约束非线性车辆队列分布式多目标模型预测控制

针对具有状态和控制约束的非线性车辆队列系统多目标控制问题,提出一种分布式多目标模型预测控制(model predictive control, MPC)策略.首先,基于前车-后车单向通信拓扑,建立网联车辆队列非线性纵向巡航模型,应用字典序算法描述分布式多目标MPC问题;然后,通过设计弦稳定与收缩约束,并结合MPC三要素条件,保证车辆队列在经济性能与协同性能最优条件下的稳定性和弦稳定性结果;最后,通过典型工况的仿真结果验证所提出策略的有效性.     

网络信息模式下分布式系统协调预测控制

工业系统中广泛存在一类由多个相互关联的子系统组成的大系统. 尽管分布式控制结构的性能没有集中式控制好,但由于其具有较高的灵活性和容错性,相对于集中控制更加适合控制上述系统.在保持容错性的情况下如何提高系统的整体性能是分布式控制的一个难点问题.本文提出了一种分布式预测控制(Distributed model predictive control, DMPC)方法,该方法通过在各子系统预测控制器的性能指标中加入输入变量对其下游子系统的影响的二次函数,来扩大分布式预测控制的协调度,进而在不增加网络连通度,不改变系统容错性的前提下,提高系统的性能.另外,本文给出了基于该协调策略的带输入约束的分布式预测控制器的设计方法,在初始可行的前提下,该方法相继可行并可保证系统渐近稳定.     

基于2维性能参考模型的2维模型预测迭代学习控制策略

将迭代学习控制(Iterative learning control, ILC)系统看作一类具有2维动态特性的控制系统,根据模型预测控制(Model predictive control, MPC)和性能参考模型控制思想, 提出了一种基于2维性能参考模型的2维模型预测迭代学习控制系统设计方案.在该控制系统设计方案中,可以通过选择适当的2 维性能参考模型来构造2 维动态变化的设定值信号和预测控制信号,从而引导迭代学习控制系统收敛到合理的控制性能,并有效避 免系统性能收敛过程中控制输入可能发生的剧烈波动.通过对控制系统的结构分析可知,所得的迭代学习控制器本质上是由沿时 间指标的参考模型预测控制器和沿周期指标的迭代学习控制器组成,闭环系统的收敛性等价于一个2维滤波系统的稳定性.数值仿 真结果证明了该设计方案的有效性和鲁棒性.     

通讯信息约束下具有全局稳定性的分布式系统预测控制

本文针对一类由状态相互耦合的子系统组成的分布式系统, 提出了一种可以处理输入约束的保证稳定性的非 迭代协调分布式预测控制方法(distributed model predictive control, DMPC). 该方法中, 每个控制器在求解控制率时只与 其它控制器通信一次来满足系统对通信负荷限制; 同时, 通过优化全局性能指标来提高优化性能. 另外, 该方法在优化 问题中加入了一致性约束来限制关联子系统的估计状态与当前时刻更新的状态之间的偏差, 进而保证各子系统优化问 题初始可行时, 后续时刻相继可行. 在此基础上, 通过加入终端约束来保证闭环系统渐进稳定. 该方法能够在使用较少 的通信和计算负荷情况下, 提高系统优化性能. 即使对于强耦合系统同样能够保证优化问题的递推可行性和闭环系统的 渐进稳定性. 仿真结果验证了本文所提出方法的有效性.     

一类非线性系统非切换解析模型预测控制方法研究

针对关系度不确定非线性系统, 基于模型预测控制理论和切换解析非线性模型预测控制(Nonlinear model predictive control, NMPC) 提出了一种非切换的解析NMPC新方法. 论证了在非切换解析NMPC控制律下, 通过坐标变换可以将闭环系统分别在关系度确定和不确定的两个子空间近似为线性系统, 得出非切换解析NMPC使闭环系统稳定的必要条件. 通过仿真实验验证了非切换解析NMPC可以达到很好的响应特性, 无需切换的特征也扩大了其应用范围.     

基于事件触发的互联电力系统分布式负荷频率预测控制

针对具有约束和扰动的多区域互联电力系统负荷频率控制(load frequency control, LFC)问题,本文提出了一种事件触发分布式模型预测控制(event-triggered distributed model predictive control,ET-DMPC)策略.将大规模互联电力系统分解成多个动态耦合的子系统,考虑发电机变化率约束(generation rate constraint, GRC)和调速器阀门位置限制,建立分布式预测控制优化问题.为了降低系统计算负担,减少计算资源的消耗和浪费,基于预测值和系统实际状态的误差构造事件触发条件.在事件触发机制下,只有子系统满足相应的事件触发条件时,控制器才传输状态信息和求解优化问题,并与邻域子系统交互最优解作用下的关联信息.仿真结果表明,本文提出的控制策略在负荷扰动和系统参数不确定的情况下具有良好的鲁棒性,同时极大地降低了系统的计算负担.     

