Control of an AUV from thruster actuated hover to control surface actuated flight

An autonomous underwater vehicle (AUV) capable of both low speed hovering and high speed flight-style operation is introduced. To have this capability the AUV is over-actuated with a rear propeller, four control surfaces and four through-body tunnel thrusters. In this work the actuators are modelled and the non-linearities and uncertainties are identified and discussed with specific regard to operation at different speeds. A thruster-actuated depth control algorithm and a flight-style control-surface actuated depth controller are presented. These controllers are then coupled using model reference feedback to enable transition between the two controllers to enable vehicle stability throughout the speed range. Results from 3 degrees-of-freedom simulations of the AUV using the new controller are presented, showing that the controller works well to smoothly transition between controllers. The performance of the depth controller appears asymmetric with better performance whilst diving than ascendin

An online learning selection hyper-heuristic for educational timetabling

Examination and course timetabling are computationally difficult real-world resource allocation problems. In 2007, an International Timetabling Competition (ITC) consisting of three classes: (i) examination timetabling, (ii) post enrollment-based, and (iii) curriculum-based course timetabling was organised. One of the competing algorithms, referred to as CPSolver, successfully achieved the first place in two out of these three tracks. This study investigates the performance of various multi-stage selection hyper-heuristics sequencing low-level heuristics/operators extending the CPSolver framework which executes hill climbing and two well-known local search metaheuristics in stages. The proposed selection hyper-heuristic is a multi-stage approach making use of a matrix which maintains transitional probabilities between each low-level heuristic to select the next heuristic in the sequence. A second matrix tracks the probabilities of ending the sequence on a given low-level heuristic. The best configuration for the selection hyper-heuristic is explored tailoring the heuristic selection process for the given timetabling problem class. The empirical results on the ITC 2007 problem instances show that the proposed selection hyper-heuristics can reduce the number of soft constraint violations, producing improved solutions over CPSolver as well as some other previously proposed solvers, particularly, in examination and curriculum-based course timetabling

The performance of vertical tunnel thrusters on an autonomous underwater vehicle operating near the free surface in waves

Underwater vehicles operating near the free surface in waves can experience large forces acting on the body which can cause the vehicle to move undesirably. To overcome these forces, and keep station with minimal disturbance, actuators fitted to the vehicle are used. To develop a suitable controller, the performance of the actuators used must be known. This paper shows that as a vertical tunnel thruster approaches the free surface, the thrust generated decreases. Experimental data is presented and reasons for the reduction in total effective thrust discussed. Further to this, the performance of the Delphin2 AUV (Phillips et al 2010) operating in waves is analysed and suggestions made for improving performance<br/

Experimentally verified depth regulation for AUVs using constrained model predictive control

In the application of an autonomous underwater vehicle a critical requirement is to keep the level of the actuation signals within operational limits to avoid, for example, actuator nonlinearities and reduce peak power consumption. The most common approach to this problem for AUVs that have been deployed is, if required, to trade-off performance in order to keep the actuation signals and power required within the operational limits. This paper addresses depth control of an AUV using model predictive control with constraints on the both the amplitude and rate of change of the entries in the control vector. The model predictive control algorithm is designed by solving a quadratic programming problem in real-time when implemented on an AUV prototype. Experimental test results for depth control are also given and demonstrate that physically relevant constraints on the thrust and actuation power, critical factors for the use of these vehicles, can be achieved. Moreover, there is agreement between the control action used and the underlying physics of a body moving in water

Effect of measurement noise on the performance of a depth and pitch controller using the model predictive control method

In this paper a depth and pitch controller for a hover-capable AUV is designed and implemented in simulation. The effect on controller performance of random Gaussian noise on the feedback signals is evaluated. It has been shown that very small levels of measurement noise will result in the controller performance degrading substantially and behaving in an erratic fashion. A polynomial type filter has been proposed and integrated into the model predictive control algorithm. This modification reduces the effect of the measurement noise substantially and improves controller performance