Experimental Validation of a Model-Free High-Order Sliding Mode Controller with Finite-Time Convergence for Trajectory Tracking of Autonomous Underwater Vehicles

Several control strategies have been proposed for the trajectory tracking problem of Autonomous Underwater Vehicles (AUV). Most of them are model-based, hence, detailed knowledge of the parameters of the robot is needed. Few works consider a finite-time convergence in their controllers, which offers strong robustness and fast convergence compared with asymptotic or exponential solutions. Those finite-time controllers do not permit the users to predefine the convergence time, which can be useful for a more efficient use of the robot’s energy. This paper presents the experimental validation of a model-free high-order Sliding Mode Controller (SMC) with finite-time convergence in a predefined time. The convergence time is introduced by the simple change of a time-base parameter. The aim is to validate the controller so it can be implemented for cooperative missions where the communication is limited or null. Results showed that the proposed controller can drive the robot to the desired depth and heading trajectories in the predefined time for all the cases, reducing the error by up to 75% and 41% when compared with a PID and the same SMC with asymptotic convergence. The energy consumption was reduced 35% and 50% when compared with those same controllers.

A Low Cost Oscillating Membrane for Underwater Applications at Low Reynolds Numbers

Active surface morphing is a nonintrusive flow control technique that can delay separation in laminar and turbulent boundary layers. Most of the experimental studies of such control strategy have been carried out in wind tunnels at low Reynolds numbers with costly actuators. In contrast, the implementation of such a control strategy at low cost for an underwater environment remains vastly unexplored. This paper explores active surface morphing at low cost and at low Reynolds for underwater applications. We do this with a 3D printed foil submerged in a water tunnel. The suction surface of the foil is covered with a magnetoelastic membrane. The membrane is actuated via two electromagnets that are positioned inside of the foil. Three actuation frequencies (slow, intermediate, fast) are tested and the deformation of the membrane is measured with an optosensor. We show that lift increases by 1%, whilst drag decreases by 6% at a Strouhal number of 0.3, i.e., at the fast actuation case. We demonstrate that surface actuation is applicable to the marine environment through an off the shelf approach, and that this method is more economical than existing active surface morphing technologies. Since the actuation mechanism is not energy intensive, it is envisioned that it could be applied to marine energy devices, boat appendages, and autonomous underwater vehicles.

Test Results of the Long Baseline Navigation Solutions under a Large a Priori Position Uncertainty

A method is proposed for long baseline navigation of autonomous underwater vehicles (AUV) to be used in the case of a large a priori position uncertainty. The new modified method is based on the iterated Kalman filter (IKF) working with different initial linearization points. The final solution is calculated by clustering and weighting the IKF results. This approach allows position estimates to be determined in accordance with the global maximum of posteriori probability density of coordinates. The test results obtained with the use of three beacons and an underwater vehicle are presented.

Artificial Intelligence Search Strategies for Autonomous Underwater Vehicles Applied for Submarine Groundwater Discharge Site Investigation

In this study, a set of different search strategies for locating submarine groundwater discharge (SGD) are investigated. This set includes pre-defined path planning (PPP), adapted random walk (RW), particle swarm optimisation (PSO), inertia Levy-flight (ILF), self-organising-migration-algorithm (SOMA), and bumblebee search algorithm (BB). The influences of self-localisation and communication errors and limited travel distance of the autonomous underwater vehicles (AUVs) on the performance of the proposed algorithms are investigated. This study shows that the proposed search strategies could not outperform the classic search heuristic based on full coverage path planning if all AUVs followed the same search strategy. In this study, the influence of self-localisation and communication errors was investigated. The simulations showed that, based on the median error of the search runs, the performance of SOMA was in the same order of magnitude regardless the strength of the localisation error. Furthermore, it was shown that the performance of BB was highly affected by increasing localisation errors. From the simulations, it was revealed that all the algorithms, except for PSO and SOMA, were unaffected by disturbed communications. Here, the best performance was shown by PPP, followed by BB, SOMA, ILF, PSO, and RW. Furthermore, the influence of the limited travel distances of the AUVs on the search performance was evaluated. It was shown that all the algorithms, except for PSO, were affected by the shorter maximum travel distances of the AUVs. The performance of PPP increased with increasing maximum travel distances. However, for maximum travel distances > 1800 m the median error appeared constant. The effect of shorter travel distances on SOMA was smaller than on PPP. For maximum travel distances < 1200 m, SOMA outperformed all other strategies. In addition, it can be observed that only BB showed better performances for shorter travel distances than for longer ones. On the other hand, with different search strategies for each AUV, the search performance of the whole swarm can be improved by incorporating population-based search strategies such as PSO and SOMA within the PPP scheme. The best performance was achieved for the combination of two AUVs following PPP, while the third AUV utilised PSO. The best fitness of this combination was 15.9. This fitness was 26.4% better than the performance of PPP, which was 20.4 on average. In addition, a novel mechanism for dynamically selecting a search strategy for an AUV is proposed. This mechanism is based on fuzzy logic. This dynamic approach is able to perform at least as well as PPP and SOMA for different travel distances of AUVs. However, due to the better adaptation to the current situation, the overall performance, calculated based on the fitness achieved for different maximum travel distances, the proposed dynamic search strategy selection performed 32.8% better than PPP and 34.0% better than SOMA.

