纸机模型的预测函数控制

造纸过程是一个多变量、强耦合、大时滞的过程,采用传统的PID控制要达到很好的控制效果是很困难的。采用三种预测控制方法分别对纸机模型进行控制,并分别进行了仿真研究。从仿真结果可以看出,动态矩阵的跟踪效果不如广义预测控制,广义预测控制算法的跟踪性能较好,但是计算量较大,预测函数控制的响应速度较快,计算简单,控制效果也较好。     

基于蚁群算法优化的再热汽温系统变参数预测PID控制

研究预测控制和PID控制在再热汽温系统控制中的应用.通过将神经网络作为预测模型,并用蚁群算法在线优化PID控制器参数.计算机仿真结果表明,基于蚁群算法的预测PID控制能够适应控制对象模型参数的时变,具有较好的鲁棒性,相对传统PID控制策略还表现出了良好的动态性能.     

模型预测控制在MACS_V6.51_DCS中的应用

实例给出MACS_V6.51_DCS系统运用模型预测控制技术进行稳定控制的新颖解决方案,解决了工业控制系统中涉及的用模型预测控制技术替代常规PID控制方法的技术要点。     

基于软测量的SBR污水处理自控系统的设计与实现

针对SBR污水处理工艺中控制系统不能有效控制除磷剂投加量的问题,设计了基于软测量的SBR污水处理自控系统。通过对水质参数的分析,确定适合的辅助变量,利用GA-BP神经网络软测量技术实现对总磷含量的实时预测,基于软测量的SBR污水处理控制系统以预测总磷含量作为系统反馈值,实现对除磷剂投加量的闭环控制。实验结果表明:该系统可以减少除磷剂的投加量,避免了过量投加试剂的危害,使SBR工艺稳定运行。     

基于在线子空间辨识的自适应预测控制

针对实际工业工程中存在非线性、时变的特点,提出一种基于子空间辨识的自适应预测控制方法。利用滚动窗口在线更新R阵,得到新的预测模型参数矩阵,通过比较更新前和更新后的预测误差来决定是否更新预测模型。将此控制方法应用于2-CSTR过程控制的仿真试验,通过与自适应模糊控制、PID控制器的比较,说明了该方法的优越性。     

LabVIEW和Matlab混合编程的预测PID控制器研究

针对现代工业过程中普遍对控制系统稳定性、小超调和快速性的要求,在经典PID控制的基础上,介绍了对模型要求不高、鲁棒性可调的预测控制算法。充分利用预测控制的预测功能和PID控制的优势,对较复杂的DMC-PID控制算法,本文采用LabVIEW中的Matlab Script Node模块将Matlab语言实现的预测控制器嵌入到LabVIEW流程图中,用混合编程的方法,最后在网络化测控试验平台上进行了实验。实验结果表明了该算法对抑制超调与改善系统的稳定具有一定效果。     

氯乙酸生产结晶过程的预测函数控制仿真研究

针对氯乙酸生产结晶过程被控对象大滞后、大惯性的特点,设计了基于PFC—PID的串级控制系统：该系统将串级控制结构和预测函数控制算法相结合,内回路采用常规PID控制器,外回路采用预测函数控制(PFC)。结合了2者的优点,仿真结果表明,该串级控制系统在控制品质、鲁棒性等方面明显优于常规PID控制系统。     

先进控制在熟料冷却系统中的应用

首先介绍了预测控制算法的发展及特点，然后分析了篦冷机的主要性能及工作原理。根据篦冷机的工作特性．运用灰色系统理论建立控制模型，采用预测控制算法对篦冷机进行在线控制，实际运行效果证明：篦冷机进料不稳定时，预测控制算法优于PID控制算法。     

模糊预测控制在工业窑炉温度控制系统中的应用

本文针对工业窑炉容量大、滞后量大、非线性等特点，提出了一种将模糊控制与预测控制相结合的控制算法。仿真结果表明，这种方法与传统的PID控制相比，具有较高的稳态精度和动态特性。     

氨合成塔温度的预测自动化控制及优化

目前国内对于氨合成塔温度控制多采用PID控制、串级控制、前馈一反馈控制等方法，这些方法虽然实现简单，但难以克服氨合成塔温度控制中的大延时、强干扰和各段间强烈耦合等特性带来的不利影响，控制效果不佳，因此，决定在氨合成塔温度控制中采用广义预测控制策略设计多路前馈广义预测控制器。     

A novel two-dimensional PID controller design using two-dimensional model predictive iterative learning control optimization for batch processes

It is known that the key indicators of batch processes are controlled by conventional proportional–integral–derivative (PID) strategies from the view of one-dimensional (1D) framework. Under such conditions, the information among batches cannot be used sufficiently; meanwhile, the repetitive disturbances also cannot be handled well. In order to deal with such situations, a novel two-dimensional PID controller optimized by two-dimensional model predictive iterative learning control (2D-PID-MPILC) is proposed. The contributions of this paper can be summarized as follows. First, a novel two-dimensional PID (2D-PID) controller is developed by combining the advantages of a PID-type iterative learning control (PIDILC) strategy and the conventional PID method. This novel 2D-PID controller overcomes the aforementioned disadvantages and extends the conventional PID algorithm from one-dimension to two-dimensions. Second, the tuning guidelines of the presented 2D-PID controller are obtained from the two-dimensional model predictive control iterative control (2D-MPILC) method. Thus, the proposed approach inherits the advantages of both PID control, PIDILC strategy, and 2D-MPILC scheme. The superiority of the proposed method is verified by the case study on the injection modelling process.     

