一种基于过程神经元网络辨识的PID控制模型及方法

针对非线性动态系统PID过程控制问题,提出了一种基于过程神经元网络辨识的PID参数自适应整定的控制模型和方法。利用过程神经元网络对于动态系统时变输入/输出信号的学习机制,在某种最优控制律下通过对被控对象进行辨识来追踪被控对象的输出对控制输入变化的灵敏度信息,实现参数自适应匹配的PID控制。给出了基于过程神经元网络辨识的PID控制系统结构以及相应的实现机制,实验结果验证了模型和算法的有效性。     

The identification of nonlinear models for process control using tailored “plant-friendly” input sequences

This paper considers certain practical aspects of the identification of nonlinear empirical models for chemical process dynamics. The primary focus is the identification of second-order Volterra models using input sequences that offer the following three advantages: (1) they are “plant friendly;” (2) they simplify the required computations; (3) they can emphasize certain model parameters over others. To provide a quantitative basis for discussing the first of these advantages, this paper defines a friendliness index f that relates to the number of changes that occur in the sequence. For convenience, this paper also considers an additional nonlinear model structure: the Volterra–Laguerre model. To illustrate the practical utility of the input sequences considered here, second-order Volterra and Volterra–Laguerre models are developed that approximate the dynamics of a first-principles model of methyl methacrylate polymerization.     

基于子空间辨识的状态空间模型预测控制

针对无法从工业过程中获得准确状态空间模型的问题,提出一种基于子空间辨识的状态空间模型预测控制方法。利用子空间辨识方法得到的状态空间模型作为系统模型,给出约束条件下的预测控制算法。以CD播放器机械臂系统为例,通过状态空间模型预测控制方法实现对系统输出的跟踪控制,仿真结果表明,该方法控制效果良好。     

基于模型辨识的非线性连续系统的迭代学习控制

基于非线性连续动态的模型辨识算法, 给出了非线性连续系统的一种非常有效的迭代学习控制方案. 该控制方案不要求非线性连续系统中具体的非线性关系, 并且容许系统初始误差的存在.     

全速范围内基于模型的速度辨识在矢量控制中的应用

在分析感应电机异步电机无速度传感器控制方法的基础上,讨论了基于模型的速度辨识方法。该速度辨识方案使用常规电流误差和新的伸缩算子来避免已知的不稳定区域,并且获得常数增益。此外还通过寄生直流成分的削除方案,以保证极低定子频率的运行,并在Matlab\Simulink环境下搭建了无速度传感器矢量控制系统的仿真模型,仿真结果证实了该方法的有效性。     

基于预测模型的浮选过程pH值控制

矿浆pH值是泡沫浮选过程中的一个非常重要的被控量.目前,多数选厂的矿浆pH值控制基本是依靠现场工人定期对矿浆样本进行pH值测量,凭主观经验对pH调整剂进行调整.由于操作工人的主观性和随意性的影响以及矿浆样本pH值测量与药剂调整间存在的较长的时间滞后,矿浆pH值波动频繁,很难使矿物浮选保持在一个稳定最优生产状态下运行.为了使矿浆pH值保持在一个期望的生产状态,基于浮选泡沫表面视觉信息提出了一种新的矿浆pH值控制方法,分别采用基于泡沫视觉信息的自适应遗传混合神经网络AG-HNN和自适应遗传PID(AG-PID)控制方法建立了矿浆pH值预测模型和pH值控制模型,基于所建立预测和控制模型对浮选药剂用量进行调整,解决了浮选矿浆pH值波动问题.工业浮选现场的实验结果表明该方法可以使矿浆pH值保持在一个期望的范围内,有效提高浮选性能.     

Adaptive control and identification of the dissolved oxygen process

This paper suggests how nonlinear adaptive control might lead to improved control of the dissolved oxygen (DO) concentration in the aerator of a wastewater treatment plant. The DO dynamics can be represented by a bilinear model for which we are interested in both parameter identification and control. The estimation of key parameters of the process model is important because the values of these parameters cannot be obtained from direct measurement. Hence a least-squares procedure for obtaining unique parameter estimates is developed and then combined with a minimum variance control algorithm to obtain an adaptive controller which is used both to generate useful parameter estimates and to control the process. Extensions to the case where the parameters vary at the same rate as the DO are also discussed.     

