**5. Conclusions**

168 Autonomous Underwater Vehicles

Furthermore, if the absolute error between actual and desired position is accumulated over the entire mission for each DoF, a measure of the accuracy of the control system can be obtained. A comparison can be made using Table 1. Here, we can see that by summing together the values obtained from the three translational DoFs, the coupled system improves upon the uncoupled system by 9.75%, while by summing together the values obtained from the three rotational DoFs, the coupled improves upon the uncoupled system by 23.87%. This indicates that the coupled system is superior to the uncoupled system for both translational and rotational motion, when looking at the accumulated absolute error.

Fig. 12. Yaw trajectory.

Fig. 13. Yaw trajectory magnified.

Due to the increased adoption of AUVs for civilian and defence operations, accuracy and reliability are two key factors that enable an AUV to successfully complete its mission. The control system is just one of the various components within the autonomy architecture of an AUV that helps in achieving this goal. Within the control system, the control law should be robust to both external disturbances and model parameter uncertainties, while the control allocation should utilise the various actuators of the vehicle to apply the desired forces to the vehicle while minimising the power expended.

PID control has been successfully implemented on a variety of systems to effectively provide compensation. However, since PID control is better suited to linear models, the level of performance provided by PID control is not to the same standard as other, particularly nonlinear, control schemes when applied to complex nonlinear systems. Sliding mode control has proven to be a control law that is robust to parameter uncertainties, and therefore is a prime candidate for implementation within this context due to the highly complex coupled nonlinear underwater vehicle model. Active utilisation of the coupled structure of this model is what coupled SMC attempts to achieve, such that induced motion in one DoF due to motion in another DoF is adequately compensated for. This is where coupled SMC has a distinct advantage over uncoupled SMC for trajectory tracking applications when multiple DoFs are excited at once.

Various schemes exist for control allocation with the ultimate goal being to apply the desired generalised forces while minimising power consumption, both due to the actuator usage and computational demands. Non-optimal schemes exist where a generalised inverse of the force produced by all actuators is used as the allocation scheme, with the limitation being that there is no functionality to bias actuators under certain operating conditions, such as utilising control surfaces over thrusters during relatively high speed manoeuvring. Quadratic programming incorporates a weighting matrix that can bias control surface usage over tunnel thrusters, and has been implemented both online and offline, with each having advantages and disadvantages. Online optimisation allows for changes to the actuator configuration, such as failures or varied saturation limits, but is computationally demanding. Offline optimisation is less computationally demanding during mission execution, but cannot allow for altered actuator dynamics. A compromise between these schemes is the proposed 2-stage scheme where control surfaces are utilised to their full extent, and the tunnel thrusters used only when needed.

Overall, the goal of the control system is to provide adequate compensation to the vehicle, even in the presence of unknown and unmodelled uncertainties while also minimising power consumption and therefore extending mission duration. Choosing wisely both the control law and the control allocation scheme within the overall control system is fundamental to achieving both of these goals.

**Part 3** 

**Mission Planning and Analysis** 
