Adaptive control/modelling

Quo, C.F., Moffitt, R.A., MerriU, A.H., Wang, M.D. (2011) Adaptive control model reveals systematic feedback and key molecules in metabolic pathway regulation. J Comput. Biol. 18, 169-182. 

Besides expertise in process analysis the consultancy held a patent on their adaptive control model that formed the basis for the operational implementation of the control project s solutions. The adaptive controller s advantage was to adapt its machine steering based on tendencies in the machines performance history. According to CORE, their adaptive control method improved mitigation of process oscillations - a well-known problem with typical Proportional-Integral-Derivative (PID) controllers because their fast operation cycles often over steer slowly responding machine process. 

More recent work has shown that the observed variation in propagation rate constants with composition is not sufficient to define the polymerization rates.5" 161,1152 There remains some dependence of the termination rate constant on the composition of the propagating chain. Thus, the chemical control (Section 7.4.1) and the various diffusion control models (Section 7.4.2) have seen new life and have been adapted by substituting the terminal model propagation rate constants (ApXv) with implicit penultimate model propagation rate constants (kpKY -Section 7.3.1.2.2). 

The derivation of process models for adaptive control falls exactly within the framework of the estimation problem studied in this chapter. Control-related implementation are natural extensions to the current work and are... 

The correct interpretation of measured process data is essential for the satisfactory execution of many computer-aided, intelligent decision support systems that modern processing plants require. In supervisory control, detection and diagnosis of faults, adaptive control, product quality control, and recovery from large operational deviations, determining the mapping from process trends to operational conditions is the pivotal task. Plant operators skilled in the extraction of real-time patterns of process data and the identification of distinguishing features in process trends, can form a mental model on the operational status and its anticipated evolution in time. 

Model of Dissolution. Based on the results described above, a model for the dissolution of phenolic resins in aqueous alkali solutions 1s proposed. The model 1s adapted from Ueberrelter s description for polymer dissolution 1n organic solvents (.21). In Ueberrelter s model, the dissolution process takes place 1n several steps with the formation of (a) a liquid layer containing the dissolved polymer, (b) a gel layer, (c) a solid swollen layer, (d) an infiltration layer, and (e) the unattacked polymer. The critical step which controls the dissolution process is the gel layer. In adapting h1s model to our case, we need to take into account the dependence of solvation on phenolate ion formation. There 1s a partition of the cation and the hydroxide ion between the aqueous solution and the solid phase. The... 

Adaptive controllers can be usefully applied because most processes are nonlinear (Section 7.16) and common controller design criteria (Section 7.12) are based on linear models. Due to process non-linearities, the controller parameters required to give the desired response of the controlled variable change as the process steady state alters. Furthermore, the characteristics of many processes vary with time, e.g. due to catalyst decay, fouling of heat exchangers, etc. This leads to a deterioration in the performance of controllers designed upon a linear basis. 

This is employed when the process is not well-known. The Model Reference Adaptive Controller contains a reference model to which the command signal or set point change is applied as well as to the process itself (Fig. 7.98)<4 ). The output of the reference model is postulated as the desired controlled process output and this is compared with the actual process output. The difference (or error) e , between the two outputs is used to adjust the controller parameters so as to minimise the relevant integral criterion. For example, if the ISE criterion is employed then the quantity... 

Fio. 7.98. Block diagram of model reference adaptive control system... 

The architecture of the self-tuning regulator is shown in Fig. 7.99. It is similar to that of the Model Reference Adaptive Controller in that it also consists basically of two loops. The inner loop contains the process and a normal linear feedback controller. The outer loop is used to adjust the parameters of the feedback controller and comprises a recursive parameter estimator and an adjustment mechanism. 

Coughanowr, D. R. Process Systems Analysis and Control, 2nd edn. (McGraw-Hill, New York, 1991). Kuo, B. C. Discrete Data Control Systems (Prentice-Hall, Englewood Cliffs, New Jersey, 1970). Landau, Y. D. Adaptive Control—The Model Reference Approach (Marcel Dekker, New York, 1979). Popovic, D. and Bhatkar, V. P. Distributed Computer Control for Industrial Automation (Marcel Dekker, New York, 1990). 

Landau, Y. D. Adaptive Control—The Model Reference Approach (Marcel Dekker, New York, 1979). 

