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Paper Title Pages. Abstract: This paper investigates the problems of delay-dependent passive analysis and control for uncertain stochastic systems with time-varying delay and norm-bounded parameters uncertainties. Delay-dependent stochastic passive condition for the uncertain stochastic time-delay systems is obtained based on Laypunov-Krasovkii functional approach. On the basis of this condition, a delay-dependent passive controller is presented.

Sufficient condition for the existence of desired controller is formulated in terms of linear matrix inequality. Finally, a numerical example is given to illustrate the effectiveness of the proposed method. Abstract: The problem of non-fragile memoryless controller design for a class of uncertain nonlinear stochastic system with time-delay is considered.

Based on Lyapunov candidate and the stochastic Lyapunov stability theory, the sufficient conditions making the closed-loop system robust stable are given and de-rived. All results are given by the form of linear matrix inequality LMI method.

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Numerical example is given to illustrate the effectiveness of the controller designed. The sufficient conditions for the robust stability of the uncertain time-delay system are given. Finally, the simulation results illustrate the effectiveness of the proposed methods. Abstract: In this paper, the problem of stability analysis of uncertain distributed time-delay systems is investigated. Systems with norm-bounded parameter uncertainties are considered. By taking suitable Lyapunov-Krasovskii functional and free weighting matrices, a delay-dependent sufficient condition is derived in terms of linear matrix inequality LMI.

The condition obtained in this paper can be tested numerically very efficiently using interior point algorithms. Abstract: The conservatism of asymptotic stability conditions is considered in terms of linear matrix inequalities for time-varying delay systems. The conservative index is defined to evaluate the conservativeness for both delay-dependent and delay-independent stability conditions.

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The general results on performance analysis are presented based on descriptor system approach. The conservativeness index is defined for time-varying delay system. Blackboard is the university's Virtual Learning Environment, for staff and students.

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Zhang, D. Gu and S. Bharani Chandra, D. Gu and I. Khan, D. Gu and M.

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Berry, J. Howitt, I. During each iteration step, or generation, optimization methods following the examples of the three genetic operators reproduction, crossover and natural behavior of living species. A number of mutation are performing to generate new points in the search space are considered as a populations offsprings , and the chromosomes of population of living creatures inside an artificial these new populations are evaluated via the value of world. Basic Darwinistic propagation in such world fitness which is related to some cost functions.

On enables the fittest to survive. Although some random the basis of these genetic operators and evaluation, effects play an important role, the behavior is not the better new populations of candidate solution are pure coincidental, since the historical information formed. With the above of the fittest. Generate randomly a population of binary strings. Calculate the fitness for each string in the into the spotlight and make them as one of the more population. Create offspring strings by simple GA operators.

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Evaluate the new strings and calculate the fitness Important aspects of GAs are universality and for each string. If the search goal is achieved, or an allowable survive in many different and hostile environments. The designer in method by appeal to this beauty-of-nature argument the search space specifies the choice of a certain alone. Genetic Algorithms are theoretically and length. The algorithms work with a population of strings, searching many peaks in parallel as opposed to a where Mi and mi are the upper and the lower of the single point; use probabilistic transition rules instead parameter ki.

By the second operator, the strings The decoding procedure is the reverse procedure of exchange information via probabilistic decisions. Crossover provides a mechanism for strings to mix In this paper, the fitness and cost function is and match their desirable qualities through a obviously define with the relation: random process.


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The third operator, mutation, enhances an 19 ability of genetic algorithms to find a near-optimal solution. The fitness value is a value at a particular string position.

In the case of reward based on the performance of the possible binary coding, the mutation operator simply flips the solution represented by the string, or it can be state of a bit from 0 to 1 and vice versa. Mutation thought of as how well a PID controller can be should be used sparingly because it is a random tuned according to the string to actually minimize search operator. As said above the convergence of a the tracking error. The better the solution encoded genetic search algorithm is discussed from the by a string chromosome , the higher the fitness.

Design example. Conclusions searching algorithm. A chromosome that has lower quadratic index should be assigning a larger fitness In order to illustrate the effectiveness of the value. Then the genetic algorithm tries to generate proposed approach the following example with better offsprings to improve the fitness.

Therefore, a numerical simulation is given: better PID controller could be obtain via better fitness in genetic algorithms. There are quite a Example. Let us consider the control system shown number of approaches to perform this mapping in Fig. A PD controller would be given to achieve known as fitness techniques.

So, windowing [11], as described in Fig.