Multiple-model control strategy for a fed-batch high cell-density culture processing

The fermentation of microorganisms is an important process in the biotechnology industry. To enhance the volumetric productivity, high cell concentration and cell productivity are required. Exponential feeding profiles are routinely applied to control growth rate and limit the production of inhibiting metabolite by-products. However, during aerobic high cell-density culture (HCDC) processes, both the rate of oxygen mass transfer and dissolved oxygen concentration decrease with increasing biomass. Due to the interactive influence of both oxygen and carbon on the growth rate of microorganisms, therefore, a good operating methodology in HCDC processes must maintain the relational concentrations of both substrates. The primary object in this work is to construct a simple and robust control strategy for this specific purpose. The overall control structure includes an optimal feedforword controller and a multiloop feedback controller. In each feedback loop, multiple-models are used such that the proposed control structure is simple to build. The feedback controllers, tuned by internal model control (IMC) principle, are scheduled by multiple-model in each loop. The control structure is shown by simulation to be robust with respect to mismatches in the model parameters and also in the model’s form. A worst case analysis on 20% mismatch in model’s five parameters showed a productivity loss of only 0.093%. Even though there exists oxygen mass transfer limitation, this control structure can still maintain good performance. Furthermore, an override control strategy can be added in the overall control structure to handle the oxygen-limiting problem.     

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.     

Bioreactor control using a probing feeding strategy and mid-ranging control

The paper presents a fed-batch fermentation technique for bioreactors operating close to their maximum oxygen transfer capacity. The method combines the advantages of the probing feeding strategy and the temperature limited fed-batch technique. When the maximum oxygen transfer capacity of the reactor is reached, the temperature is decreased to lower the oxygen demand. To achieve a good control of the dissolved oxygen a mid-ranging controller manipulating the stirrer speed and the temperature is used. The feeding strategy is analysed and it is also illustrated by simulations and an experiment.     

Temperature control of industrial gas phase polyethylene reactors

The performance of linear and nonlinear temperature control schemes is assessed for an open-loop unstable gas-phase polyethylene reactor (GPPER), based on speed, damping, robustness and the ability to maintain closed-loop stability in different operating regimes. An existing industrial GPPER model is improved by modelling the temperature states in the external heat exchanger using linear and nonlinear driving force models with varying numbers of heat transfer stages. Differences in heat exchanger models do not produce gain mismatch but do result in phase mismatch. It is shown that the nonlinear error trajectory controller (ETC) exhibits significantly superior responses in terms of speed, damping and robustness compared with an optimally-tuned PID controller. Therefore, substantial benefits could be realized using nonlinear controllers because they can provide good disturbance rejection capabilities and ensure closed-loop stability over a wide range of operating conditions. An approach is presented for tuning ETCs for minimum-phase processes of arbitrary relative degree.     

Persistency of excitation for continuous-time systems — Time-domain approach

This paper presents the time-domain approach to the analysis of the convergence of continuous-time adaptive control and estimation algorithms. The time-domain definition of persistently exciting signals is introduced and the convergence of estimation algorithms is established in the cases of open-loop and closed-loop systems. An application of the persistency of excitation theory to the design of a globally stable adaptive pole-placement controller is given.     

Direct pole placement adaptive tracking of non-minimum phase systems

In this paper, we propose a direct pole placement adaptive tracking scheme for non-minimum-phase, open-loop stable, linear plants with time delays. This controller utilizes the internal model principle to eliminate steady-state tracking error for signals with known distinct frequencies. The controller order depends only on the number of frequencies in the reference input, but not on the order of the plant. It is shown that with sufficiently small loop gain, the controller can guarantee stable closed loop, and asymptotic tracking.     

Adaptive receding horizon control for constrained MIMO systems

An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented.     

