Knowledge-based adaptive controllers

Most supervisory controllers contain some minimal capability to vary from the process plan, such as the ability to detect faulty thermocouples and react appropriately. Knowledge-based, real-time controllers take this adaptability one step further by using combinations of sensors and/or models to determine the state of the process and predict trends. The controllers then compare the measured state with the desired state, and change the process plan when necessary to adapt to those trends, forcing the outcome toward a desired state. Knowledge-based systems must include some means of converting the sensor data into information and a set of rules to act on that information. Knowledge-based control systems are not always 

Expert systems is a general term used to refer to computer programs that incorporate expert knowledge, usually in the form of rules, to duplicate the behavior of a human expert. Rules can be used to define process states  

IF the past autoclave temperature is less than the current autoclave temperature THEN the autoclave temperature is rising 

This simpler rule can be combined with others to define more complex process states  

IF the middle-laminate temperature is rising AND the top-laminate temperature is rising AND middle-laminate temperature is accelerating THEN accelerated reaction is active [26] 

---

Having successfully implemented conventional MRAC techniques, the next logical step was to try to incorporate the MRAC techniques into a neural network-based adaptive control system. The ability of multilayered neural networks to approximate linear as well as nonlinear functions is well documented and has foimd extensive application in the area of system identification and adaptive control. The noise-rejection properties of neural networks makes them particularly useful in smart structure applications. Adaptive control schemes require only limited a priori knowledge about the system to be controlled. The methodology also involves identification of the plant model, followed by adaptation of the controller parameters based on a continuously updated plant model. These properties of adaptive control methods makes neural networks ideally suited for both identification and control aspects [7-11]. 

The research efforts that have been stimulated by the phenomenon of enhanced degradation have resulted in the creation of a substantial knowledge base regarding microbial adaptation for pesticide degradation and associated pest control failures. The body of scientific literature on the subject has grown rapidly and Is now substantial. Recent reviews of enhanced degradation and microbial adaptation for pesticide degradation by Kaufman (2) (59 ref.),... 

Model based control schemes such as model predictive control are highly related to the accuracy of the process model. A regional-knowledge index is proposed in this study and applied in the analysis of dynamic artificial neural network models in process control. To tackle the extrapolation problem and assure stability of the control system, we propose to run a neural adaptive controller in parallel with a model predictive control. A coordinator weights the outputs of these two controllers to make the final control decision. The proposed analysis method and the modified model predictive control architecture have been applied to a neutralization process and excellent control performance is observed in this highly nonlinear system. 

Today, even after more than 20 years, almost all developed methods related to the synthesis of mesoporous materials by surfactant soft templates still use knowledge based on mesoporous silica materials. The synthesis of mesoporous oxides of TMs (i.e., Ti, Zr, and Mn), metalloids (i.e., Ge), posttransition metals (i.e., A1 and Ga), and lanthanides (i.e., Ce) has been adapted from the methods developed in mesoporous silica synthesis [44-49]. In other words, one can easily find a silica analog of any procedure for the synthesis of non-silicious mesoporous oxides. Flexible Si—O bonds made via well-known and easily manageable sol-gel chemistry, allow one to use various solvents or solvent mixtures (i.e., aqueous or alcoholic), pH (1-7), temperatures, and pressures to synthesize numerous mesoporous silica materials [50]. However, sol-gel chemistry of other elements especially TMs requires more controlled reaction conditions. The sol-gel chemistry (hydrolysis and condensation) of early (group I-IV) TMs can be controlled in alcoholic solutions with proper pH, temperature, and humidity adjustments [2,4,10,46,47,50]. Typical TM sources are either commercially available alkoxides (i.e., titanium isopropox-ide) or can be formed in situ by the reaction between anhydrous TM chloride salts and alcohols (i.e., WClg + EtOH W(OCH2CH3)6). 

The control of such structures has been extensively studied. The main approaches are methods of non exciting trajectories [3], adaptative control [4], control using a knowledge model. In this last case, we can find singular perturbation methods [5], LQR methods based on an approximated linearized model [6] or non linear decoupling methods [7]. 

Section 9.4 introduces K-RAMP from its process perspective. It is explained how the approach determines reusable elements of forerunner products or of existing process plans for the creation of forerunner products. Based on this process view on K-RAMP, the requirements to be satisfied are introduced in Sect. 9.5. In Sect. 9.6, the underlying ontology models and the software architecmre of the knowledge base are described. Section 9.7 is completing the description of the approach by addressing the reuse and adaptation of semps of the process control software (further described as production-IT). This part is particularly essential as process control is cmcial in order to meet the quality targets at the end of the ramp-up phase. 

Based on this comparison of TPO pretreatment processes, their quality control methods, including actual (a, b, c) or hypothetical (d, e) knowledge of their adhesion mechanisms and their benefits, it was concluded that the low viscosity olefinic polymers may be feasible to be incorporated in the production pigmented color coats. A rigorous evaluation is essential before their application can be adapted in the automotive manufacturing plants, and the benefits realized. 

---