Model predictive control of an industrial packed bed reactor using neural networks

This paper discusses an industrial application of a multivariable nonlinear feedforward/feedback model predictive control where the model is given by a dynamic neural network. A multi-pass packed bed reactor temperature profile is modelled via recurrent neural networks using the backpropagation through time training algorithm. This model is then used in conjunction with an optimizer to build a nonlinear model predictive controller. Results show that, compared with conventional control schemes, the neural network model based controller can achieve tighter temperature control for disturbance rejection     

螺旋桨负载平衡装置控制系统设计及仿真

以一螺旋桨负载平衡装置为研究对象,通过对该装置在俯仰及偏转位置角度的控制,以达到对该装置方位控制和负载力平衡的目标。基于该系统强的耦合特性,通过状态反馈策略实现对系统解耦,并结合位置控制,实现该装置方位和俯仰角度的精确控制。同时,为了应对负载突变扰动对该装置位置及姿态影响,一个干扰观测器被设计出以用于实时观测外部干扰推力大小,最后,通过前馈补偿的方式提高该装置对干扰抑制的响应效果。通过系统动态模型建立、解耦设计、位置控制设计、扰动观察器设计和基于该观察输出的前馈干扰抑制补偿设计等研究,实现了当外载荷突变时的扰动抑制及姿态控制。研究结果表明引入干扰观测器及前馈补偿调节控制器时的扰动抑制能力得以增强。这对于基于该装置的平台的稳定性提高有着极大的工程应用价值。     

Dynamics and control of benzene hydrogenation via reactive distillation

This work develops a dynamic, first principles-based model of a reactive distillation column used for benzene hydrogenation of a reformate stream and investigates different control structures for this process. The model is used initially to develop and evaluate a feedback control strategy which provides good regulatory performance for small disturbances, however, it tends to be sluggish for significant disturbances in the feed composition. In order to address this point, adding a feedforward controller to the feedback structure has also been investigated. However, the feedforward controller can only be implemented if composition measurements of the feed are taken. As online composition measurements are expensive in practice, several different scenarios have been investigated where samples of the feed are taken and subsequently analyzed in a lab, as represented by measurement time delays. Simulation results show that adding feedforward control to the feedback scheme can be very beneficial for this process, however, this is only the case if the composition disturbance measurements do not involve a significant time delay.     

六自由度机械臂约束预测控制系统的设计

本文提出了一种基于约束预测控制的机械臂实时运动控制方法.该控制方法分为两层,分别设计了约束预测控制器和跟踪控制器.其中,约束预测控制器在考虑系统物理约束的条件下,在线为跟踪控制器生成参考轨迹;跟踪控制器采用最优反馈控制律,使机械臂沿参考轨迹运动.为了简化控制器的设计和在线求解,本文采用输入输出线性化的方式简化机械臂动力学模型.同时,为了克服扰动,在约束预测控制器中引入前馈策略,提出了带前馈一反馈控制结构的预测控制设计.因此,本文设计的控制器可以使机械臂在满足物理约束的条件下快速稳定地跟踪到目标位置.通过在PUMA560机理模型上进行仿真实验,验证了预测控制算法的可行性和有效性.     

Self-tuning of 2 DOF control based on evolving fuzzy model

In this paper we present a self-tuning of two degrees-of-freedom control algorithm that is designed for use on a non-linear single-input single-output system. The control algorithm is developed based on the Takagi-Sugeno fuzzy model, and it consists of two loops: a feedforward loop and feedback loop. The feedforward part of the controller should drive the system output to the vicinity of the reference signal. It is developed from the inversion of the T-S fuzzy model. To achieve accurate error-free reference tracking a feedback part of the controller is added. A time-varying error-model predictive controller is used in the feedback loop. The error-model is obtained from the T-S fuzzy model. The T-S fuzzy model of the system, required in the controller, is obtained with evolving fuzzy modelling, which is based on recursive Gustafson-Kessel clustering algorithm and recursive fuzzy least squares. It employs evolving mechanisms for adding, removing, merging and splitting the clusters.The presented control approach was experimentally validated on a non-linear second-order SISO system helio-crane in simulation and real environment. Several criteria functions were defined to evaluate the reference-tracking and disturbance rejection performance of the control algorithm. The presented control approach was compared to another fuzzy control algorithm. The experimental results confirm the applicability of the approach.     

A nonlinear state‐feedback state‐feedforward tracking control strategy for a boiler‐turbine unit

A boiler‐turbine unit is a primary module for coal‐fired power plants, and an effective automatic control system is needed for the boiler‐turbine unit to track the load changes with the drum water level kept within an acceptable range. The aim of this paper is to develop a nonlinear tracking controller for the Bell‐Åström boiler‐turbine unit. A Takagi‐Sugeno fuzzy control system is introduced for the nonlinear modeling of the Bell‐Åström boiler‐turbine unit. Based on the Takagi‐Sugeno fuzzy models, a nonlinear tracking controller is developed, and the proposed control law is comprised of a state‐feedforward term and a state‐feedback term. The stability of the closed‐loop control system is analyzed on the basis of Lyapunov stability theory via the linear matrix inequality approach and Schur complement. Moreover, model uncertainties are also considered, and it is proved that with the proposed control law the tracking error converges to zero. To assess the performance of the proposed nonlinear state‐feedback state‐feedforward control strategy, a nonlinear model predictive control strategy and a linear strategy are presented as comparisons. The effectiveness and the advantages of the proposed nonlinear state‐feedback state‐feedforward control strategy are demonstrated by simulations.     