基于滑模预测控制的海底采矿车轨迹跟踪算法

海底采矿车多工作于稀软底质,其面临的外部扰动较大,难以快速收敛跟踪误差,精准地跟踪预设轨迹。为此,本文提出了一种海底采矿车的滑模预测控制（sliding model predictive control,SMPC）轨迹跟踪算法。基于海底采矿车的运动学模型,首先设计滑模控制率实现轨迹跟踪误差快速收敛,其次利用少预测时域的线性时变模型预测控制算法（linear time varying model predictive control,LTV-MPC）优化该滑模控制率。而后,通过证明滑模控制率收敛和模型预测控制稳定,保证了闭环控制系统的稳定性。RecurDyn&Simulink联合仿真结果表明,与单一的滑模控制（sliding mode control,SMC）和线性时变模型预测控制算法相比,所提出的SMPC轨迹跟踪算法提高了轨迹跟踪精度,且算法具有较好的实时性。     

基于纳什议价的无人机编队自主重构控制方法

针对任务环境下携带不同载荷的无人机(Unmanned aerial vehicles, UAVs)组成的编队, 为实现无人机间的相互支援和补充而进行编队重构控制, 运用多目标多人博弈理论, 将其转化为纳什谈判过程. 结合分布式模型预测控制(Distributed model predictive control, DMPC)方法, 设计一种基于纳什谈判的分布式预测控制(Nash bargaining solution-DMPC, NBS-DMPC)算法求解该问题, 并对算法收敛性进行了证明. 仿真实验表明, 该算法能够有效控制编队自主重构, 实现编队无人机间的威胁规避和协同保护, 同时能够有效降低无人机编队自主重构控制问题的求解规模.     

PI型自抗扰广义预测控制的性能分析

为克服自抗扰控制(active disturbance rejection control, ADRC)算法在大时滞系统中的局限性,减小PI型广义预测控制(PI-type generalized predictive control, PI-GPC)算法的在线计算量,我们在先前的研究中提出了PI型自抗扰广义预测控制(PI-type active disturbance rejection generalized predictive control, PI-ADRGPC)算法。本文通过频域分析方法,对PI-ADRGPC算法进行了稳定性分析,利用PI-ADRGPC算法离散形式的开环传递函数绘制其伯德图,分析了参数变化对PI-ADRGPC性能的影响。通过绘制奈奎斯特曲线,分析了PI-ADRGPC算法的稳定性。通过控制一阶惯性环节以及船舶航向控制系统验证了所提出算法的性能。研究结果表明:与ADRC-GPC算法相比,PI-ADRGPC算法的响应速度更快、控制效果更好。     

存在未知时滞非线性系统的迭代变区间预测迭代学习控制

本文针对机理模型未知的非线性非仿射多入多出(multiple-input and multiple-output,MIMO)离散时间系统, 研究了系统同时存在未知时滞和迭代变化运行时间区间的预测迭代学习控制(predictive iterative learning control,PILC)问题. 首先利用未知时滞的上下界信息建立了一种新型的动态线性化(dynamic linearization,DL)模型, 理论分析表明该模型能够等价描述本文所考虑的存在未知时滞的未知非线性系统. 同时, 设计一种新的数据补偿机制用以处理由于系统运行时间区间迭代变化而引起的数据丢失问题. 基于所建立的DL模型和数据补偿机制, 设计了能够同时处理未知时滞和迭代变化运行时间区间的预测迭代学习控制方法. 通过严格的理论分析同时给出了建模误差和跟踪控制误差的收敛性质. 最后, 通过仿真进一步验证了所提方法的有效性.     

Nonlinear predictive control of smooth nonlinear systems based on Volterra models. Application to a pilot plant

There is a large demand to apply nonlinear algorithms to control nonlinear systems. With algorithms considering the process nonlinearities, better control performance is expected in the whole operating range than with linear control algorithms. Three predictive control algorithms based on a Volterra model are considered. The iterative predictive control algorithm to solve the complete nonlinear problem uses the non‐autoregressive Volterra model calculated from the identified autoregressive Volterra model. Two algorithms for a reduced nonlinear optimization problem are considered for the unconstrained case, where an analytic control expression can be given. The performance of the three algorithms is analyzed and compared for reference signal tracking and disturbance rejection. The algorithms are applied and compared in simulation to control a Wiener model, and are used for real‐time control of a chemical pilot plant. Copyright © 2009 John Wiley & Sons, Ltd.     

