精馏塔分级最优控制的探讨

精馏控制是非常复杂的，其方案亦非常多，以一个精馏为例，应用多级递阶结构，按照关联平衡的协调原则，将精馏过程分解为回流量调节，物料平衡与能量平衡等三个子系统，并以回流量作为协调变量，去操纵物料平衡和能量平衡，以此实现对精馏塔的最优控制。     

基于PDE降阶模型的最优控制

针对带有约束条件的偏微分方程（PDE）模型最优控制的实时性要求和巨大的内存开销问题,提出了基于降阶模型的输入/状态约束的最优实时控制方法。采用特征正交分解和奇异值分解以及Galerkin投影方法导出了动态PDE具有高精度离散形式的状态空间低阶模型,提出了一定输入/状态约束条件下的基于二次规划单步滚动最优控制并与基于线性二次调节器的极值验证最优控制策略相互验证。通过对流-扩散-反应过程的控制仿真结果证明了所提方法的高效性和准确性。     

基于时延的电加热炉温度综合控制

针对电加热炉温度控制具有大惯性、大滞后及升温单向性等特点,首先采用递推最小二乘法对电加热炉的模型进行参数辨识并建立PID闭环系统的模型,在此基础上提出基于谱分解的中立型延时控制理论与PID相结合的综合控制方法,实现电加热炉温度的综合控制.仿真结果表明,基于综合控制策略的电加热炉温度控制系统具有超调小及升温快等优点.     

神经网络控制器在未建模型的非线性系统中的应用

应用神经网络对未建模型的非线性随机系统进行控制。采用Ｓｐａｌｌ＾「１」提出的同步扰动随机逼近的算法,通过系统的输出误差对神经网络控制器进行训练。因为系统模型未知,本在Ｓｐａｌｌ＾「１」的基础上采用了争层能自动增加的神经网络,通过实时的对神经网络的结构进行控制,可以实现对未建模型的非线笥随机系统的最优控制。     

噪声环境下参数估计和模型降阶的一种有效方法

针对噪声环境下的非线性系统参数估计和模型降阶问题,提出了一种带假设检验的微粒群优化算法(PSOHT),以最小化平均平方误差为目标,结合统计意义下的评价和比较,通过微粒群操作进行参数估计.基于典型非线性时滞系统的仿真实验,验证了所提算法的有效性和抗噪声能力.     

工业过程PI控制器性能评价

针对工业过程的PI控制器,提出了一种控制器性能评价的新算法。该算法以广义最小方差为目标,推导出最优PI控制器参数。以该最优控制器为基础,得到PI控制下系统的最优控制性能。然后,以最优控制的输出为基准,进行性能评价,并给出了性能评价的可操作步骤。最后通过仿真实验验证了该方法的有效性。     

连续搅拌釜式反应器的鲁棒最优控制

针对一类带不确定性的连续搅拌釜式反应器,提出基于滑模控制理论的鲁棒最优控制算法。输入输出线性化方法用于线性化对象模型,假设系统的不确定因素有界,滑模面采用积分型滑模面以确保系统稳态误差为零,将线性二次型理论用于等效控制律的设计中,保证了系统的性能指标最优,自适应滑模切换控制增益的选取在降低系统抖振的前提下补偿了系统的不确定因素及外部扰动,实现了控制器的鲁棒最优。通过仿真实验表明,提出的控制器对匹配的不确定性因素及外部扰动具有鲁棒性,且闭环系统的性能指标最优。     

多阶段间歇过程无穷时域优化线性二次容错控制

针对具有输入时滞的多阶段间歇过程,考虑执行器故障影响,提出了无穷时域优化混杂容错控制器设计方法。该方法首先将给定具有输入时滞的模型转化为新的无时滞的状态空间模型,接着再将此模型转换为包含状态变量误差和输出跟踪误差的扩展状态空间模型,并用切换系统模型表示,然后引入有限时域的二次目标函数,利用最优控制理论,设计出在无穷时域中容错控制器。为获得最小运行时间,针对不同阶段设计依赖于Lyapunov函数的驻留时间方法。创新之处在于,控制律设计简单,计算量小,且每一阶段时间求取不需要引用任何其他变量,简单易行。最后,以注塑成型过程为例,仿真结果证明所提出方法具有可行性和有效性。     

