一种间歇测量下产品质量轨迹跟踪控制方法

在间歇过程中,传统工艺缺少产品质量的在线传感器,因此很难直接跟踪产品质量轨迹。基于过程变量与产品质量之间的偏最小二乘(PLS)模型,提出一种间歇测量下产品质量轨迹跟踪控制方法。在批内设定时间窗口,首先利用缺失数据算法估计未知的过程变量,再根据过程变量预测产品质量,并采用迭代学习控制和模型预测控制(MPC)相结合的方法设计控制器,达到跟踪当前批次产品质量设定点的目的。该方法采用移动窗口实现MPC的滚动优化,克服了批间和批内的干扰,从而使输出轨迹能精确跟踪期望轨迹。最后,在连续搅拌反应釜(CSTR)上进行仿真研究,仿真结果说明了该方法的有效性。     

约束不确定系统的误差有界无偏跟踪

为使精密制造设备中定位机构实现在容许的误差带内对时变轨迹的精准无偏跟踪,针对约束不确定系统提出一种基于鲁棒控制不变(RCI)集的误差有界且无偏模型预测控制(EOMPC)方法.首先,为了消除由扰动引起的稳态误差,构建包含可测输出、估计状态和估计扰动的增广系统作为EOMPC的预测模型以提高预测精度;其次,基于增广系统和轨迹模型,使用可以在有限步内终止的迭代方法求解RCI集,并将其作为最优控制问题(OCP)中的状态约束以实现误差有界的跟踪;然后,为保障OCP的实时性,给出一种可在线执行的数值优化方案;最后,在磁悬浮定位系统上验证了所提出方法的有效性.     

基于等价输入干扰滑模观测器的磁悬浮球系统模型预测控制

本文针对系统不确定性和外部干扰引起的磁悬浮球系统控制性能下降的问题,提出了一种基于等价输入干扰滑模观测器的模型预测控制(MPC+EIDSMO)方法.首先将原系统转化为EID系统,采用等价输入干扰滑模观测器对EID系统状态变量及等价输入干扰进行估计;然后基于状态估计值设计模型预测控制器,并将等价输入干扰估计值以前馈的方式补偿后得到最终的复合控制律,实现对参考位置跟踪的快速性,准确性以及对总扰动的鲁棒性.值得注意的是,与传统EID结构中的龙伯格观测器相比,等价输入干扰滑模观测器中增加的非线性观测误差反馈项有助于提高状态估计的快速性和精确性.从理论上证明了该系统是全局一致毕竟有界的.仿真和实验结果表明,相较于基于EID观测器的模型预测控制方法和基于龙伯格观测器的积分模型预测控制方法,所提方法提高了磁悬浮球系统的跟踪性能,并且有效的抑制了系统不确定性和外部干扰.     

基于自适应MPC算法的轨迹跟踪控制研究

为了保证智能车辆在低附着且变速条件下跟踪控制的精确性和稳定性,提出一种基于自适应模型预测控制（MPC）的轨迹跟踪控制算法。针对低附着条件下轨迹跟踪存在行驶稳定性较差的问题,对车辆动力学模型添加侧偏角软约束,分别设计有无添加侧偏角约束的MPC控制器。仿真结果表明,添加侧偏角约束后MPC控制器性能更优,车辆行驶稳定性得到有效提高。在此基础上,又提出了一种自适应的轨迹跟踪控制策略,能够根据车辆速度的变化,实时产生预测时域[(Hp)],分别设计自适应的MPC控制器与4组定值[Hp]的MPC控制器。仿真结果表明,基于自适应模型预测控制的轨迹跟踪控制算法在提高低附着且变速条件下智能车辆轨迹跟踪控制的精度和稳定性方面具有一定的有效性和先进性。     

基于反馈线性化的永磁同步电机模型预测控制

提出一种基于反馈线性化和模型预测控制(MPC)策略的永磁同步电机(PMSM)控制方案.运用微分几何理论讨论了非线性PMSM模型可进行反馈线性化的充分必要条件,并将其转换为新坐标空间中的线性模型;分析了MPC原理和对系统约束条件的处理方法.针对获得的PMSM线性模型,分别采用MPC和状态反馈极点配置方法设计了控制器.在有...     

多工况以及多目标优化的自适应巡航系统

为提高自适应巡航系统(Adaptive Cruise Control,ACC)的综合性能,通过对跟驰性能、安全性、燃油经济性以及乘客舒适性进行分析,作为系统的控制约束,并引入了基于驾驶数据的车头时距.采用了分层控制的架构,并基于模型预测控制理论(Model Predictive Control,MPC)设计了上层控制器.提出了一种可以根据当前行驶工况来对目标函数中的权重进行实时再分配的策略(Dynamic Weight Adjustment Strategy,DWAS),来解决传统固定权重在多工况下表现差的情况.实车实验表明,在复杂的多个工况下,所提出的权重可变的MPC控制器在保证跟驰性能和安全性的前提下,提高了燃油经济性和舒适性.     