In the following, the model-based controller-observer adaptive scheme in [15] is presented. Namely, an observer is designed to estimate the effect of the heat released by the reaction on the reactor temperature dynamics then, this estimate is used by a cascade temperature control scheme, based on the closure of two temperature feedback loops, where the output of the reactor temperature controller becomes the setpoint of the cooling jacket temperature controller. Model-free variants of this control scheme are developed as well. The convergence of the overall controller-observer scheme, in terms of observer estimation errors and controller tracking errors, is proven via a Lyapunov-like argument. Noticeably, the scheme is developed for the general class of irreversible nonchain reactions presented in Sect. 2.5. 

X.Q. Xie, D.H. Zhou, and Y.H. Jin. Strong tracking filter based adaptive generic model control. Journal of Process Control, 9 337-350, 1999. 

In the fifth chapter, a general overview of temperature control for batch reactors is presented the focus is on model-based control approaches, with a special emphasis on adaptive control techniques. Finally, the sixth chapter provides the reader with an overview of the fundamental problems of fault diagnosis for dynamical systems, with a special emphasis on model-based techniques (i.e., based on the so-called analytical redundancy approach) for nonlinear systems then, a model-based approach to fault diagnosis for chemical batch reactors is derived in detail, where both sensors and actuators failures are taken into account. 

Pattern recognition self-adaptive controllers exist that do not explicitly require the modeling or estimation of discrete time models. These controllers adjust their tuning based on the evaluation of the system s closed-loop response characteristics (i.e., rise time, overshoot, settling time, loop damp-... 

The classical adaptive control scheme is shown in Figure 2.58. Its goal is to use online identification through artificial intelligence (Al), neural networks, and fuzzy logic to adapt the model to the actual process. Al and model predictive control (MPC) can tolerate inaccuracy and uncertainty in the model, and online training can continuously improve the model. 

Model reference adaptive control is based on a Lyapunov stability approach, while the hyperstability method uses Popov stability analysis. All of the above methods have been tested on experimental systems, both SISO and MIMO (53), (54), (55). The selftuning regulator is now available as a commercial software package, although this method is not satisfactory for variable time delays, an important industrial problem. 

Other recent developments in the field of adaptive control of interest to the processing industries include the use of pattern recognition in lieu of explicit models (Bristol (66)), parameter estimation with closed-loop operating data (67), model algorithmic control (68), and dynamic matrix control (69). It is clear that discrete-time adaptive control (vs. continuous time systems) offers many exciting possibilities for new theoretical and practical contributions to system identification and control. 

Shah, S. L., Fisher, D. G. and Karim, N. M., "Hyperstability Adaptive Control-A Direct Input-Output Approach without Explicit Model Identification," Proc. Joint Automatic Control Conference, 1979, 481. 

Oliver, W. K. Seborg, D. E. and Fisher, D.G., "Model Reference Adaptive Control Based on Lyapunov s Direct Method," Chem. Engr. Comm., 1973, 1, 125. 

Modeling, Parameter Identification, and Adaptive Control of Anticoagulant Drug Therapy... 

In the recent years Simulated Moving Bed (SMB) technology has become more and more attractive for complex separation tasks. To ensure the compliance with product specifications, a robust control is required. In this work a new optimization bas adaptive control strategy for the SMB is proposed A linearized reduced order model, which accounts for the periodic nature of the SMB process is used for online optimization and control purposes. Concentration measurements at the raffinate and extract outlets are used as the feedback information together with a periodic Kalman filter to remove model errors and to handle disturbances. The state estimate from the periodic Kalman filter is then used for the prediction of the outlet concentrations over a pre-defined time horizon. Predicted outlet concentrations constitute the basis for the calculation of the optimal input adjustments, which maximize the productivity and minimize the desorbent consumption subject to constraints on product purities. 

In two papers of great interest, Roberts (1960a,b) has considered the dynamic programming formulation of a catalyst replacement problem. It will not be possible to discuss the whole of his work here as it goes a long way towards a consideration of adaptive control we shall content ourselves with one of his simpler models which brings out some features of stochastic dynamic programming. 

Challenges in real-time process optimization mainly arise from the inability to build and adapt accurate models for complex physico-chemical processes. This paper surveys different ways of using measurements to compensate for model uncertainty in the context of process optimization. A distinction is made between model-adaptation methods that use the measurements to update the parameters of the process model before repeating the optimization, modifier-adaptation methods that adapt constraint and gradient modifiers, and direct-input-adaptation methods that convert the optimization problem into a feedback control problem. This paper argues in favor of modifier-adaptation methods, since it uses a model parameterization, measurements, and an update criterion that are tailored to the tracking of the necessary conditions of optimality. 

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