复杂非线性系统的子系统分解方法

利用社区发现算法研究了一种复杂非线性化工系统的子系统分解方法,并进行了分布式模型预测控制设计。引入信息图论的节点表示系统的状态、输入和输出变量,构建非线性过程系统的加权有向图,节点通过加权边连接,加权反映了节点间连接的强度,因而能够同时反映系统内部的连通性和连接强度。利用社区结构发现算法将所有变量分成子系统的群组,使得每个组内的关联比不同组间的相互作用强,从而得到复杂化工过程系统的子系统分解。针对连续搅拌反应釜过程,实施子系统分解,并设计分布式模型预测控制算法,结果表明,所提出的子系统分解方法更能考虑子系统之间的连接权重,得到更有利于分布式模型预测控制的子系统划分,提升系统控制的性能。     

多阶段间歇过程无穷时域优化线性二次容错控制

针对具有输入时滞的多阶段间歇过程,考虑执行器故障影响,提出了无穷时域优化混杂容错控制器设计方法。该方法首先将给定具有输入时滞的模型转化为新的无时滞的状态空间模型,接着再将此模型转换为包含状态变量误差和输出跟踪误差的扩展状态空间模型,并用切换系统模型表示,然后引入有限时域的二次目标函数,利用最优控制理论,设计出在无穷时域中容错控制器。为获得最小运行时间,针对不同阶段设计依赖于Lyapunov函数的驻留时间方法。创新之处在于,控制律设计简单,计算量小,且每一阶段时间求取不需要引用任何其他变量,简单易行。最后,以注塑成型过程为例,仿真结果证明所提出方法具有可行性和有效性。     

An Off-Line Formulation of Tube-Based Robust MPC Using Polyhedral Invariant Sets

In this paper, an off-line formulation of tube-based robust model predictive control (MPC) using polyhedral invariant sets is proposed. A novel feature is the fact that no optimal control problem needs to be solved at each sampling time. Moreover, the proposed tube-based robust MPC algorithm can deal with the linear time-varying (LTV) system with bounded disturbance. The simulation results show that the state at each time step is restricted to lie within a tube whose center is the state of the nominal LTV system that converges to the origin. Finally, the state is kept within a tube whose center is at the origin, so robust stability is guaranteed. Satisfaction of the state and control constraints is guaranteed by employing tighter constraint sets for the nominal LTV system.     

Frequency domain solution of the optimal control problem for linear distributed systems

This paper describes a numerical method for the spectrum factorization procedure. This numerical method enables the solution of the optimal control problem for linear distributed systems with quadratic performance indices. The numerical method is demonstrated on an optimal feed forward—feed back control system for a heat exchanger.     

基于加权偏离度统计方法的预测控制性能评估算法

针对带区域约束条件的预测控制系统性能评估问题,在考虑过程输出变量约束类型的基础上,提出了基于加权偏离度统计方法的控制性能评估算法。该方法依据控制要求的不同,将输出变量分为质量变量和约束变量,并结合工程经验合理选择变量的权重。基于系统闭环运行数据和约束设置,通过计算变量的加权偏离度得到控制系统的性能评估指标,从而为预测控制器的参数调整和性能提升提供了决策依据。系统仿真实例和工程应用证明了该评估算法对区域预测控制系统性能评估的有效性。     

Constraint data‐driven optimal terminal ILC of end product quality for a class of I/O constrained batch processes

In order to deal with the I/O constraints in a practical plant, a soft limiter is often added into the control design procedure directly; however, the performance of the soft limiter based control approach will be degraded greatly due to the use of the soft constraints. This paper proposes a data‐driven optimal terminal iterative learning control (constraint‐DDOTILC) approach for the end product quality control of batch processes with I/O hard constraints. To deal with nonlinearities, a novel iterative dynamic linearization method without omitting any information of the original plant is introduced such that the derived linearized data‐driven model is completely equivalent to the original nonlinear system. By transferring all the constraints on the system output, control input, and the change rate of input signals into a linear inequality, a novel constraint‐DDOTILC is developed by minimizing an objective function under the derived linear matrix inequality constraint. The optimal learning gain of the constraint‐DDOTILC can be updated iteratively according to the input and output measurements to enhance the flexibility for modifications and expansions of the controlled plant. Both theoretical analysis and simulation results confirm the effectiveness of the proposed constraint‐DDOTILC.     