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.     

基于随机模型预测控制的四旋翼无人机研究

由于随机扰动(可能无界)的存在，四旋翼无人机系统存在控制性能不稳定、输入抖动大的问题。针对这种情况，提出一种随机模型预测控制算法。算法对控制约束进行软化，使其在一定范围内可以违反约束以满足系统性能的提高，并添加扰动反馈结构进行实时监测。运用分布式鲁棒的方法对扰动进行重构，扰动重构后再分别对单输入机会约束与联合状态机会约束进行可计算的精确凸优化重构。加入精确罚函数方法以进一步保证系统可行性。实验对比结果表明，与无扰动的模型预测控制(MPC)算法相比，该算法能够在满足约束的情况下克服任意随机扰动(可能无界)完成目标跟踪，且具备更优秀的预测性能。     

无人驾驶飞行器姿态的非线性扰动抑制控制

本文研究了无人驾驶飞行器(unmanned aerial vehicle,UAV)的姿态跟踪控制问题.针对在飞行器姿态跟踪时存在的系统模型不确定性和外界扰动,提出了一种基于四元数的姿态跟踪控制方法,基于UAV的姿态误差模型分别设计系统的观测器和控制器.首先,以四元数为姿态参数建立系统的非线性误差模型;在此基础之上,设计一种非线性干扰观测器(nonlinear disturbance observer,NDOB)用以在线估计误差模型中的复合扰动,并在控制输入端进行相应的补偿.然后通过设计非线性广义预测控制律设镇定误差系统,实现姿态跟踪.最后基于频域理论分析了非线性干扰观测器的扰动抑制性能.仿真与实验结果表明本文提出的方法在系统存在复合扰动的情况下能使系统姿态有效的跟踪期望值.     

含间隙机械系统的混杂模型预测控制器

研究含间隙机械系统的混杂模型预测控制问题.首先,将含间隙机械系统的运行模式分为"间隙模式"和"接触模式".其次,建立了含间隙机械系统的混杂分段仿射 (PWA)模型.然后,利用模型预测控制 (MPC)的方法对约束PWA系统的最优控制进行求解,通过动态规划与多参数二次规划方法,得到了MPC的离线解.最后,通过将分段二次 (PWQ)Lyapunov函数的求解转换成半正定规划,找到了确保闭环控制稳定性的PWQ Lyaplanov函数.跟踪参考速度的实验结果表明,混杂模型预测控制器对含间隙机械系统的跟踪控制具有较好的效果,能够满足小采样时间系统的实时控制要求.     

Linear offset-free Model Predictive Control

This work addresses the problem of offset-free Model Predictive Control (MPC) when tracking an asymptotically constant reference. In the first part, compact and intuitive conditions for offset-free MPC control are introduced by using the arguments of the internal model principle. In the second part, we study the case where the number of measured variables is larger than the number of tracked variables. The plant model is augmented only by as many states as there are tracked variables, and an algorithm which guarantees offset-free tracking is presented. In the last part, offset-free tracking properties for special implementations of MPC schemes are briefly discussed.     

Tuning SISO offset-free Model Predictive Control based on ARX models

In this paper, we present a tuning methodology for a simple offset-free SISO Model Predictive Controller (MPC) based on autoregressive models with exogenous inputs (ARX models). ARX models simplify system identification as they can be identified from data using convex optimization. Furthermore, the proposed controller is simple to tune as it has only one free tuning parameter. These two features are advantageous in predictive process control as they simplify industrial commissioning of MPC. Disturbance rejection and offset-free control is important in industrial process control. To achieve offset-free control in face of unknown disturbances or model-plant mismatch, integrators must be introduced in either the estimator or the regulator. Traditionally, offset-free control is achieved using Brownian disturbance models in the estimator. In this paper we achieve offset-free control by extending the noise model with a filter containing an integrator. This filter is a first order ARMA model. By simulation and analysis, we argue that it is independent of the parameterization of the underlying linear plant; while the tuning of traditional disturbance models is system dependent. Using this insight, we present MPC for SISO systems based on ARX models combined with the first order filter. We derive expressions for the closed-loop variance of the unconstrained MPC based on a state space representation in innovation form and use these expressions to develop a tuning procedure for the regulator. We establish formal equivalence between GPC and state space based off-set free MPC. By simulation we demonstrate this procedure for a third order system. The offset-free ARX MPC demonstrates satisfactory set point tracking and rejection of an unmeasured step disturbance for a simulated furnace with a long time delay.     

