A fuzzy logic based dynamic wave model inversion algorithm for canal regulation

A fuzzy logic based centralized control algorithm for irrigation canals is presented. Purpose of the algorithm is to control downstream discharge and water level of pools in the canal, by adjusting discharge release from the upstream end and gates settings. The algorithm is based on the dynamic wave model (Saint-Venant equations) inversion in space, wherein the momentum equation is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. The fuzzy rule based model is developed on fuzzification of a new mathematical model for wave velocity, the derivational details of which are given. The advantages of the fuzzy control algorithm, over other conventional control algorithms, are described. It is transparent and intuitive, and no linearizations of the governing equations are involved. Timing of the algorithm and method of computation are explained. It is shown that the tuning is easy and the computations are straightforward. The algorithm provides stable, realistic and robust outputs. The disadvantage of the algorithm is reduced precision in its outputs due to the approximation inherent in the fuzzy logic. Feed back control logic is adopted to eliminate error caused by the system disturbances as well as error caused by the reduced precision in the outputs. The algorithm is tested by applying it to water level control problem in a fictitious canal with a single pool and also in a real canal with a series of pools. It is found that results obtained from the algorithm are comparable to those obtained from conventional control algorithms.

Effects Of 5' Flanking Sequences And Changes In The 5' Internal Control Region On The Transcription Of Rice Transfer-Rna Gcc Cly Gene

A stretch of 71 nucleotides in a 1.2 kilobase pair Pst I fragment of rice DNA was identified as tRNA~ gene by hybridization and nucleotide sequence analyses. The hybridization of genomic DNA with the tRNA gene showed that there are about 10 glycine tRNA genes per diploid rice genome. The 3' and 5' internal control regions, where RNA polymerase III and transcription factors bind, were found to be present in the coding sequence. The gene was transcribed into a 4S product in an yeast cell-free extract. The substitution of 5' internal control region with analogous sequences from either M13mpl9 or M13mpl8 DNA did not affect the transcription of the gene in vitro. The changes in three highly conserved nucleotides in the consensus 5' internal control region (RGYNNARYGG; R = purine, Y = pyrimidine, N = any nucleotide) did not affect transcription showing that these nucleotides are not essential for promotion of transcription. There were two 16 base pair repeats, 'TGTTTGTTTCAGCTTA' at - 130 and - 375 positions upstream from the start of the gene. Deletion of 5' flanking sequences including the 16 base pair repeat at - 375 showed increased transcription indicating that these sequences negatively modulate the expression of the gene.

The Alpha-Method Direct Transcription In Path Constrained Dynamic Optimization

Numerically discretized dynamic optimization problems having active inequality and equality path constraints that along with the dynamics induce locally high index differential algebraic equations often cause the optimizer to fail in convergence or to produce degraded control solutions. In many applications, regularization of the numerically discretized problem in direct transcription schemes by perturbing the high index path constraints helps the optimizer to converge to usefulm control solutions. For complex engineering problems with many constraints it is often difficult to find effective nondegenerat perturbations that produce useful solutions in some neighborhood of the correct solution. In this paper we describe a numerical discretization that regularizes the numerically consistent discretized dynamics and does not perturb the path constraints. For all values of the regularization parameter the discretization remains numerically consistent with the dynamics and the path constraints specified in the, original problem. The regularization is quanti. able in terms of time step size in the mesh and the regularization parameter. For full regularized systems the scheme converges linearly in time step size.The method is illustrated with examples.