Multi-objective model-based control for an automotive catalyst

A model-based feedforward/feedback air fuel ratio controller that optimizes the oxygen storage capacity of the three-way catalyst in automotive emission control systems is presented. This work incorporates a simplified dynamic catalyst model that describes the physical behavior of oxygen chemisorption and reversible deactivation in the catalyst system. A novel aspect of this work is the use of the oxygen storage capability of the catalyst not only to minimize vehicle emissions but also to optimize engine performance and fuel economy during transient engine demand. The feedback/feedforward controller is a nonlinear model predictive controller that incorporates catalyst, engine air fuel ratio controller, and fuel system models to determine the optimal air fuel ratio target trajectory. Feedback is provided by a nonlinear moving horizon estimation strategy for the determination of the oxygen storage level of the catalyst based on air fuel ratio sensors.     

Two-degree-of-freedom H-infinity control of combustion in diesel engine using a discrete dynamics model

This paper proposes an H-infinity combustion control method for diesel engines. The plant model is the discrete dynamics model developed by Yasuda et al., which is implementable on a real engine control unit. We introduce a two-degree-of-freedom control scheme with a feedback controller and a feedforward controller. This scheme achieves both good feedback properties, such as disturbance suppression and robust stability, and a good transient response. The feedforward controller is designed by taking the inverse of the static plant model, and the feedback controller is designed by the H-infinity control method, which reduces the effect of the trubocharger lag. The effectiveness of the proposed method is evaluated in simulations using the nonlinear discrete dynamics model.     

含正反馈振动主动控制系统的混合自适应控制

对于存在结构正反馈的振动主动控制系统,传统的基于有限冲击响应的自适应前馈控制器设计方法难以同时保证控制系统稳定与良好的控制性能.本文在分析正反馈对前馈控制系统影响的基础上,基于无限冲击响应控制器设计模式,提出一种结合前馈自适应控制器和反馈自适应控制器的混合自适应振动主动控制方法.其中前馈自适应控制器采用参考传感器采集到的扰动相关信号作为参考信号,反馈自适应控制器通过构建扰动的估计量作为参考信号,控制器参数更新采用Landau参数递推算法.以一典型的具有固有正反馈性质的机械振动系统为控制对象,给出了该混合自适应控制算法的详细推导过程以及稳定性和收敛性分析过程,得到了算法稳定与收敛的严格正实条件以及相应放松严格正实条件的要求.在此基础上,通过构建实时振动主动控制实验平台,针对多种振动扰动开展对比实验分析.相关实验结果验证了本文提出的混合自适应振动主动控制方法的可行性和有效性.     

基于扩张状态观测器的分散模型预测控制

针对复杂关联系统中分散控制方法无法有效解决子系统间的耦合和干扰问题, 提出一种基于扩张状态观测器的分散模型预测控制算法. 首先将复杂关联系统分解为多个状态维数较低、控制变量较少的子系统, 并为每个子系统设计本地预测控制器; 然后, 采用扩张状态观测器对子系统的耦合项以及干扰项进行估计, 进而利用估计值对子系统进行前馈补偿, 从而降低复杂关联系统的计算复杂度, 提高系统的稳定性和抗干扰能力; 最后, 利用液位控制系统验证了所提出算法的有效性.

     

Rule-based supervisory control of a two-link flexible manipulator

In this paper, we propose a two level hierarchical control strategy to achieve accurate end-point position of a planar two-link flexible manipulator. The upper level consists of a feedforward rule-based supervisory controller that incorporates fuzzy logic, whereas the lower level consists of conventional controllers that combine shaft position-endpoint acceleration feedback for disturbance rejection properties and shaping of the (joint) actuator inputs to minimize the energy transferred to the flexible modes during commanded movements. The effectiveness of this hierarchical control strategy is verified by experimental results for various movements of the links, in various configurations. In particular, we illustrate how the hierarchical intelligent control strategy performs better than conventional control techniques for endpoint position control in the presence of flexure effects.     

Feedforward Control for Wind Turbine Load Reduction with Pseudo-LIDAR Measurement

A gain-scheduled feedforward controller, based on pseudo-LIDAR (light detection and ranging) wind speed measurement, is designed to augment the baseline feedback controller for wind turbine’s load reduction in above rated operation. The pseudo-LIDAR measurement data are generated from a commercial software – Bladed using a designed sampling strategy. The nonlinear wind turbine model has been simplified and linearised at a set of equilibrium operating points. The feedforward controller is firstly developed based on a linearised model at an above rated wind speed, and then expanded to the full above rated operational envelope by employing gain scheduling strategy. The combined feedforward and baseline feedback control is simulated on a 5 MW industrial wind turbine model. Simulation studies demonstrate that the proposed control strategy can improve the rotor and tower load reduction performance for large wind turbines.     

