Towards Communication UAV-Based: Improving Throughput By Optimum Trajectory And Power Allocation

It is predicted that the use of unmanned aerial vehicles (UAVs) in communication systems will be more extensive in future generations of wireless telecommunication networks, due to their facilitating advantages. In this paper, a UAV-based wireless communication system is considered in which a UAV is employed as a relay to connect two ground users. These two disconnected users make a communication pair. Our aim is to maximize the minimum achievable information rate for the communication link between the transmitter and receiver, by cooperatively optimizing UAV trajectory and transmitter and source power allocation. Motivated by the above, we formulate the optimization problem. The solving process is complicated because of the non-convexity of the formulated problem. To overcome this difficulty, we convert the main problem to some sub-problems by fixing some constraints and solve them with iterative algorithms such as successive convex optimization and reach the solution for the main problem. Simulation results show the capability of the proposed algorithm.

The Optimal Adaptive-Based Neurofuzzy Control of the 3-DOF Musculoskeletal System of Human Arm in a 2D Plane

Each individual performs different daily activities such as reaching and lifting with his hand that shows the important role of robots designed to estimate the position of the objects or the muscle forces. Understanding the body’s musculoskeletal system’s learning control mechanism can lead us to develop a robust control technique that can be applied to rehabilitation robotics. The musculoskeletal model of the human arm used in this study is a 3-link robot coupled with 6 muscles which a neurofuzzy controller of TSK type along multicritic agents is used for training and learning fuzzy rules. The adaptive critic agents based on reinforcement learning oversees the controller’s parameters and avoids overtraining. The simulation results show that in both states of with/without optimization, the controller can well track the desired trajectory smoothly and with acceptable accuracy. The magnitude of forces in the optimized model is significantly lower, implying the controller’s correct operation. Also, links take the same trajectory with a lower overall displacement than that of the nonoptimized mode, which is consistent with the hand’s natural motion, seeking the most optimum trajectory.

Trajectory optimisation of satellite launch vehicles using network flow-based algorithm

ABSTRACTA trajectory optimisation procedure is addressed to generate a reference trajectory for Satellite Launch Vehicles (SLVs). Using a grid-based discrete scheme, a Modified Minimum Cost Network Flow (MCNF)-based algorithm over a large-scale network is proposed. By using the network grid around the Earth and the discrete dynamic equations of motion, the optimum trajectory from a launch point to the desired orbit is obtained exactly by minimisation of a cost functional subject to the nonlinear dynamics and mission constraints of the SLV. Several objectives such as the flight time and terminal conditions may be assigned to each arc in the network. Simulation results demonstrate the capability of the proposed algorithm to generate an admissible trajectory in the minimum possible time compared with previous works.

Robust Optimum Trajectory Design of a Satellite Launch Vehicle in the Presence of Uncertainties

In this paper, a robust optimization method is developed to solve the Satellite Launch Vehicle (SLV) trajectory design problem in the presence of uncertainties. Given these uncertainties in the actual SLV ascent trajectory, it is important to find an optimal trajectory that is resistant to these uncertainties, as it results in increased flight performance, reduced steering-control system workload and increased SLV reliability. For this purpose, the optimization problem is first considered by applying to maximize the payload mass criterion as an objective function and three-dimensional equations of motions as the governing constraints. Then by adding mean and standard deviation parameters of uncertainties, the robust optimizer model is developed and the genetic algorithm is used to execute the model. Monte Carlo simulation is also used to analyze the results of uncertainties and its continuous feedback to the optimizer model. Finally, an optimal trajectory is obtained that is robust to the uncertainties effects such as aerodynamic coefficients, dry mass and thrust errors of the SLV. The results of the simulation show the validity of this claim.

Further Research on an Anti-Swing Control System for Overhead Cranes

A method of reducing load oscillations that occur when overhead crane reaches destination position is presented in the article. The use of control drive scheme of crane bridge and trolley that ensures a smooth phase trajectory transition of the load to the optimum trajectory in accordance with Pontryagin's maximum principle is proposed. Mentioned control system changes the magnitude or direction of the traction force at the moment when the load is located above the destination. It is found that the degree of change of the traction force depends on the hoisting rope deviation angle from vertical. This study was conducted in order to provide more accurate and fast handling of loads by overhead crane.