finite time steps. The conditions needed for switching and robust convergence of the multi-mode MPC controllers are provided. Compared with standard robust explicit MPC implementations, the proposed approach has the flexibility of having a large domain of attraction, a good asymptotic behaviour and a small number of partitions.     

Distributed model predictive control of constrained weakly coupled nonlinear systems

This paper proposes a distributed model predictive control (MPC) strategy for a large-scale system that consists of several dynamically coupled nonlinear systems with decoupled control constraints and disturbances. In the proposed strategy, all subsystems compute their control signals by solving local optimizations constrained by their nominal decoupled dynamics. The dynamic couplings and the disturbances are accommodated through new robustness constraints in the local optimizations. The paper derives relationships among, and designs procedures for, the parameters involved in the proposed distributed MPC strategy based on the analysis of the recursive feasibility and the robust stability of the overall system. The paper shows that, for a given bound on the disturbances, the recursive feasibility is guaranteed if the sampling interval is properly chosen. Moreover, it establishes sufficient conditions for the overall system state to converge to a robust positively invariant set. The paper illustrates the effectiveness of the proposed distributed MPC strategy by applying it to three coupled cart-(nonlinear) spring–damper subsystems.     

Robust model predictive control for constrained continuous-time nonlinear systems

In this paper, a robust model predictive control (MPC) is designed for a class of constrained continuous-time nonlinear systems with bounded additive disturbances. The robust MPC consists of a nonlinear feedback control and a continuous-time model-based dual-mode MPC. The nonlinear feedback control guarantees the actual trajectory being contained in a tube centred at the nominal trajectory. The dual-mode MPC is designed to ensure asymptotic convergence of the nominal trajectory to zero. This paper extends current results on discrete-time model-based tube MPC and linear system model-based tube MPC to continuous-time nonlinear model-based tube MPC. The feasibility and robustness of the proposed robust MPC have been demonstrated by theoretical analysis and applications to a cart-damper springer system and a one-link robot manipulator.     

持续有界扰动下的非线性H∞鲁棒预测控制

针对未知但有界的持续扰动, 提出了一种约束非线性 H∞ 鲁棒预测控制策略. 首先, 引入离散系统的输入状态稳定性概念; 其次, 采用仿射输入定义预测控制的控制律, 并给出相应终端约束集的估计解法. 进一步, 得到预测控制闭环系统的鲁棒稳定性结论. 最后, 数值仿真验证了上述策略的有效性.     

Nominally Robust Model Predictive Control With State Constraints

In this paper, we present robust stability results for constrained discrete-time nonlinear systems using a finite-horizon model predictive control (MPC) algorithm for which we do not require the terminal cost to have any particular properties. We introduce a definition that attempts to characterize the robustness properties of the MPC optimization problem. We assume the systems under consideration satisfy this definition (for which we give sufficient conditions) and make two further assumptions. These are that the value function is bounded by a K_infin function of a state measure (related to the distance from the state to some target set) and that this measure is detectable from the stage cost used in the MPC algorithm. We show that these assumptions lead to stability that is robust to sufficiently small disturbances. While in general the results are semiglobal and practical, when the detectability and upper bound assumptions are satisfied with linear K_infin functions, the stability and robustness are either semiglobal or global (with respect to the feasible set). We discuss algorithms employing terminal inequality constraints and also provide a specific example of an algorithm that employs a terminal equality constraint.     

Tube-based robust economic model predictive control

In this paper, we develop a tube-based economic MPC framework for nonlinear systems subject to unknown but bounded disturbances. Instead of simply transferring the design procedure of tube-based stabilizing MPC to an economic MPC framework, we rather propose to consider the influence of the disturbance explicitly within the design of the MPC controller, which can lead to an improved closed-loop average performance. This will be done by using a specifically defined integral stage cost, which is the key feature of our proposed robust economic MPC algorithm. Furthermore, we show that the algorithm enjoys similar properties as a nominal economic MPC algorithm (i.e., without disturbances), in particular with respect to bounds on the asymptotic average performance of the resulting closed-loop system, as well as stability and optimal steady-state operation.