容错逆变器驱动的PMSM双环预测控制的研究

为了提高三相四开关容错逆变器驱动的永磁同步电机调速系统的性能,提出了调速系统双闭环预测控制策略。在转速环中设计了带扰动补偿的模型预测控制方法,通过离散扰动观测器估计负载扰动并进行前馈补偿,与模型预测速度控制相结合得到[q]轴电流环的期望给定值。在电流环中设计了基于离散滑模的有限控制集模型预测控制方法。通过与传统PI、FCS-MPC方法进行仿真对比,验证了该方法在空载启动、给定转速突变以及存在负载扰动、参数变化时,可使容错逆变器驱动的永磁同步电机调速系统具有更好的动态响应能力和鲁棒性。     

Disturbance compensation incorporated in predictive control system using a repetitive learning approach

In this paper, a disturbance compensation scheme is incorporated into a predictive control scheme using a repetitive learning approach. It has the following contributions. First, based on the assumption of the presence of both state and output disturbances, a predictive control algorithm is derived. Secondly, to estimate the disturbances, two feedforward disturbance learning schemes are proposed. Thirdly, the rigid mathematic proof is given to guarantee the convergence of the tracking error under the proposed disturbance learning laws used in conjunction with the predictive controller formulated. Finally, simulation results are provided to illustrate the good performance achievable by the proposed control law.     

线性系统的动态输出反馈最优扰动抑制

首先通过求解Riccati方程和Sylvester方程,推导出前馈反馈最优扰动抑制控制律．然后构建了能同时预估状态和扰动的降维观测器,解决了前馈控制的物理不可实现问题．进而结合降维观测器和前馈反馈最优控制律,提出了一种动态输出反馈扰动抑制控制器的设计算法．最后通过仿真实例表明本文提出的控制算法的可行性．     

具有对负荷变化前馈补偿的过热汽温系统预测函数控制

提出了具有对负荷变化前馈补偿的过热汽温系统预测函数控制策略，控制算法简单，易于工程实现，具有很高的实际应用价值。仿真结果表明，采用该新型控制策略的系统的动态品质明显优于采用PID串级控制策略的系统，而且系统克服负荷扰动的能力比采用PID串联控制策略和未加对负荷变化前馈补偿的PFC－PID串联控制策略的系统有了明显的提高。     

一种新的非线性离散时间系统迭代学习控制的前馈－反馈机制

This paper presents a new feedback-feedforward configuration for the iterative learning control (ILC) design withfeedback, which consists of a feedback and a feedforward component. The feedback integral controller stabilizes the system,and takes the dominant role during the operation, and the feed-forward ILC compensates for the repeatable nonlinear/unknowntime-varying dynamics and disturbances, thereby enhancing the performance achieved by feedback control alone. As the mostfavorable point of this control strategy, the feedforward ILC and the feedback control can work either independently or jointlywithout making efforts to recongurate or retune the feedforward/feedback gains. With rigorous analysis, the proposedlearning control scheme guarantees the asymptotic convergences along the iteration axis.     

基于反馈控制的迭代学习控制器设计

针对具有不确定项或干扰项的重复非线性时变系统,提出了基于反馈控制的迭代学习控制器,其中迭代学习控制器设计为高阶PD型,它以前馈的形式作用于对象,在满足一定的收敛性条件下,证明了该控制器的跟踪误差界是系统初始状态误差界和系统输出干扰项界的线性函数,同时改变反馈增益可以调整系统的最终跟踪误差界,仿真与实验均表明了该方法的有效性。     

基于DSP前馈模糊PI的舵机控制算法

针对负载转矩对舵机转速的影响,建立负载力矩的数学模型,提出带前馈模糊自整定比例积分参数的速度环控制算法,利用前馈控制器补偿负载变化对电机转速的影响。当直流无刷电机特性和力矩变化时,通过动态调整控制参数使系统具有良好的鲁棒性和控制精度。设计以数字信号处理器为核心的模糊控制器,采用高精度的绝对编码器作为位置环反馈。实验结果表明,该控制器具有较强鲁棒性,且响应速度快、超调小、控制精度高。     

基于高阶滑模速度控制器的异步电机模型预测转矩控制

针对三相异步电机驱动系统,提出一种基于高阶滑模速度控制器的模型预测转矩控制策略.为了降低负载扰动对系统运行性能的影响,设计一种基于二阶Super-Twisting滑模技术的速度环控制器,以代替传统PI速度控制器,并应用Lyapunov稳定性理论对其稳定性和鲁棒性进行分析,得到使速度控制系统收敛的参数范围.为了提升转矩控制精度,基于异步电机的数学模型,采用模型预测转矩控制理论,以转矩和定子磁链为控制目标设计评价函数,得到最优输出电压矢量驱动电机运行.仿真结果表明,所提出的控制方法能有效提升系统对负载扰动的鲁棒性,并有效降低转矩波动,使电机具有良好的动态和静态运行性能.