基于BPPID的疏浚管道稳定流速控制

针对疏浚船管道输送过程中泥浆管道流速难以控制,容易造成较大的功耗、磨损甚至会堵管、爆管等风险,本文以疏浚泥泵管道输送实验台为研究对象,提出一种基于BP神经网络的PID控制器,对管道输送流速进行稳定控制。在采用系统辨识方法对实验台进行建模的基础上,将BPPID与传统PID控制器进行仿真对比分析并利用模型实验台分别进行了流速阶跃变化和流速跟踪实验。实验结果表明BPPID控制对突变工况具有自适应自学习能力,为实际挖泥船输泥管道的稳定流速提供借鉴。     

A robust PI passivity‐based control of nonlinear systems and its application to temperature regulation

This paper deals with the problem of control of partially known nonlinear systems, which have an open‐loop stable equilibrium, but we would like to add a PI controller to regulate its behavior around another operating point. Our main contribution is the identification of a class of systems for which a globally stable PI can be designed knowing only the systems input matrix and measuring only the actuated coordinates. The construction of the PI is carried out invoking passivity theory. The difficulties encountered in the design of adaptive PI controllers with the existing theoretical tools are also discussed. As an illustration of the theory, we consider a class of thermal processes. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive control of light attenuation for optimizing microalgae production

The light attenuation factor, defined by the ratio between the incident light and the light at the bottom of the reactor, is a key operating parameter for light-limited phototrophic culture. Here, two nonlinear control laws have been proposed in order to regulate this ratio: a static controller, which is input-to-state stable with respect to measurement noise, and an adaptive controller. Then, we propose a set-point for the light attenuation factor in order to optimize microalgae productivity under constant illumination. Finally, numerical simulations illustrate how the adaptive controller can be used to optimize biomass productivity under realistic day–night cycles.     

序批式活性污泥法污水处理系统溶解氧优化控制方法

研究序批式活性污泥法(SBR)污水处理过程溶解氧(DO)的优化控制方法,探讨不同DO曲线下出水水质、曝气时间和能源消耗等问题。溶解氧是污水处理过程的一个重要参数,DO的需求量是有规律地变化,当系统的实际溶解氧量能按一定规律变化时,污水处理将能得到一个较好的效果。采用模糊控制解决了SBR系统出水水质不能在线检测、数学建模困难、不便于实时控制等问题,通过模糊控制方法实时地改变风机的频率来满足溶解氧的要求,可实现对不同溶解氧曲线的控制。从而缩短了污水处理时间,稳定了出水质量,明显地减少了电能消耗～15%,实现了污水处理过程的实时优化控制。     

Learning robustly stable open-loop motions for robotic manipulation

Robotic arms have been shown to be able to perform cyclic tasks with an open-loop stable controller. However, model errors make it hard to predict in simulation what cycle the real arm will perform. This makes it difficult to accurately perform pick and place tasks using an open-loop stable controller. This paper presents an approach to make open-loop controllers follow the desired cycles more accurately. First, we check if the desired cycle is robustly open-loop stable, meaning that it is stable even when the model is not accurate. A novel robustness test using linear matrix inequalities is introduced for this purpose. Second, using repetitive control we learn the open loop controller that tracks the desired cycle. Hardware experiments show that using this method, the accuracy of the task execution is improved to a precision of 2.5 cm, which suffices for many pick and place tasks.     

Dissolved oxygen control for activated sludge processes

Controllers for dissolved oxygen reference trajectory tracking for activated sludge processes are proposed and investigated. A nonlinear model predictive controller and a direct reference adaptive controller are investigated. Both the nutrient and the phosphorous removal from a wastewater by its biological treatment using an activated sludge technology are considered. An approach to the controller design utilises a structure of the dissolved oxygen dynamics and its two time scales: fast and slow. The predictive controllers offer good tracking performance and robustness. The direct model reference adaptive controller is much simpler to implement. However, it is more difficult to compromise between tracking accuracy and rate of change and magnitudes of the control actions. The controllers are validated by simulation using real data sets and an ASM2d model of the biological reactor.     

Series PID controller tuning based on the SIMC rule and signal filtering

The SIMC (Simple control) rule, proposed by Skogestad, is ineffective for a class of processes with oscillatory dynamics and processes defined by transfer functions obtained as a result of ideal decoupling of multiple-input multiple-output systems. For this class of stable processes it is proposed to apply a higher-order filtering to the open-loop process step response and to approximate the filtered step response with stable SOPDT models. These models are used to obtain a high performance/robustness tradeoff by the ideal series PID controllers, tuned by the SIMC rule, with the higher-order filter in the feedback loop. Parallel PID controllers, with higher-order noise filters, tuned by applying exact process frequency response and optimization under constraints on the robustness and sensitivity to measurement noise, are used to demonstrate merits of the proposed simple design and tuning of the series PID controller. Experimental results on a mechanical laboratory plant are presented in Appendix.     