Disturbance modeling for offset-free linear model predictive control

An offset-free controller is one that drives controlled outputs to their desired targets at steady state. In the linear model predictive control (MPC) framework, offset-free control is usually achieved by adding step disturbances to the process model. The most widely-used industrial MPC implementations assume a constant output disturbance that can lead to sluggish rejection of disturbances that enter the process elsewhere. This paper presents a general disturbance model that accommodates unmeasured disturbances entering through the process input, state, or output. Conditions that guarantee detectability of the augmented system model are provided, and a steady-state target calculation is constructed to remove the effects of estimated disturbances. Conditions for which offset-free control is possible are stated for the combined estimator, steady-state target calculation, and dynamic controller. Simulation examples are provided to illustrate trade-offs in disturbance model design.     

Constrained nonlinear MPC for accelerated tracking piece-wise references and its applications to thermal systems

In this paper, we propose a model predictive control (MPC) strategy for accelerated offset-free tracking piece-wise constant reference signals of nonlinear systems subject to state and control constraints. Some special contractive constraints on tracking errors and terminal constraints are embedded into the tracking nonlinear MPC formulation. Then, recursive feasibility and closed-loop convergence of the tracking MPC are guaranteed in the presence of piece-wise references and constraints by deriving some sufficient conditions. Moreover, the local optimality of the tracking MPC is achieved for unreachable output reference signals. By comparing to traditional tracking MPC, the simulation experiment of a thermal system is used to demonstrate the acceleration ability and the effectiveness of the tracking MPC scheme proposed here.     

A new robust model predictive control method I: theory and computation

The success of the single-model MPC (SMPC) controller depends on the accuracy of the process model. Modeling errors cause sub-optimal control performance and may cause the control system to become closed-loop unstable. The goal of this paper is to examine the control performance of the robust MPC (RMPC) method proposed by Wang and Rawlings [34] on several illustrative examples. In this paper, we show the RMPC method successfully controls systems with time-varying uncertainties in the process gain, time constant and time delay and achieves offset-free non-zero set point tracking and non-zero disturbance rejection subject to input and output constraints.     

New offset-free method for model predictive control of open channels

Irrigation or drainage canals can be controlled by model predictive control (MPC). Applying MPC with an internal model in the presence of unknown disturbances in some cases can lead to steady state offset. Therefore an additional component should be implemented along with the MPC. A new method eliminating the offset has been developed in this paper for MPC. It is based on combining two basic approaches of MPC. It has been implemented to control water levels in the three-pool UPC laboratory canal and further numerically tested using a test case benchmark proposed by the American Society of Civil Engineers (ASCE). It has been found that the developed offset-free method is able to eliminate the steady-state offset, while taking into account known and unknown disturbances.     

Robust disturbance modeling for model predictive control with application to multivariable ill-conditioned processes

In this paper the disturbance model, used by MPC algorithms to achieve offset-free control, is optimally designed to enhance the robustness of single-model predictive controllers. The proposed methodology requires the off-line solution of a min-max optimization problem in which the disturbance model is chosen to guarantee the best closed-loop performance in the worst case of plant in a given uncertainty region. Application to a well-known ill-conditioned distillation column is presented to show that, for ill-conditioned processes, the use of a disturbance model that adds the correction term to the process inputs guarantees a robust performance, while the disturbance model that adds the correction term to the process outputs (used by industrial MPC algorithms) does not.     

Offset-free model predictive control of a vapor compression cycle

This work describes the application of an offset-free model predictive controller (OF-MPC) to a vapor compression cycle (VCC). To this end, first a linear model is identified from data generated from a first-principle model of a VCC, interfaced with a building model implemented in EnergyPlus. Next, a model predictive controller is designed that includes an augmented model (including disturbance states) and an associated Luenberger observer to estimate the disturbance (plant-model mismatch) at steady state and eliminate it. Tuning guidelines are presented that enable partial ‘decoupling’ of the Luenberger design and choice of MPC parameters, leading to offset elimination and superior tracking performance while reducing the energy demand on the VCC, relative to traditional control approaches. The superior closed-loop performance of the OF-MPC strategy is demonstrated for a VCC model in isolation, subject to realistic disturbances and subject to measurement noise.     

Multiplexed model predictive control

This paper proposes a form of MPC in which the control variables are moved asynchronously. This contrasts with most MIMO control schemes, which assume that all variables are updated simultaneously. MPC outperforms other control strategies through its ability to deal with constraints. This requires on-line optimization, hence computational complexity can become an issue when applying MPC to complex systems with fast response times. The Multiplexed MPC (MMPC) scheme described in this paper solves the MPC problem for each subsystem sequentially, and updates subsystem controls as soon as the solution is available, thus distributing the control moves over a complete update cycle. The resulting computational speed-up allows faster response to disturbances, which may result in improved performance, despite finding sub-optimal solutions to the original problem. This paper describes nominal and robust MMPC, states some stability results, and demonstrates the effectiveness of MMPC through two examples.