This paper proposes a fault location technique on three-terminal transmission lines based on a solution to the optimization problem. The cuckoo search (CS) algorithm is improved and applied to solve this fault location. The CS algorithm modifies the constant step size into the chaotic step size to achieve the globally optimal solution through the chaotic behavior, called the Chaos CS (CCS) algorithm. The obtained numerical results of applying the CCS algorithm are compared to those of applying the CS algorithm and genetic algorithm (GA). The comparison shows the effectiveness of the proposal of the fault location on the three-terminal transmission lines.

This paper proposes a novel technique for unknown parameter estimation of a detailed solar photovoltaic (PV) cell model based on the artificial bee colony (ABC) algorithm and particle swarm optimization (PSO) algorithm. This combination allows to balance between the exploration and exploitation abilities of each algorithm for achieving a good optimization performance. The detailed solar PV cell model is the double-diode model with seven unknown parameters which are estimated by using the hybrid ABC-PSO algorithm. The numerical results of the parameter estimation using the hybrid ABC-PSO algorithm are compared with those using the PSO, advanced PSO and ABC algorithms showing that the convergence speed and value of the hybrid ABC-PSO algorithm are always better than the PSO, advanced PSO and ABC algorithms.

This paper introduces an adaptive tuning method for the controllers of a 4 degrees-of-freedom autonomous underwater vehicle. The proposed scheme consists of two control loops, one for position control and an inner one for velocity control. The gains of the controller are determined on-line, according to the position/velocity errors. Using the proposed adaptive architecture, the uncertainties in the parameters of the system are addressed and the system is able to operate when hydrodynamic disturbances are present. The complexity of the fixed gain tuning procedure is also greatly decreased for underwater vehicles when the algorithm suggested here is used. Experimental results with the Nessie VII AUV show that the adaptive controller is beneficial for underwater vehicles. Finally it is shown that the current approach reduces the energy consumption of the system.

This paper proposes a general framework to control underwater vehicle-manipulator systems from external sensors. The design of the control law follows several constraints and criteria to optimize the overall behavior. The task is defined in the sensor space, that is suited for visual servoing from camera or sonar images. The control input distribution between the arm and the body is considered and separated between the approach and the intervention phases. The proposed framework is validated in a realistic simulation environment, imposing constant and varying disturbances at the velocity level.

A new control concept is proposed for an underwater vehicle-manipulator system (UVMS) where the desired target is defined as a region boundary rather than a point or a region. For a mapping of the uncertain persistent effects i.e.: the restoring forces, a least-squares estimation algorithm along with the inverse Jacobian matrix is utilized in the adaptive control law. The unit quaternion representation is used for the attitude representation. The stability analysis is carried out using a Lyapunov-like function. Simulation results are presented to assess the effectiveness of the proposed control scheme.

A new control concept is proposed for an autonomous underwater vehicle (AUV) where the desired target is defined as a boundary rather than a point or a region. The inverse Jacobian is utilized in the adaptive control law for compensation of the persistent effects i.e.: the restoring forces. The unit quaternion representation is used for the AUVs attitude representation. The stability analysis is carried out using a Lyapunov-like function. Simulation results are presented to access the effectiveness of the proposed control scheme.

Current generation unmanned underwater vehicles, equipped with robotic manipulators, are teleoperated and consequently place a large workload burden on the human operator. A greater degree of automation could improve the efficiency and accuracy with which underwater tasks are carried out. These tasks can involve manipulator motion that is both unconstrained and/or constrained. For unconstrained motion, where a trajectory requires following, a prerequisite is good joint angle control. An adaptive self-tuning pole-placement controller is used for joint angle control. Practical results show the benefits compared to the conventional fixed-gain control. For constrained motion, simultaneous controls of position and force are often required. An adaptive hybrid position/force controller is proposed and compared to a fixed-gain version. Simulation and practical results illustrate the merits and drawbacks of each scheme.