In this paper, sliding mode control is used to develop two passive fault tolerant controllers for an AscTec Pelican UAV quadrotor. In the first approach, a regular sliding mode controller (SMC) augmented with an integrator uses the robustness property of variable structure control to tolerate partial actuator faults. The second approach is a cascaded sliding mode controller with an inner and outer SMC loops. In this configuration, faults are tolerated in the fast inner loop controlling the velocity system. Tuning the controllers to find the optimal values of the sliding mode controller gains is made using the ecological systems algorithm (ESA), a biologically inspired stochastic search algorithm based on the natural equilibrium of animal species. The controllers are tested using SIMULINK in the presence of two different types of actuator faults, partial loss of motor power affecting all the motors at once, and partial loss of motor speed. Results of the quadrotor following a continuous path demonstrated the effectiveness of the controllers, which are able to tolerate a significant number of actuator faults despite the lack of hardware redundancy in the quadrotor system. Tuning the controller using a faulty system improves further its ability to afford more severe faults. Simulation results show that passive schemes reserve their important role in fault tolerant control and are complementary to active techniques

This article presents an intelligent fuzzy logic controller (FLC) for controlling single-body heaving wave energy converter (WEC) or what is widely known as “Point Absorber”. The controller aims at maximizing the energy captured from the sea waves. The power take-off (PTO) limitations are addressed implicitly in the fuzzy inference system (FIS) framework. In order to enhance the WEC power capturing bandwidth and make it less susceptible to wave environment irregularities and the system parametric uncertainties, the controller is built to have a self-configurable capability. This also eliminates the need to repeatedly run in-situ tuning procedure of the fuzzy controller or switch between several controllers based on the operating conditions. The fuzzy membership functions (MFs) are optimally tuned using particle swarm optimization (PSO) algorithm. To alleviate the computational burden associated with performing on-line optimization, the fuzzy controller is tuned at a rate significantly lower than the system sampling time. The suggested PSO-FLC has shown promising results compared with the fixed structure fuzzy logic controller (FS-FLC) and other passive control strategies. Several computer simulations were carried out to evaluate the controller effectiveness by applying different sea-states and analyzing the resultant WEC dynamics.

The United Arab Emirates (UAE) has been exploring several renewable and green technologies to help reduce the increasing pollution rates. However, its coarse climate might impose some limitations toward the types of green technologies that can be effectively deployed in the region. In the first part of this work, an improved dynamic model of the Nexa 1.2 kW PEM fuel cell is developed using particle swarm optimization (PSO), and validated using experimental data. The developed model is then used to analyze the effect of the severe climate conditions of the UAE on the performance of the system to evaluate its operational compatibility with the region.

There are many advantages for having collaborative flight of multiple Unmanned Arial Vehicles (UAVs) rather than a single UAV. In this paper, a proposed formation flight algorithm is tested and a Graphical User Interface (GUI) is implemented on the ground station to allow for path planning and flight control. The proposed algorithm is based on a Hierarchal Leader-Follower formation, where the leader has more flexibility and independence, while the follower is completely dependent on the path taken by the leader. The path planning and the proposed formation flight algorithm are implemented on one axis and tested on two quadrotors. The proposed algorithm is implemented on real quadrotors and its ability to track the given path will be shown.