Time-local formulation and identification of implicit Volterra models by use of diffusive representation

We present a time-continuous identification method for nonlinear dynamic Volterra models of the form HX=f(u,X)+v with H, a causal convolution operator. It is mainly based on a suitable parameterization of H deduced from the so-called diffusive representation, which is devoted to state representations of integral operators. Following this approach, the complex dynamic nature of H can be summarized by a few numerical parameters on which the identification of the dynamic part of the model will focus. The method is validated on a physical numerical example.     

基于惯量辨识的测试转台自适应内模控制

为了降低系统模型参数变化对测试转台控制系统的影响,提出了基于惯量辨识技术的自适应内模控制方案.首先建立了某型号测试转台数学模型,设计了测试转台内模控制器,在此基础上采用基于扰动转矩观测器的惯量辨识算法来辨识测试转台转动惯量的变化情况,然后通过模糊控制器根据转动惯量的变化自动调整内模控制器的参数,从而确保了控制器的控制性能.仿真和实验结果表明,该控制方案对惯量的变化有着很强的自适应性,提高了测试转台控制系统的抗干扰性能和系统的鲁棒性,取得了较好的控制效果.     

Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters

A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.     

Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes

While good physiological models of the glucose metabolism in type 1 diabetic patients are well known, their parameterisation is difficult. The high intra-patient variability observed is a further major obstacle. This holds for data-based models too, so that no good patient-specific models are available. Against this background, this paper proposes the use of interval models to cover the different metabolic conditions. The control-oriented models contain a carbohydrate and insulin sensitivity factor to be used for insulin bolus calculators directly. Available clinical measurements were sampled on an irregular schedule which prompts the use of continuous-time identification, also for the direct estimation of the clinically interpretable factors mentioned above. An identification method is derived and applied to real data from 28 diabetic patients. Model estimation was done on a clinical data-set, whereas validation results shown were done on an out-of-clinic, everyday life data-set. The results show that the interval model approach allows a much more regular estimation of the parameters and avoids physiologically incompatible parameter estimates.     

基于支持向量机N4SID辨识模型的非线性预测控制

针对工业控制领域中非线性系统的模型辨识与预测控制问题,采用最小二乘支持向量机回归方法构造非线性函数,运用状态子空间（N4SID）模型辨识方法辨识非线性状态空间模型.在此基础上建立非线性预测控制器,利用拟牛顿算法进行非线性预测控制律的求解,从而实现了一种新的基于支持向量机N4SID辨识模型的非线性预测控制算法.仿真实验验证了该算法的有效性和可行性.     

System identification and improved internal model control for yaw of unmanned helicopter

The sea breeze is a low-frequency disturbance that severely damages the stability of small unmanned helicopters operating over the sea, especially for the yaw control, which is highly sensitive to disturbance. General internal model control is an appropriate method for dealing with this kind of operation conditions, whereas conventional internal model control cannot eliminate the tracking errors between a nominal model and a real model. In coping with unknown dynamics and low-frequency gust disturbances for small helicopters, this paper proposes a novel robust controller constructed with system identification and integrator-based improved general internal model. As a refinement of the conventional frame, the proposed control scheme extends the applicable scope of a controlled plant from a priori known dynamic to an unknown dynamic. Furthermore, under the proposed controller, it is guaranteed that the tracking error between the actual model and the nominal model converges to zero asymptotically. Finally, the effectiveness and advantage of the proposed control scheme are verified through comparative practical flight tests.     

