The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft

Robust Control For Underwater Vehicle Systems With Time Delays

Presented in this paper is a robust control scheme for controlling systems with time delays. The scheme is based on the Smith controller and the LQG/LTR (Linear Quadratic Gaussian/Loop Transfer Recovery) methodology. The methodology is applicable to undenvater vehicle systems that exhibit time delays, including tethered vehicles that are positioned through the movements of a surface ship and autonomous vehicles that are controlled through an acoustic link. An example, using full-scale data from the Woods Hole Oceanographic Institution’s tethered vehicle ARGO, demonstrates the developments

An improved swarm intelligence algorithms-based nonlinear fractional order-PID controller for a trajectory tracking of underwater vehicles

This paper presents a nonlinear fractional order proportional integral derivative (NL-FOPID) for autonomous underwater vehicle (AUV) to solve the path tracking problem under the unknown disturbances (model uncertainty or external disturbances). The considered controller schemes are tuned by two improved swarm intelligence optimization algorithms, the first on is the hybrid grey wolf optimization with simulated annealing (HGWO-SA) algorithm and an improved whale optimization algorithm (IWOA). The developed algorithms are assessed using a set of benchmark function (unimodal, multimodal, and fixed dimension multimodal functions) to guarantee the effectiveness of both proposed swarm algorithms. The HGWO-SA algorithm is used as a tuning method for the AUV system controlled by NL-FOPID scheme, and the IWOA is used as a tuning algorithm to obtain the PID controller’s parameters. The evaluation results show that the HGWO-SA algorithm improved the minimal point of the tested benchmark functions by 1-200 order, while the IWOA improved the minimum point by (1-50) order. Finally, the obtained simulation results from the system operated with NL-FOPID shows the competence in terms of the path tracking by 1-15% as compared to the PID method

Real-Time Experimental Comparison of Two Depth Control Schemes for Underwater Vehicles Regular Paper

International audienceThis paper deals with an experimental comparison be‐ tween the proportional integral derivative (PID) control law and the adaptive nonlinear state feedback control, both applied on the AC-ROV underwater vehicle. The experi‐ mental results evaluate the closed-loop behaviour of the system under each controller in various operating condi‐ tions in order to compare how robust they are towards parameters' change and how they can reject external disturbances. It was concluded that the adaptive controller ensures a faster convergence and can adapt to a change of parameters as well as compensate for external disturban‐ ces. The PID needs to be retuned for every parameter change and is more sensitive to external disturbances