Non-linear control algorithms for an unmanned surface vehicle

Abstract

Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design.

A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents

“…In terms of autopilot and control system development, a detailed review of studies con-ducted on USVs has been discussed by Roberts (2008). Many control techniques like H ∞ (Lefeber et al, 2003), linear quadratic Gaussian (LQG) (Sharma et al, 2012), model predictive control (MPC) (Annamalai et al, 2015) have been proposed recently, together with development of an adaptive control system (Sharma et al, 2014) towards making the controller effective for a range of USV speeds and operating sea conditions.…”

Robust Optimal Navigation Using Nonlinear Model Predictive Control Method Combined with Recurrent Fuzzy Neural Network

“…In [22], MPC is also developed to micro air vehicle system generating real-time optimal trajectory in unknown and low-sunlight environments. In [23], autopilot controlling the nonlinear yaw dynamics of an unmanned surface vehicle is designed using MPC combined with multilayer neural network.…”

The angle guidance path planning algorithms for unmanned surface vehicle formations by using the fast marching method

“…With the advance of the autonomy technology as well as the drive of demand, there has been an increasing development of unmanned surface vehicles (USVs) in recent decade. The applications of USVs include military utilisations such as sea patrol (Bertaska et al, 2015) and coastal guarding (Manley, 2008), and civilian or scientific deployments such as environmental monitoring (Sharma et al, 2014) and bathymetric survey (Kitts et al, 2012). However, as mentioned in Campbell et al (2012), most existing USV platforms are restricted by the problem of semi-autonomy, which prevents the USV from performing complex tasks requiring long endurance times.…”