一种基于在线辨识的Web 服务自适应接纳控制

设计并实现了一种基于量子行为粒子群算法(QPSO)系统模型在线辨识的Web服务自适应接纳控制,根据系统模型的变化在线调节比例积分控制器参数.通过接纳时间比反馈控制机制,调整控制周期内服务器接纳请求的时间长度,进而实现接纳控制.通过仿真实验,并与多种不同控制方法进行比较,所得结果表明,在线辨识自适应控制能够在服务器过载的情况下更有效地控制系统资源,进一步提高了服务质量.     

An integrated model based on statistical process control and maintenance

The close relationship between quality and maintenance of manufacturing systems has contributed to the development of integrated models, which use the concept of statistical process control (SPC) and maintenance. Such models not only help to improve quality of products but also lead to lower maintenance cost. In this paper, an integrated model is presented which considers complete failure and planned maintenance simultaneously. This model leads to six different scenarios. A new procedure for calculating average cost per time unit is also presented. Finally, a numerical example is used to evaluate sensitivity of the model parameters and compare performance of the developed model to a planned maintenance model. Results indicate satisfactory performance for the developed model.     

Feedback and feedforward control in the context of model predictive control with application to the management of type 1 diabetes mellitus

A unified formulation of feedback and feedforward control is given in the context of model predictive control. The ideas are illustrated by the management of type 1 diabetes mellitus although the general principles apply, mutatis mutandis, to other scenarios and problems.     

Experimental evaluation of MPC-based anti-surge and process control for electric driven centrifugal gas compressors

The present work concerns model predictive control (MPC) of centrifugal gas compressors and describes the development of an MPC application for the tasks of anti-surge and process control. More specifically, the MPC formulation focuses on the question of how the transient manipulation of driver torque can be used to improve the performance of anti-surge and process control. For the purpose of testing and validating the proposed control algorithm, an experimental compressor test rig is presented, which is designed to mimic a typical centrifugal compressor application in the oil and gas industry. Modeling and parameter identification of the experimental setup is followed by the realization of the MPC solution on an embedded system to comply with the stringent real-time requirements for anti-surge control. Testing is performed with experiments using suction and discharge side disturbances, which are created by rapid valve closures. For comparison the same tests are repeated with conventional control approaches. The test results indicate improvements in maintaining the distance to surge by up to 11%, while at the same time reducing the process control settling time by up to 50%.     

High-purity control of internal thermally coupled distillation columns based on nonlinear wave model

Internal thermally coupled distillation column (ITCDIC) is a frontier of energy saving distillation researches, which is a great improvement on conventional distillation column (CDIC). However its high degree thermal coupling makes the control design a bottleneck problem, where data-driven model leads to obvious mismatch with the real plant in the high-purity control processes, and a first-principle model which is comprised of complex mass balance relations and thermally coupled relations could not be directly used as control model for the bad online computing efficiency. In the present work, wave theory is extended to the control design of ITCDIC with variable molar flow rates, where a general nonlinear wave model of ITCDIC processes based on the profile trial function of the component concentration distribution is proposed firstly; combined with the thermally coupled relations, a novel wave model based generic model controller (WGMC) of ITCDIC processes is developed. The benzene-toluene system for ITCDIC is studied as illustration, where WGMC is compared with another generic model controller based on a data-driven model (TGMC) and an internal model controller (IMC). In the servo control and regulatory control, WGMC exhibits the greatest performances. Detailed research results confirm the efficiency of the proposed wave model and the advantage of the proposed WGMC control strategy.     

高温气冷核反应堆变负荷过程的非线性模型预测控制

HTR-PM作为采用高温气冷堆以及“两堆带一机”特殊结构的新型核电站系统,其本身具有强耦合、非线性等复杂特性;HTR-PM自身的参数在运行过程中也会随负荷的变化而变化,动态特性发生很大的偏移.本文提出了一种适用于HTR-PM变负荷过程的非线性预测控制策略,基于操作轨迹LPV模型的辨识方法,克服了HTR-PM耦合、非线性及参数时变等问题.仿真结果表明,本文提出的方法能提高HTR-PM变负荷过程中的运行平稳性,各项控制指标均高于控制要求,明显优于线性模型预测控制方法.