In this paper, we focus to find desired node position in indoor environments using a sequence of observations and user movement records. For this purpose, we first record the user's movements in indoor environments by defining a set of states and several matrices, which are Viterbi inputs. To record the fingerprints of the environment, we move across the entire coordinates of the building to collect and record the fingerprints of different places. In the online phase, we use the Weighted K-Nearest Neighbors (WKNN) algorithm in parallel to check the accuracy of both WKNN and Viterbi algorithms and to correct the WKNN behavior by Viterbi. During this phase, an experimental node is inserted into the environment and moves in the desired direction by determining the destination. The proposed method calculates the current location of the node and its most probable location in the next step. The results of the implementation and testing of the proposed algorithm in the Faculty of Engineering, Arak University, show the optimal performance of the proposed idea for predicting the location and PATH of the node.

In this article, the linear quadratic regulator method (LQR) for voltage control of a linear time-varying model of a robot is used to design an online adaptive optimal stable controller to trace the robot arm PATH. Normally, offline solving of Riccati differential equations in backward with final conditions for linear time-varying system or converting the Riccati differential equation to an algebraic one in linear time-invariant system is inevitable in LQR. However, in this paper, the differential Riccati equations are considered as the adaptation laws along with a voltage control strategy to be solved online in forward method with initial conditions. Choosing a proper Lyapunov function guarantees the asymptotic stability of the TRACKING. Furthermore, parametric model uncertainties such as mass parameter variation and external disturbances which affect the dynamics of the model are also taken into account. Simulation results show the energy used by dc motors of the voltage optimal control strategy is less than that of the torque control strategy and as good as the classical PID one. The superior performance of the voltage optimal control over torque control strategy is also shown in presence of disturbance.

Based on learning control methods and computational intelligence, control of quantum systems is an attractive field of study in control engineering. What is important is to establish control approach ensuring that the control process converges to achieve a given control objective and at the same time it is simple and clear. In this paper, a learning control method based on genetic quantum controller approach is presented. For TRACKING a time variant function trajectory, in a closed quantum system, the presented controller is used. For this purpose a constrained optimization problem, based on minimization of difference between a given trajectory and system states subject to an iteration relation of the dynamical solution be satisfied, is designed. According to high convergence rate in quantum genetic algorithm, a quantum genetic algorithm for solving the optimization problem is used. A stochastic measure for observation the initial population is used. Efficient an optimal TRACKING, with at least TRACKING errors and at least learned chattering are advantages of the presented method. A couple of examples for demonstrating the advantages are simulated. Simulation results reflect the good performance of the proposed method for controlling the quantum systems.

This paper addresses the dynamic PATH planning for two mobile robots in unknown environment with obstacle avoidance and moving target TRACKING. These robots must form a triangle with moving target. The algorithm is composed of two parts. The first part of the algorithm used for formation planning of the robots and a moving target. It generates the desired position for the robots for the next step. The second part is designed as the PATH planning for mobile robots. In this part desired trajectory of the robots for reaching the desired position of formation is generated. The potential field method is used to PATH planning for the robots. This method enables the robot to achieve these tasks: to avoid obstacles, and to make ones way toward its new position. Finally, the effectiveness of the proposed algorithm is demonstrated through simulations.

In this article, the modified dynamic PATH-planning algorithm is designed. The problem of overlapped obstacles (e. g., forbidden flight zones) is solved while the pervious dynamic PATH planning methods could not response to this problem. This modified introduced algorithm can be applied in the complex environment and complicated applications without any limitation on the states of obstacles and targets while the pervious dynamic PATH planning algorithm stops working when UAVs is inside the target circle. In this article, by considering a virtual obstacle, formation flying PATH planning is introduced. Numerical simulations are performed to verify the feasibility and ability of this proposed algorithm. This newly proposed algorithm performed for coordinated and group target TRACKING of multiple UAVs.

In this paper, dynamic modeling, optimal PATH planning and control scheme on a redundant parallel cable robotis presented. PATH planning in parallel robots necessitates the consideration of robot’s kinematics to discern the singularities in the workspace. Also, dynamics analysis is required to consider actuation constraints. To this end, kinematics and dynamics of cable driven redundant parallel robot is derived. In this modeling, cables are assumed to be rigid with negligible mass and hence, tension and sagging along the cable are neglected. Next, a sampling-based algorithm upon rapidly-exploring random tree is developed to increase the convergence rate. In this scheme, distance, epochs and safety are considered as optimization constraints. To evaluate the performance of the proposed algorithm in collision avoidance, a number of obstacles have been considered too. TRACKING of the planned PATH has been handled using a feed-forward controller in the presence of obstacles. Regarding the redundancy feature of robot, a redundancy resolution scheme is considered for optimal force distribution. PATH planning and control algorithms are implemented on the RoboCab (ARAS Lab.) and experimental results reveal the efficiency of the proposed schemes.

In this paper, electric power production using airborne systems (kites) has been investigated. In the first step, an appropriate model is extracted to describe the behavior of airborne systems. Based on this model, a new PATH planning algorithm is proposed for the airborne system in the traction phase. Then, in order to achieve the proper operation, TRACKING the desired PATH and thus extracting optimal wind energy, a robust controller based on the sliding mode approach is designed in the presence of variations in atmospheric parameters and uncertainties in the system model. In the proposed method, the control strategy is obtained based on the speed vector angle of the airborne. In the proposed approach, six target points are used for the PATH designing of the kite motion in the traction phase, which increases the precision and flexibility of the designed PATH. Furthermore, the effect of adjusting the shape of the flight PATH of the airborne system during the traction phase on the system performance and extraction of the maximum wind force is also investigated.