Designing a ship course controller by applying the adaptive backstepping method

Abstract

The article discusses the problem of designing a proper and efficient adaptive course-keeping control system for a seagoing ship based on the adaptive backstepping method. The proposed controller in the design stage takes into account the dynamic properties of the steering gear and the full nonlinear static maneuvering characteristic. The adjustable parameters of the achieved nonlinear control structure were tuned up by using the genetic algorithm in order to optimize the system performance. A realistic full-scale simulation model of the B-481 type vessel including wave and wind effects was applied to simulate the control algorithm by using time domain analysis.

Adaptive System for Steering a Ship Along the Desired Route

“…The article presented an adaptive control system for keeping track of a ship as an element of an intelligent system applicable in modern marine navigation [24][25][26][27][28][29][30][31][32][33][34][35]. An optimal LQR regulator with a symmetric indicator of control quality was adopted as the control algorithm.…”

Dynamically Positioned Ship Steering Making Use of Backstepping Method and Artificial Neural Networks

“…To determine the adaptation laws for parameters of matrices M, D, C and vector b, the function f is to be presented in the form of the regression model, after which the standard backstepping procedure can be applied. This approach is labour-intensive, requires huge computational effort, and leads to an excessively large number of estimated parameters, as stated in [23]. Instead, the components of the function f = [f 1 , f 2 , f 3 ] T can be approximated using three artificial RBF type neural networks NN i with the number of neurons l > 1.…”

Analytical Synthesis of Regulators for Nonlinear Systems with a Terminal State Method on Examples of Motion Control of a Wheeled Robot and a Vessel

“…A common drawback of these methods, as well as others based on the use of linear feedback from the state coordinates [14,15], is that they contain indeterminate coefficients that are unknown in advance and may depend on the operating conditions. To adapt to the operation conditions, principles of adaptive and intelligent control are used [16,17]. To a lesser extent, such drawbacks are inherent in methods based on the use of control object mathematical models [18,19].…”