A genetic algorithm-based model predictive control autopilot design and its implementation in an autonomous underwater vehicle

Abstract

The control of any underwater vehicle has always been a challenging task and is certainly an important and necessary feature of an autonomous underwater vehicle (AUV ). This paper describes the implementation of a genetic algorithm (GA)-based model predictive controller for an AUV named Hammerhead which is being developed jointly by the Universities of Plymouth and Cranfield. To the present authors' knowledge, this is the first successful application of a GA in realtime optimization for controller tuning in the marine sector and thus the paper makes an extremely novel and useful contribution to control system design. The advantages of using model predictive control (MPC ) include its constraint handling and disturbance rejection properties commonly present in an underwater environment. The use of GAs generalizes MPC to employ linear as well as nonlinear process models. Furthermore, it supports the inclusion of various types of objective function without having to change the controller structure. The model required for MPC is extracted using system identification techniques on actual AUV data obtained from full-scale in-water tests. A description of Hammerhead AUV is outlined and simulation and experiment data are shown which were obtained by optimizing the cost function online using the GA. Results demonstrate good tracking behaviour despite the presence of disturbances and ever-present modelling uncertainty.

Non-linear model predictive formation control for groups of autonomous surface vessels

“…Methods for trajectory tracking that ensure a good performance even in the presence of disturbances have been introduced by researchers who have worked on underwater vehicles. A model predictive controller (MPC) based on genetic algorithms for an autonomous underwater vehicle (AUV) has been implemented by Naeem et al (2004). In that research, the advantages of using MPC in handling constraints and rejecting the disturbances commonly present in an underwater environment have successfully been shown.…”

Iterative Self-Tuning Minimum Variance Control of a Nonlinear Autonomous Underwater Vehicle Maneuvering Model

“…In underwater vehicle autopilot design, the existing literature ranges from the application of PID and classical control [3] techniques to the application of modern techniques such as H-infinite [4], sliding model control [5][6][7][8][9][10], fuzzy control [11][12][13][14], neural networks [15], output feedback [16], linearization via state feedback [17], adaptive control [18], predictive control [19,20], and backstepping control [21,22]. These control methods provide good results in the cited references but are-in most cases-restricted to a certain operational condition.…”

Transient uniformity model predictive control in dealing with non-uniformity of multivariable systems

“…Model predictive control (MPC) designs have the ability to yield high-performance control systems which are capable of operating without expert intervention for long periods of time. 1 Hence, the MPC scheme has been extensively employed in a wide range of practical applications, such as semiconductor wafer manufacturing, 2 marine surface vessels, 3,4 aircraft, 5 road vehicles, 6,7 unmanned aerial vehicles, 8 underwater vehicles, 9,10 supply chain, 11 building climate systems, 12 and financial planner. 13 The MPC scheme is also called receding horizon control, that it solves the finite-horizon optimization problem at every sampling time repeatedly.…”