棒材连轧机立式活套的多变量自适应解耦控制

将非线性补偿和PI控制策略相结合,针对棒材连轧机立式活套系统提出了一种多变 量自适应解耦控制器,从理论上证明了闭环系统大范围渐近稳定,自适应参数能以很小的误差 跟踪实际参数的变化,并给出了可用于控制器参数整定的系统动态性能与控制器参数间的定量 关系.仿真和工业实验证明该算法具有良好的控制效果.     

随机多变量系统的自适应容错控制

为保证控制系统在正常和故障状态下都能可靠安全地运行,本文将自适应控制方法和容 错技术结合,提出了一种适于随机多变量系统的新型自适应容错控制器.该控制器不仅适于 开环不稳定的非最小相位系统,而且当执行机构卡死或失效时仍能稳定工作,仿真结果验证了 本文算法的有效性.     

基于自组织模糊神经网络溶解氧控制方法研究

针对污水处理过程中溶解氧浓度难以控制的问题,提出了一种基于自组织模糊神经网络（self-organizing fuzzy neural network, SOFNN）的溶解氧（dissolved oxygen, DO）控制方法。首先,采用激活强度和神经元重要性两个评判标准,来判断神经元对网络的贡献及活跃程度。然后,对不活跃的神经元进行删减,以此来对神经网络结构进行自适应的调整,从而满足实际控制要求,提高控制精度。其次,采用梯度下降算法对SOFNN神经网络的各个参数进行实时调整,以保证网络的精度。最后,将该自组织方法用在Mackey-Glass时间序列预测中,结果表明所提出的自组织模糊神经网络具有较好的预测效果;同时将所提出的SOFNN方法在BSM1仿真平台上进行实验验证。结果表明,所提出的自组织模糊神经网络控制方法能够对溶解氧浓度进行较好地控制,具有一定的自适应能力。     

Nonlinear model predictive control of fed-batch fermentations using dynamic flux balance models

Fed-batch fermentation is an important production technology in the biochemical industry. Using fed-batch Saccharomyces cerevisiae fermentation as a prototypical example, we developed a general methodology for nonlinear model predictive control of fed-batch bioreactors described by dynamic flux balance models. The control objective was to maximize ethanol production at a fixed final batch time by adjusting the glucose feeding rate and the aerobic–anaerobic switching time. Effectiveness of the closed-loop implementation was evaluated by comparing the relative performance of NMPC and the open-loop optimal controller. NMPC was able to compensate for structural errors in the intracellular model and parametric errors in the substrate uptake kinetics and cellular energetics by increasing ethanol production between 8.0% and 14.7% compared with the open-loop operating policy. Minimal degradation in NMPC performance was observed when the biomass, glucose, and ethanol concentration and liquid volume measurements were corrupted with Gaussian white noise. NMPC based on the dynamic flux balance model was shown to improve ethanol production compared to the same NMPC formulation based on a simpler unstructured model. To our knowledge, this study represents the first attempt to utilize a dynamic flux balance model within a nonlinear model-based control scheme.     

Discrete-Time Adaptive Command Following and Disturbance Rejection With Unknown Exogenous Dynamics

We present an adaptive controller that requires limited model information for stabilization, command following, and disturbance rejection for mult-input multi-output minimum-phase discrete-time systems. Specifically, the controller requires knowledge of the open-loop system's relative degree as well as a bound on the first nonzero Markov parameter. Notably, the controller does not require knowledge of the command or the disturbance spectrum as long as the command and disturbance signals are generated by a Lyapunov-stable linear system. Thus, the command and disturbance signals are combinations of discrete-time sinusoids and steps. In addition, the Markov-parameter-based adaptive controller uses feedback action only, and thus does not require a direct measurement of the command or disturbance signals. Using a logarithmic Lyapunov function, we prove global asymptotic convergence for command following and disturbance rejection as well as Lyapunov stability of the adaptive system when the open-loop system is asymptotically stable.      

Multivariable PID control by decoupling

This paper presents a new methodology to design multivariable proportional-integral-derivative (PID) controllers based on decoupling control. The method is presented for general n × n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimise interactions. It depends on the desired open-loop processes that are specified according to realisability conditions and desired closed-loop performance specifications. These realisability conditions are stated and three common cases to define the open-loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the wind-up problem is also considered and a new anti-wind-up scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process.