基于立火道温度智能控制的研究

建立焦炉加热的智能控制模型,提出采用预测控制和模糊控制相结合的方法。预测控制提高控制精度,模糊控制为实时控制提供预测模型未收敛时的控制量及模型收敛后的修正前馈控制量。采用该算法对焦炉立火道温度进行控制,保证控制精度和被控对象的快速性,取得了较好的效果。     

Monitoring and retuning of low-level PID control loops

In this work, we focus on the problem of monitoring and retuning of low-level proportional-integral-derivative (PID) control loops used to regulate control actuators to the values computed by advanced model-based control systems like model predictive control (MPC). We consider the case where the real-time measurement of the actuation level is unavailable, and thus PID controller monitoring has to be achieved on the basis of process state measurements. A fault detection and isolation (FDI) method involving process models and real-time process measurements is used to monitor the PID control loops and compute appropriate residuals. Once poor tuning is detected and isolated, a PID tuning method based on the estimated transfer function of the control actuator is applied to the isolated, poorly functioning PID controller. An example of a non-linear reactor–separator process operating under MPC with low-level PID controllers regulating the control actuators is used to demonstrate the approach.     

Fuzzy model predictive control of nonlinear pH process

A new fuzzy model-based predictive control scheme was developed to control a nonlinear pH process. The control scheme is based on the Takagi-Sugeno type fuzzy model of the process being controlled. In the present fuzzy model predictive control method, the process model maintains a linear representation of the conclusion parts of fuzzy rules. Therefore, it has a significant advantage over other types of models in the sense that nonlinear processes can be handled effectively by embedding the linear characteristic. The fuzzy model of the pH process to be controlled was constructed and used in the predictive control algorithm. Results of computer simulations and experiments demonstrated the effectiveness of the present control method.     

A new predictive PID controller for the processes with time delay

A new proportional-integral-derivative (PID) controller is proposed based upon a simplified generalized predictive control (GPC) control law. The tuning parameters of the proposed predictive PID controller are obtained from the simplified GPC control law for the 1 ^st -order and 2 ^nd -order processes with time delays of integer and non-integer multiples of the sampling time. The internal model technique is employed to compensate the effect of time delay of the target process. The predictive PID controller is equivalent to the PI controller when the target process is 1 ^st -order and to the PID controller when the target process is an integrating process. The performance of the proposed predictive PID controller is almost the same as that of the simplified GPC. The main advantage of the proposed control scheme over other control methods is the ease of tuning and operation.     

Modeling and nonlinear predictive functional control of liquid level in a coke fractionation tower

The level control of the fractionation tower in industrial coke unit is not very easy due to its complex characteristics and nonlinearity. Most are controlled by PID or linear predictive control methods without considering the complexity. This paper shows that a more comprehensive nonlinear-model-based predictive control method can further improve control performance. A verification of a nonlinear process model with plant data is first shown. Then the design of nonlinear predictive functional control is discussed and results are shown by way of simulations and application to demonstrate the effectiveness and feasibility of the proposed control strategy.     

MODELING AND PREDICTIVE CONTROL OF MIMO NONLINEAR SYSTEMS USING WIENER-LAGUERRE MODELS

In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open loop stable MIMO nonlinear systems with deterministic inputs. The linear dynamic component of the model was parameterized using orthogonal Laguerre filters while the nonlinear state output map was constructed either using quadratic polynomial functions or artificial neural networks. The properties of the resulting model, such as open loop stability and steady-state behavior, are discussed in detail. The identified Weiner-Laguerre model was further used to formulate a nonlinear model predictive control (NMPC) scheme. The efficacy of the proposed modeling and control scheme was demonstrated using two benchmark control problems: (a) a simulation study involving control of a continuously operated fermenter at its optimum (singular) operating point and (b) experimental verification involving control of pH at the critical point of a neutralization process. It was observed that the proposed Weiner-Laguerre model is able to capture both the dynamic and steady-state characteristics of the continuous fermenter as well as the neutralization process reasonably accurately over wide operating ranges. The proposed NMPC scheme achieved a smooth transition from a suboptimal operating point to the optimum (singular) operating point of the fermenter without causing large variation in manipulated inputs. The proposed NMPC scheme was also found to be robust in the face of moderate perturbation in the unmeasured disturbances. In the case of experimental verification using the neutralization process, the proposed control scheme was found to achieve much faster transition to a set point close to the critical point when compared to a conventional gain-scheduled PID controller.     

MODELING AND PREDICTIVE CONTROL OF MIMO NONLINEAR SYSTEMS USING WIENER-LAGUERRE MODELS

In this work, a Weiner-type nonlinear black box model was developed for capturing dynamics of open loop stable MIMO nonlinear systems with deterministic inputs. The linear dynamic component of the model was parameterized using orthogonal Laguerre filters while the nonlinear state output map was constructed either using quadratic polynomial functions or artificial neural networks. The properties of the resulting model, such as open loop stability and steady-state behavior, are discussed in detail. The identified Weiner-Laguerre model was further used to formulate a nonlinear model predictive control (NMPC) scheme. The efficacy of the proposed modeling and control scheme was demonstrated using two benchmark control problems: (a) a simulation study involving control of a continuously operated fermenter at its optimum (singular) operating point and (b) experimental verification involving control of pH at the critical point of a neutralization process. It was observed that the proposed Weiner-Laguerre model is able to capture both the dynamic and steady-state characteristics of the continuous fermenter as well as the neutralization process reasonably accurately over wide operating ranges. The proposed NMPC scheme achieved a smooth transition from a suboptimal operating point to the optimum (singular) operating point of the fermenter without causing large variation in manipulated inputs. The proposed NMPC scheme was also found to be robust in the face of moderate perturbation in the unmeasured disturbances. In the case of experimental verification using the neutralization process, the proposed control scheme was found to achieve much faster transition to a set point close to the critical point when compared to a conventional gain-scheduled PID controller.