The design of experiments,training and implementation of nonlinear controllers based on neural networks

In the area of nonlinear predictive control, several control schemes using artificial neural networks have been proposed. In this work, the issues relating to the information contents of the data used to train the neural network components of these nonlinear predictive control schemes are considered. This raises questions about the design of experiments. A class of feedback-feedforward nonlinear controller based on the model predictive structure (also known as Internal Model Control, IMC, structure) is investigated. The implementation and performance of these neural network based controllers, together with comparisons to other nonlinear and linear controllers, are illustrated on two nonlinear continuous-stirred-tank-reactor simulations.     

A feedback control framework for safe and economically‐optimal operation of nonlinear processes

Maintaining safe operation of chemical processes and meeting environmental constraints are issues of paramount importance in the area of process systems and control engineering, and are ideally achieved while maximizing economic profit. It has long been argued that process safety is fundamentally a process control problem, yet few research efforts have been directed toward integrating the rather disparate domains of process safety and process control. Economic model predictive control (EMPC) has attracted significant attention recently due to its ability to optimize process operation accounting directly for process economics considerations. However, there is very limited work on the problem of integrating safety considerations in EMPC to ensure simultaneous safe operation and maximization of process profit. Motivated by the above considerations, this work develops three EMPC schemes that adjust in real‐time the size of the safety sets in which the process state should reside to ensure safe process operation and feedback control of the process state while optimizing economics via time‐varying process operation. Recursive feasibility and closed‐loop stability are established for a sufficiently small EMPC sampling period. The proposed schemes, which effectively integrate feedback control, process economics, and safety considerations, are demonstrated with a chemical process example. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2391–2409, 2016     

Statistical machine-learning–based predictive control of uncertain nonlinear processes

In this study, we present machine-learning–based predictive control schemes for nonlinear processes subject to disturbances, and establish closed-loop system stability properties using statistical machine learning theory. Specifically, we derive a generalization error bound via Rademacher complexity method for the recurrent neural networks (RNN) that are developed to capture the dynamics of the nominal system. Then, the RNN models are incorporated in Lyapunov-based model predictive controllers, under which we study closed-loop stability properties for the nonlinear systems subject to two types of disturbances: bounded disturbances and stochastic disturbances with unbounded variation. A chemical reactor example is used to demonstrate the implementation and evaluate the performance of the proposed approach.     

变增益的非线性预测控制算法

采用变增益策略,用输入与稳态输出的映射表示系统的静态非线性,用一个增益为1的ARX模型表示系统的动态模型,代替多数文献中常用的分段线性多模型方法进行非线性系统的预测控制.文中通过对连续搅拌釜反应器(CSTR)的仿真,验证了本算法的有效性.     

On-Farm Drying and Storage Systems

ABSTRACT

Rotary dryers are widely used for the continuous drying of minerals and chemicals on a large scale. Hot gases are passed parallel to the flowing solid to achieve the desired product moisture content. Because these dryers are energy intensive, it is mandatory to operate them as efficiently as possible to respond to economic pressures. Using a dynamic rotary dryer simulator for mineral concentrate, five control strategies are evaluated and compared. Two control strategies are based on PI controllers and the others use neural network models. Results clearly show that a feedforward action, in conjunction with a PI controller or incorporated within the structure of a neural network model, led to the best performances provided an accurate measurement of the feed moisture content is available.     

Dynamics And Assessment of Some Control Strategies of a Simulated Industrial Rotary Dryer

Rotary dryers are widely used for the continuous drying of minerals and chemicals on a large scale. Hot gases are passed parallel to the flowing solid to achieve the desired product moisture content. Because these dryers are energy intensive, it is mandatory to operate them as efficiently as possible to respond to economic pressures. Using a dynamic rotary dryer simulator for mineral concentrate, five control strategies are evaluated and compared. Two control strategies are based on PI controllers and the others use neural network models. Results clearly show that a feedforward action, in conjunction with a PI controller or incorporated within the structure of a neural network model, led to the best performances provided an accurate measurement of the feed moisture content is available.     

非线性Hammerstein模型预测控制策略及其在pH中和过程中的应用

所有实际工业过程都包含一定程度的非线性,如pH中和过程由于其本身的强非线性是工业过程控制中具有挑战性的难题,但至今为止仍缺乏有效的非线性控制方法。将基于差分方程模型的模型预测控制策略(model predictive control,MPC)推广到包含一个静态非线性多项式函数和一个线性差分方程动态环节的非线性Hammerstein系统,详细描述了基于静态非线性多项式函数的最优控制作用求解方法,提出了一套新的非线性Hammerstein MPC 控制策略(nonlinear Hammerstein predictive control,NLHPC)。pH中和过程控制仿真和控制实验表明,NLHPC的控制结果好于工业上常用的非线性 PID(nonlinear PID,NL-PID)控制器。     

基于神经网络的pH中和过程非线性预测控制

针对pH中和过程这一化工过程系统中的典型非线性对象特点,应用神经网络建模思想和模型预测控制方法,并结合Hammerstein模型特点,研究pH中和过程非线性系统的两种新型模型预测控制手段,分别建立基于神经网络的非线性预测控制系统整体求解策略和基于Hammerstein模型的两步法预测控制策略,并用MATLAB对其进行仿真。控制仿真结果表明,建立的神经网络预测控制策略和非线性Hammerstein模型预测控制均优于传统PID控制方法,具有良好的设定值跟踪效果和抗干扰控制响应,说明这两种控制策略是非线性过程的有效控制方法。     

Distributed economic model predictive control for operational safety of nonlinear processes

Achieving operational safety of chemical processes while operating them in an economically‐optimal manner is a matter of great importance. Our recent work integrated process safety with process control by incorporating safety‐based constraints within model predictive control (MPC) design; however, the safety‐based MPC was developed with a centralized architecture, with the result that computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. To address this potential practical limitation of the safety‐based control design, in this work, we propose the integration of a distributed model predictive control architecture with Lyapunov‐based economic model predictive control (LEMPC) formulated with safety‐based constraints. We consider both iterative and sequential distributed control architectures, and the partitioning of inputs between the various optimization problems in the distributed structure based on their impact on process operational safety. Moreover, sufficient conditions that ensure feasibility and closed‐loop stability of the iterative and sequential safety distributed LEMPC designs are given. A comparison between the proposed safety distributed EMPC controllers and the safety centralized EMPC is demonstrated via a chemical process example. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3404–3418, 2017     

Real‐time economic model predictive control of nonlinear process systems

Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state. © 2014 American Institute of Chemical Engineers AIChE J, 61: 555–571, 2015