Development of AUV MONACA - Hover-Capable Platform for Detailed Observation Under Ice –

The melting of ice and changes in ocean currents in Antarctica must be investigated to understand global climate change. In this regard, the volume changes of sea ice and ice shelves, bathymetry, and ocean currents in the Antarctic Ocean must be measured in three dimensions. Therefore, the use of autonomous underwater vehicles (AUVs), which can directly observe under ice, is being considered. The authors developed an AUV named Mobility Oriented Nadir AntarctiC Adventurer (MONACA) to observe sea ice and the lower region of the ice shelf in the Antarctic Ocean. Herein, we describe MONACA and its basic autonomous navigation methods (altitude control, depth control, and waypoint tracking), as well as report the results of a sea experiment conducted in Shimoda Bay, Japan. During the 5-day sea trial, the MONACA successfully measured bathymetry by tracking 15 waypoints in sequence, switching the control criteria in the -axis direction between 3 m depth and 3 m altitude.

DESIGN OF LOW-DRAG AUTONOMOUS UNDERWATER VEHICLES AND FLOW CONTROL

Design of autonomous underwater vehicles (AUV) met the opposite challenges. Their achievable route can be enhanced with drag reduction due to an increase of AUV slenderness. However, blunt short AUV have others operational advantages. The possibility to design low-drag bodies for Reynolds numbers employed by contemporary AUV (2×106<Re<107) is based on a combination of known facts. First, blunt bodies experience a drag crisis associated with laminar-turbulent transition in their boundary layers and some boundary layer suction additionally reduces their drag. Second, the transition can be delayed till much higher Re for bodies without adverse pressure gradients over their forward and medium parts. Suction on sterns of such bodies allows for the very substantial drag reduction. Several body shapes with distributed suction with extremely low slenderness (L/B<1.5) are presented. Their drag coefficients are between 0.007 and 0.02, whereas for ellipsoid of the same slenderness it exceeds 0.08.

Optimal Solutions for Underwater Capacitive Power Transfer

Capacitive power transfer (CPT) has attracted attention for on-road electric vehicles, autonomous underwater vehicles, and electric ships charging applications. High power transfer capability and high efficiency are the main requirements of a CPT system. This paper proposes three possible solutions to achieve maximum efficiency, maximum power, or conjugate-matching. Each solution expresses the available load power and the efficiency of the CPT system as functions of capacitive coupling parameters and derives the required admittance of the load and the source. The experimental results demonstrated that the available power and the efficiency decrease by the increasing of the frequency from 300 kHz to 1 MHz and the separation distance change from 100 to 300 mm. The maximum efficiency solution gives 83% at 300 kHz and a distance of 100 mm, while the maximum power solution gives the maximum normalized power of 0.994 at the same frequency and distance. The CPT system can provide a good solution to charge electric ships and underwater vehicles over a wide separation distance and low-frequency ranges.

ADP based trajectory-tracking control via backstepping method for underactuated AUV with unknown dynamics

This paper investigates trajectory-tacking control problem for underactuated autonomous underwater vehicles (AUV) with unknown dynamics. Different from existing adaptive dynamic programming (ADP) schemes, our proposed control scheme can achieve high-level system stability and tracking control accuracy. Firstly, the backstepping approach is introduced into the kinematic model of underactuated AUV and produces a virtual velocity control which is taken as the desired velocity input of the dynamic model of underactuated AUV. Secondly, the error tracking system is constructed according to the dynamic model of underactuated AUV. Thirdly, the critic neural network and the action neural network are employed to transform the trajectory-tracking control problem into optimal control problem based on policy iteration algorithm. At last simulation results are given to verify the effectiveness of the control scheme proposed in this paper.