Journal of Control
Year:2008 | Volume:2 | Issue:1|Papers Count:8

In this study we predict air pollution data by using Multi Layer Percepteron, Time Delay Line, Gamma and ANFIS by gradient free learning methods. This paper, using real data for Arak city during Oct 2003, the following pollution parameters are analysed: Co (Carbon Monoxide), PM10 (Particulate Matter).This analysis is carried out in two stages: Predictability analysis using Lyapanov, Exponent, Correlation Dimension and Rescaled Range Analysis (R/S), Prediction using Multi layer perceptron, Time delay line, Gamma and ANFIS. Also, a comparative study is performed using the different methods employed and prediction results are provided to show the effectiveness of the predictions.

In this paper, routing in computer networks is performed using a fuzzy logic strategy. The present network traffic is evenly distributed during the routing. The fuzzy based system will periodically receive the information from the network and based on this information will divert the load to other parts of the network. Simulation results are used to show the performance of the proposed method and comparison results are also provided.

This paper presents nonlinear modeling, simulation, and controller design for wind energy conversion system in Dizbad power plant. The wind power plant scheme consists of a three-phase induction generator that connected to the wind turbine via a fixed ratio gearbox. A static VAR compensator was connected at the generator terminals to regulate its voltage. The induction generator was connected to the utility through a double transmission line. The mechanical power input and terminal voltage output was controlled using blade pitch angle and firing angle of thyristor. An optimal stochastic control is designed for the system based on the optimal stochastic estimator. The performance of the proposed scheme is compared with both output feedback and state feedback controllers with extended Kalman filter. These controllers are applied to the nonlinear system and their performances are recognized under gust and different type of disturbances. The responses of the proposed optimal stochastic controller exhibited a good damping and fast recovery under these disturbances.

In this paper, an optimal Takagi-Sugeno fuzzy control is designed with a novel method called combined discrete and continuous action reinforcement learning algorithm (CDCARLA). The proposed method is implemented for a nonlinear system that is Cart-Pole system. Simulation results show that the proposed method has significant performance. The advantage of CDCARLA is that it does not need system dynamics as well as any other information of power system. It can be said that, this method will consider nonlinear features of power system. It is shown that CDCARLA method can be considered as one of the automatic design technique for designing of controller parameters.

The problem of optimal sensor location for fault detection in Linear Time-Invariant (LTI) systems is considered. A novel characterization of undetectable faults is shown to give rise to a new optimal sensor location problem for fault detection in the presence of L2 disturbances. An algorithm is presented whereby suboptimal solutions to this problem can be determined. A numerical example is presented to illustrate the theory and execution of the algorithm.

In this paper an adaptive controller for the Unified chaotic system is presented. For this purpose, a combination of linear feedbacks with constant and adaptive gains and a nonlinear adaptive state feedback is used. In this way, the Lyapunov's direct method is used to select the suitable non-linear state feedback. Moreover, the asymptotic stability guarantee of the uncertain Unified chaotic system in a stochastic point on the general manifold of its equilibrium points is proved in a lemma by using non-linear feedback controller. Then, the asymptotic stability of the final controlled system in any desired point on the mentioned manifold is guaranteed by adding the linear feedback signals to the prior controlled system. This subject is proved, by using the Lyapunov's indirect method. Finally, the simulation results related to applying the controller to the Unified chaotic system, in different cases, even when simultaneous-changing of the system type and the desired equilibrium point occurs are provided to show the well-acceptable performance of the proposed controller.

In this Paper, an efficient and quick method for inverse kinematic solution of hyper-redundant manipulators is presented. In addition, it minimizes the joints' displacement, energy consumption, and actuators torque while the end effecter reaches the target. Due to the flexibility of the genetic algorithm, it is capable of handling any number of constraints. At the end, the simulation result is compared with a method based on workspace density function. The advantages of new method are fast, optimization of robot function, handling many constraints, independent of joint design, real time, minimum error and ability to control the direction of reaching the target.