Model Predictive Control with Feedforward Strategy for Gas Collectors of Coke Ovens

In coking process, the production quality, equipment life, energy consumption, and process safety are all influenced by the pressure in gas collector pipe of coke oven, which is frequently influenced by disturbances. The main control objectives for the gas collector pressure system are keeping the pressures in collector pipes at appropriate operating point. In this paper, model predictive control (MPC) strategy is introduced to control the collector pressure system due to its ability to handle constraint and good control performance. Based on a method proposed to simplify the system model, an extended state space model predictive control is designed, which combines the feedforward strategy to eliminate the disturbance. The simulation results in a system with two coke ovens show the feasibility and effectiveness of the control scheme.     

Optimization of blow molded part performance through process simulation

In this work, a gradient‐based numerical optimization scheme is proposed to determine the optimal process operating conditions to produce a blow molded part by with a given performance. Finite element simulations are used to relate the part performance to the processing conditions. A performance optimization is first performed to find the minimum part thickness distribution that minimizes the part weight while satisfying mechanical performance constraints such as maximum part deflection or maximum stress for an applied load. Then a process optimization finds the optimal operating conditions, e.g. the die gap opening profile, that minimize the part weight while respecting the minimum thickness distribution dictated by the performance optimization. The results show that the optimization scheme minimizes the part weight with minimal constraint violation. The addition of a constraint associated with process stability is proposed.     

Discrete-time contraction constrained nonlinear model predictive control using graph-based geodesic computation

Modern chemical processes need to operate around time-varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time-varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.     

DEVELOPMENT OF A SUPERVISORY CONTROL STRATEGY FOR THE OPTIMAL OPERATION OF GRAIN DRYERS

In spite of the importance and especially high energy demands of grain dryers, relatively few studies have been carried out to discover the optimal conditions for their operation. High performance operation can only be achieved if an adequate operating strategy is developed. For its implementation, a reliable control structure is required, and some of the limitations of the conventional control strategies normally used in dryers are observed. These strategies are SISO; the control normally used presents low performance and the disturbance is characterized by several amplitudes and frequencies. A possible way to minimize this difficulty consists of defining the multilevel structure such that each level acts at a given amplitude and frequency. In order to implement this multilevel structure, an optimization problem was developed to function as a supervisory control and a predictive algorithm (DMC) was used for servo or regulatory control. The proposed DMC algorithm presented satisfactory results for the load rejection and set-point variation, only when a small disturbance was applied. For a larger disturbance an optimization procedure was necessary. The routine efficiently maintained the optimal operational conditions of the dryer and could be used in the supervisory control of the system.     

Adaptive,multi-parameter battery state estimator with optimized time-weighting factors

We derive and implement a battery control algorithm that can accommodate an arbitrary number of model parameters, with each model parameter having its own time-weighting factor, and we propose a method to determine optimal values for the time-weighting factors. Time-weighting factors are employed to give greater impact to recent data for the determination of a system’s state. We employ the (controls) methodology of weighted recursive least squares, and the time weighting corresponds to the exponential-forgetting formalism. The output from the adaptive algorithm is the battery state of charge (remaining energy), state of health (relative to the battery’s nominal performance), and predicted power capability. Results are presented for a high-power lithium ion battery.     

基于多属性性能评估的焦炉加热燃烧过程在线优化控制方法

针对焦炉加热燃烧过程中控制器参数难以适应由加热煤气热值和结焦时间变化等因素引起的火道温度波动的问题, 设计了一种基于多属性性能评估的焦炉加热燃烧过程优化控制方法。首先通过分析焦炉加热燃烧过程的工艺特点及生产需求, 针对过程参数周期差异较大的特点, 提出了基于信息熵的多属性性能评估模型, 实现控制系统的在线性能评估。针对控制系统性能评估不合格的情况, 建立了以火道温度偏差、偏差变化率和调节时间为目标的多目标优化模型, 并采用差分进化算法进行求解, 通过控制器参数的在线调节, 保证焦炉火道温度的稳定。仿真结果表明该优化控制方法在加热煤气热值和结焦时间变化时能较好地抑制火道温度的波动。