Amirmehdi Yazdani

The paper analyses the Electromagnetic Transient (EMT) stability analysis of a modified Medium Voltage (MV). This Microgrid (MG) comprises a Diesel Generator (DG) and a Photovoltaic (PV) source under balanced load conditions. The system model was operated in a stable grid-connected mode with only DG's, but the authors proposed a new system with DG and PV in an islanded operation scenario. Islanded MG must operate in a stable mode where controllers can take prompt action in the fault event and make the system more reliable. In this regard, the droop control and PI control manage DG's active power and PV system's power operation, respectively. The Western Electricity Coordination Council (WECC) plant control is utilised as the benchmark model with PV only for a large-scale PV plant model. The control parameters are optimised for both machines to improve the system's dynamic response. The effectiveness and robustness of the MV islanded system are examined under different operating conditions through extensive simulation studies in the DigSILENT PowerFactory software.

Autonomous underwater vehicles (AUVs) are increasingly being used for underwater survey and exploration missions. The expanding mission scope for AUVs highlights the need for a long-endurance operational capability, which mainly depends on propulsion efficiency and battery capacity. For most deployments, AUVs are launched and recovered from a mother ship. While the launch process is relatively straightforward and automated, the recovery process is more risky and conventionally involves man-in-the-loop intervention to ensure that the AUV can be recovered safely. The use of submerged docking stations (DS) permitting battery recharge, data transfer and vehicle recovery offer a means of enabling persistence while also reducing associated deployment/recovery costs and risks. Autonomous docking with a submerged dock towed behind a ship is however complicated by the presence of currents, propeller wash and by the tow behaviour, all of which combine to cause disturbances that create misalignments in pose between the dock and the incoming vehicle. A robust docking guidance system is identified as a core and crucial component for ensuring successful AUV docking. This paper proposes an efficient and universal docking guidance framework that can help to address the limitations of existing docking guidance solutions.

This paper presents a novel real-time quasi-optimal trajectory generator, based on the inverse dynamics in the virtual domain (IDVD) method, to produce a reliable and efficient guidance system for autonomous underwater vehicle (AUV) docking operations. To this end, a challenging docking scenario is defined in a cluttered operating field, encompassing ocean currents and no-fly zones. Using the IDVD method, a trajectory that takes into consideration the hydrodynamic model of the vehicle is generated and the optimality of this trajectory, in regards to mission time and energy expenditure of the vehicle, is considered. Computer simulations demonstrate that the IDVD-based strategy enables the guiding of a vehicle into the dock by satisfying the final boundary conditions of the dock's position and orientation. Generated trajectories are feasible, smooth and realizable using for the vehicle's low-level auto-pilot module. In terms of computation, the solution is suitable for real-time implementation that incorporates uncertainty handling of the operating environment. For further analysis, the generated trajectory is evaluated on a high fidelity AUV simulator. The result of this latter test also demonstrates applicability of the utilized IDVD method for optimization of docking trajectories.

Increasing the level of autonomy facilitates a vehicle in performing long-range operations with minimum supervision. This paper shows that the ability of Autonomous Underwater Vehicles (AUVs) to fulfill mission objectives is directly influenced by route planning and task assignment system performance. This paper proposes an efficient task-assign route-planning model in a semi-dynamic network, where the location of some waypoints can change over time within a target area. Two popular meta-heuristic algorithms, biogeography-based optimization (BBO) and particle swarm optimization (PSO), are adapted to provide real-time optimal solutions for task sequence selection and mission time management. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of Monte Carlo simulation trials are undertaken. The results of simulations demonstrate that the proposed methods are reliable and robust, particularly in dealing with uncertainties and changes in the operations network topology. As a result, they can significantly enhance the level of vehicle's autonomy, enhancing its reactive nature through its capacity to provide fast feasible solutions.

This paper presents an intelligent strategy, based on the combination of the fuzzy logic and imperialist competitive algorithm (ICA), to cope the problem of speed tracking in stepper motor. An optimal Fuzzy-PID controller (FPID), on the other hand, is designed by using ICA and its performance is examined under different circumstances. Considering the results of simulations, done in MATLAB Simulink, expresses the merits of the proposed methodology in offering an acceptable flexibility and valid performance in dealing with nonlinear and uncertain systems.

In this paper, imperialist competitive algorithm is applied to the problem of finding the optimum value for an online PI controller. The objective is to design an adaptive controller for temperature control of non-isothermal reactor with respect to the set point variations. The gain scheduling method is employed to construct an online PI controller. The PI controller is designed in such a way that it minimizes the sum of square error. Fixed gain PI controllers are also implemented on process plant. The results among former method and non-dynamic approaches when compared indicated that the proposed model offers a remarkable performance.