一类串联生产过程的分布式解耦预测控制

研究一类由多个子过程串联而成的生产过程的预测控制，提出了在分布式通信模式下的预测控制算法．该算法由两部分组成：一部分利用其他子过程在上一时刻的控制策略，对子过程间的弱耦合进行前馈补偿；另一部分通过顺序求解对强耦合进行解耦控制．在线性无约束情况下，得到了分布式解耦动态矩阵控制律．最后通过仿真验证了算法的有效性．     

分布式预测控制算法的性能分析

分布式求解策略是为了降低大规模预测控制系统实施的计算量和计算复杂性而提出的一种有效算法，在算法收敛的条件下，分析了分布式求解和集中求解两种方法在单步时域上的性能偏差，给出了标称情况下分布式预测控制系统名义稳定的充分条件，为更好地理解所提出的分布式预测控制算法和算法的实施提供了理论依据。     

Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes

In this paper, two novel networked model predictive control schemes based on neighbourhood optimization are presented for on-line optimization and control of a class of serially connected processes (known as the cascade processes in some references), in which the on-line optimization of the whole system is decomposed into that of several small-scale subsystems in distributed structures. Under network environment, the connectivity of the communication network is assumed to be sufficient for each subsystem to exchange information with its neighbour subsystems. An iterative algorithm for networked MPC and a networked MPC algorithm with one-step delay communication are developed according to different network capacities. The optimality of the iteration based networked MPC algorithm is analyzed and the nominal stability is derived for unconstrained distributed control systems. The nominal stability with one-step delay communication is employed for distributed control systems without the inequality constraints. Finally, an illustrative example and the simulation study of the fuel feed flow control for the walking beam reheating furnace are provided to test the effectiveness and practicality of the proposed networked MPC algorithms.     

A design method for indirect iterative learning control based on two-dimensional generalized predictive control algorithm

Indirect iterative learning control (ILC) facilitates the application of learning-type control strategies to the repetitive/batch/periodic processes with local feedback control already. Based on the two-dimensional generalized predictive control (2D-GPC) algorithm, a new design method is proposed in this paper for an indirect ILC system which consists of a model predictive control (MPC) in the inner loop and a simple ILC in the outer loop. The major advantage of the proposed design method is realizing an integrated optimization for the parameters of existing feedback controller and design of a simple iterative learning controller, and then ensuring the optimal control performance of the whole system in sense of 2D-GPC. From the analysis of the control law, it is found that the proposed indirect ILC law can be directly obtained from a standard GPC law and the stability and convergence of the closed-loop control system can be analyzed by a simple criterion. It is an applicable and effective solution for the application of ILC scheme to the industry processes, which can be seen clearly from the numerical simulations as well as the comparisons with the other solutions.     

分散式MPC经济性能评估

近年来,学术界对集中式模型预测控制 (Model predictive control, MPC) 性能评估进行了广泛的研究. 对于大规模化工过程, 工业现场通常采用分散式MPC的控制结构. 由于各子系统间存在复杂的耦合关系, 针对集中式MPC 的性能评估方法不能客观反映分散式MPC的性能. 本文基于线性矩阵不等式(Linear matrix inequality, LMI)的方法对分散式MPC进行经济性能评估. 首先提出了一种迭代方法求解分散式线性二次型调节器(Linear quadratic regulator, LQR)问题, 该方法显著降低了已有求解方法的保守性. 再利用LQR基准建立了一组随机优化命题对MPC进行经济性 能评估, 评估方法对集中式MPC与分散式MPC均适用, 评估结果可以指导MPC参数调整, 也可以为集中式与分散式MPC结构选择提供重要参考. 通过对重油分馏塔控制问题的仿真验证了本文方法的有效性与应用价值.     

Distributed stochastic MPC for systems with parameter uncertainty and disturbances

A distributed stochastic model predictive control algorithm is proposed for multiple linear subsystems with both parameter uncertainty and stochastic disturbances, which are coupled via probabilistic constraints. To handle the probabilistic constraints, the system dynamics is first decomposed into a nominal part and an uncertain part. The uncertain part is further divided into 2 parts: the first one is constrained to lie in probabilistic tubes that are calculated offline through the use of the probabilistic information on disturbances, whereas the second one is constrained to lie in polytopic tubes whose volumes are optimized online and whose facets' orientations are determined offline. By permitting a single subsystem to optimize at each time step, the probabilistic constraints are then reduced into a set of linear deterministic constraints, and the online optimization problem is transformed into a convex optimization problem that can be performed efficiently. Furthermore, compared to a centralized control scheme, the distributed stochastic model predictive control algorithm only requires message transmissions when a subsystem is optimized, thereby offering greater flexibility in communication. By designing a tailored invariant terminal set for each subsystem, the proposed algorithm can achieve recursive feasibility, which, in turn, ensures closed‐loop stability of the entire system. A numerical example is given to illustrate the efficacy of the algorithm.