IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

In this paper the design of robust Active Queue Management (AQM) for congestion control in computer networks is presented. The Ant Colony Optimization (ACO) method is used to tune the parameters of PID controller subject to H-infinity constraints. The nonlinear dynamic model for multiple TCP flows control is developed based on fluid-flow theory. The designed controller provides good tracking performance in the presence of wide range of system parameter uncertainty. NS2 package is used to perform the nonlinear simulation of the system.

Hexagonal hexapod robot is a flexible mechanical robot with six legs. It has the ability to walk over terrain. The hexapod robot likes insect so it has the same periodic gaits. These gaits are tripod, wave and ripple gaits. Hexapod robot needs to stay statically stable at all the times during each gait in order not to fall with three or more legs continuously contacts with the ground. The safety static stability walking can be indicated by the stability margin. In this paper we based on the forward, inverse kinematics for each hexapod's leg to simulate the hexapod robot model walking for all periodic gaits and the geometry in order to derive the equations of the sub-constraint workspaces for each hexapod's leg. They are defined as the sub-constraint workspaces volumes when the legs are moving without collision with each other and they are useful to keep the legs stable from falling during each gait. A smooth gait was analyzed and enhanced for each hexapod's leg in two phases, stance phase and swing phase. The equations of the stability margins are derived and computed for each gait. The simulation results of our enhanced path planning of the hexapod robot approach whish's include all the gaits are statically stable and we are compared between all stability margins for each gait. In addition, our results show clearly that the tripod gait is the fastest gait while the wave and the ripple gaits are more stable than the tripod gait but the last one has less peaks of stability margins than others.

Accurate on-line estimates of critical system states and parameters are needed in a variety of engineering applications, such as condition monitoring, fault diagnosis, and process control. In these and many other applications it is required to estimate a system variable which is not easily accessible for measurement, using only measured system inputs and outputs. The classical identification methods, such as least-square method, are calculus-based search method. They have many drawbacks such as requiring a good initial guess of the parameter and gradient or higher-order derivatives of the objective function are generally required also there is always a possibility to fall into a local minimum. In this paper we develop on-line, robust, efficient, and global optimization identification for parameters estimation based on genetic algorithms. The simulation results show that the proposed algorithm is very fast to find and adapt the estimated parameters.

In this paper, a mobile manipulator consisting of four degrees of freedom (4-DOF) robotic manipulator mounted at the top of a mobile platform (front point) of a mobile robot with four differential drive wheels is presented. A mobile manipulator combines the dexterous manipulator capability offered by fixed-based manipulators and the mobility offered by the mobile robot. The work involves the modeling of mobile manipulator robot and using the partial feedback linearization approach. The central idea is to algebraically transform nonlinear systems dynamics into partially linear form, so that linear control techniques can be applied to control on the wheel mobile manipulator robot in order to track any trajectory such as an ellipse, circle....etc, without violating the non holonomic constraints. However, and in order to consider the uncertainty in system parameters and the effects of the external disturbances a nonlinear PID controller is proposed in this work. The results demonstrate a good ability of the designed nonlinear PID controller in regulating the mobile robot to track the desired path in the presence of the external disturbances and the uncertainty in system parameters

Nowadays congestion in computer networks is pointed out as an important and a challenging problem. TCP (Transmission Control Protocol) has the mechanism to avoid congestion in computer networks. TCP detects congestion by checking acknowledgements or time-out processing and adjusts TCP window sizes of senders. However, this control method shows low efficiency in communications because it is based on a mechanism that avoid congestion after congestion once appears in computer networks. TCP random early detection RED is another popular congestion control scheme. The fundamental idea behind this control algorithm randomly drops the incoming packets proportional to the average queuing length and to keep the queuing length to a minimum. To achieve high efficiency and high reliability of communications in computer networks, many control strategies based on advanced control theories have been introduced to tackle the congestion problem. Model Predictive Control (MPC) is the only practical control method that takes account of system constraints explicitly, and the only 'advanced control' method to have been adopted widely in industry. MPC is a model-based method which uses online optimization in real time to determine control signals. The solution to optimization problem is usually formulated with the help of a process model and measurements. At each control interval, an optimization algorithm attempts to determine the plant dynamics by computing a sequence of control input values satisfying the control specifications. In this work, a planning strategy based on MPC will be developed for congestion control problem. A "preset controllers" approach will be introduced for such application. The effectiveness of considered controller will assessed in terms of how well it could show good tracking performance, maximizing the utilization of the available bandwidth and to what extent it could cope with system uncertainties.

The Recursive Least Squares (RLS) is usually utilized in control applications as in self-tuning strategy to estimate the plant discrete-time transfer function. Furthermore, it can be used as a tool to continuously monitoring the operating condition of the plant under control. However, in such applications, the RLS should be always in a "wake up" state so that it can estimate, in a few sampling time, the plant transfer function after any abrupt change in its dynamic. In this work, two modifications to the standard RLS are presented. The first modification is called the “switching forgetting factor" while the other is called the" resetting covariance matrix”. The two modifications are applied, under LabVIEW environment, on-line to estimate the proper transfer function of a DC motor as an example to show their capabilities to monitor the motor operation. It is found that with these modifications, the RLS can estimate the plant transfer function much faster in comparison to the standard RLS algorithm.

This work introduces the implementation of particle system to be simulated to work as a group of unmanned mobile robots (swarm robots). These robots are able to locate a specified target in the predefined environment with high efficiency when driven by an optimized Particle Swarm Optimization (PSO) algorithm. The application of the particle system to the mobile robots to search for a target in the environment is called Collective Robotics Search (CRS) problem. The main benefit of this application is to evolve better solutions than using single robot through the collective interaction of all robots between them to achieve the searching task successfully. Particle system has been chosen in this work to employ the mobile robots in the CRS problem due to its simplicity and easy to implement. To measure the performance of this simulation, a simple obstacle free environment will be used to implement behaviors of the group of mobile robots when those robots are used to search for a single target. The results of this work show that applying PSO to a CRS problem in off-line and on-line approaches are efficient in terms of minimum error and also minimum number of iterations during the evolutionary process.