In the last two decades, many researchers have focused on the problem of automation of vehicles, and many research has been devoted to solving the challenges posed by this area. One of the important aspects in this area is the problem of localizing the vehicle and mapping the environment simultaneously in an unknown environment, which is briefly referred to as SLAM. So far, many methods have been proposed to solve this problem, but few of these researches have been implemented on the platform of collaborative robots. In this paper, SLAM problem is extended to multi robot platform by employing extended kalman filter. Due to lack of knowledge about the measurement noise covariance, the elements of this matrix adapted according to the actual data received from the sensor by employing particle swarm optimization technique. Then, to solve this problem in the DYNAMIC environment, probability hypothesis density filter is used to track the DYNAMIC objects in the field of view of sensors. Finally, the performance of the ALGORITHM is evaluated in a MATLAB environment.

In today's economy, manufacturing plants must be able to operate efficiently and respond quickly to changes in the product mix and demand. [1] Layout design has a significant impact on manufacturing efficiency. Initially, it was treated as a static decision but due to improvements in technology, it is possible to rearrange the manufacturing facilities in different scenarios. The Plant layout affects on the total cost in the industry. Nowadays DYNAMIC layout is becoming an important issue. DYNAMIC layout is the different layout at different time periods to satisfy the needs of industry, due to change in product, or reduced product life cycle, or change in demand. Layout problem is a quadratic assignment problem, and for larger size problems it becomes impossible to be solved. So, for solving this problem Meta heuristic ALGORITHMs are used. In this paper, DYNAMIC layout problem is solved using Genetic ALGORITHM. This DYNAMIC Problem is restricted up to two-time periods only.

A novel nonlinear controller is proposed to track active and reactive power for a Brushless Doubly-Fed Induction Generator (BDFIG) wind turbine. Due to nonlinear DYNAMICs and the presence of parametric uncertainties and perturbations in this system, sliding mode control is employed. To generate a smooth control signal, DYNAMIC sliding mode method is used. Uncertainties bound is not required in the suggested ALGORITHM, since the adaptive gain in the controller relation is used in this study. Convergence of the sliding variable to zero and adaptive gain to the uncertainty bound are verified using Lyapunov stability theorem. The proposed controller is evaluated in a comprehensive simulation on the BDFIG model. Moreover, output performance of the proposed control ALGORITHM is compared to the conventional and second-order sliding mode and proportional-integral-derivative (PID) controllers.

A large number of ALGORITHMs have been developed for optimization of mining limits in open pit boundaries, however, little amount of research is available for underground cases. This is mostly due to variability of the underground mining methods, complexity of economic factors in underground mines and lack of acceptability of computer ALGORITHMs among the underground mine design practitioners. This paper presents a new ALGORITHM to optimize ultimate stope geometry, using the DYNAMIC programming technique. The ALGORITHM employs a conventional 20 economic block model, called the "primary model". The minimum stope height and length are imposed to the primary to construct intermediate and final models. The DYNAMIC programming ALGORITHM is then applied on the final model to maximize the stope total net value. The task is performed through a recursive formula, which consists of two criteria. The proposed DYNAMIC programming ALGORITHM is best suited for vein type orebodies using any type of underground mining methods.    

In this study, a DYNAMIC based control ALGORITHM for six-link quadruped locomotion is proposed. Up to now, a lot of researches have been conducted in the field of quadruped locomotion but most of the researches are based on the detailed modeling of a robot and its surrounding. Such methods are not able to generate a stable locomotion when the surrounding changes. However, the main characteristic of vertebral motion is the adaptability of their motions. So this is important to propose a control ALGORITHM based on the robot DYNAMICs. The ALGORITHMs that can guarantee the stability are classified to two categories of DYNAMIC based and trajectory based methods. The trajectory based ALGORITHMs need detailed information about gait which is not necessarily available. But the DYNAMIC based ALGORITHMs use some geometric constraints to reach a stabilizer controller. These geometric constraints generate the proper gaits. So in this study by employing the DYNAMIC based control ALGORITHM, we proposed a controller for generating the Trot and Pace gait on a straight and flat path for quadruped robot locomotion. Given that the quadruped robot has four degrees of freedom, three geometric constraints are needed to provide rhythmic locomotion. In this study, the stability of quadruped locomotion has been proved using the Poincaré return map.

This article investigates the problem of simultaneous attitude and vibration control of a flexible spacecraft to perform high precision attitude maneuvers and reduce vibrations caused by the flexible panel excitations in the presence of external disturbances, system uncertainties, and actuator faults. Adaptive integral sliding mode control is used in conjunction with an attitude actuator fault iterative learning observer (based on sliding mode) to develop an active fault tolerant ALGORITHM considering rigid-flexible body DYNAMIC interactions. The discontinuous structure of fault-tolerant control led to discontinuous commands in the control signal, resulting in chattering. This issue was resolved by introducing an adaptive rule for the sliding surface. Furthermore, the utilization of the sign function in the iterative learning observer for estimating actuator faults has not only enhanced its robustness to external disturbances through a straightforward design, but has also led to a decrease in computing workload. The strain rate feedback control ALGORITHM has been employed with the use of piezoelectric sensor/actuator patches to minimize residual vibrations caused by rigid-flexible body DYNAMIC interactions and the effect of attitude actuator faults. Lyapunov's law ensures finite-time overall system stability even with fully coupled rigid-flexible nonlinear DYNAMICs. Numerical simulations demonstrate the performance and advantages of the proposed system compared to other conventional approaches.

In data grid, using reservation is accepted to provide scheduling and service quality.Users need to have an access to the stored data in geographical environment, which can be solved by using replication, and an action taken to reach certainty. As a result, users are directed toward the nearest version to access information. The most important point is to know in which sites and distributed system the produced versions are located. By selecting a suitable place for versions, the versions having performance, efficiency and lower access time are used. In this study, an efficient method is presented to select the best place for those versions created in data grid by using the users’ firefly ALGORITHM which is compared with two ALGORITHMs. Results show that firefly ALGORITHM has better performance than others.

Haplotype block portioning is one of the most important problems in computational biology and bioinformatics. In this paper a new efficient ALGORITHM is presented for haplotype block partitioning based on haplotype diversity. The ALGORITHM can be performed in polynomial time complexity with regard to the number of haplotypes and SNPs. Our ALGORITHM is compared with the other well known ALGORITHMs. The obtained results show the efficiency and the better performance of our ALGORITHM.