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Content available remote Adaptive control scheme based on the least squares support vector machine network
Recently, a new type of neural networks called Least Squares Support Vector Machines (LS-SVMs) has been receiving increasing attention in nonlinear system identification and control due to its generalization performance. This paper develops a stable adaptive control scheme using the LS-SVM network. The developed control scheme includes two parts: the identification part that uses a modified structure of LS-SVM neural networks called the multi-resolution wavelet least squares support vector machine network (MRWLS-SVM) as a predictor model, and the controller part that is developed to track a reference trajectory. By means of the Lyapunov stability criterion, stability analysis for the tracking errors is performed. Finally, simulation studies are performed to demonstrate the capability of the developed approach in controlling a pH process.
Content available remote Cellulosic fiber for odor and pH control
A new approach to odor and pH control in medical and hygiene care applications relies on a treatment of absorbent fluff with selected enzyme inhibitors. Cellulose fiber was found to be a convenient carrier for some chemicals which modify the structure of an enzyme and/or block its active site. In an absorbent system exposed for example to body liquids containing urea, selected substances, when released from the fibrous material, can effectively slow down enzymatic hydrolysis of NH2CONH2 and suppress the emission of ammonia. An additional benefit of this technology is in this case a control of the pH of the skin environment and keeping it closer to neutral or slightly acid region. A slowdown of the ammonia emission can be enhanced by blending sodium polyacrylate-based superabsorbent polymer (SAP) particles with the modified cellulose fibers. Suitable inhibitors of urease can also be applied directly to absorbent nonwoven fabrics comprising fluff.
Content available remote Application of reduced order phenomenological model to pH nonlinear control
Changes in process sensitivity with pH make the design of the conventional controllers difficult. pH processes are very hard to control because of strong nonlinearity and usually time varying characteristics. A lot of work has been done in the field of phenomenological modelling of the pH processes. However, there is a need to simplify the model to have it suitable for control purposes. This follows from the fact that the internal model for the controllers must be as simple as possible. Wiener models consisting of a linear dynamic element followed by a static nonlinear element are considered to be perfect to represent a wide range of nonlinear processes. The proposed reduced order phenomenological model of pH has a similar form as the Wiener models but includes bilinear reduced order phenomenological dynamic element. This model was validated using the real world pilot plant. The possibility of combining the phenomenological and arbitrary approaches occurred. The aim of this paper is to present reduced order model of pH process which can be used for simulation purposes and to design process model based controller.
Content available remote Differential Evolution based Fuzzy Logic Controller for Nonlinear Process Control
This paper presents an unconventional approach to adaptive fuzzy logic controller (FLC) de-sign wherein a new evolution strategy. Differential Evolution (DE) is used in the simultane-ous design of membership functions and rule sets for fuzzy logic controllers. Differential Evo-lution is an exceptionally simple, fast and robust population based search algorithm that is able to locate near-optimal solutions to difficult problems. This technique, which is similar to genetic algorithms, has been applied to the control of pH, which is a requirement in many chemical industries. Control of pH poses a difficult problem because of inherent nonlinearities and frequently changing process dynamics. This technique has been successfully implemented on a laboratory scale pH plant setup. The results have been compared with a simple GA based adaptive FLC where we have incorporated a search space smoothing function for achieving faster convergence and for ascertaining a global optimum. Results indicate that FLC's aug-mented with DE's offer a powerful alternative to GA based FLC's. Results also show that the search space smoothing function helps in faster convergence of a GA.
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