A biologically inspired approach to tracking control of underactuated surface vessels subject to unknown dynamics. Issue 4 (March 2015)

• Record Type: Journal Article
• Authors: Pan, Chang-Zhong; Lai, Xu-Zhi; Yang, Simon X.; Wu, Min
• Is Part Of: Expert systems with applications. Volume 42:Issue 4(2015)
• Page Start: 2153
• Page End: 2161
• Publication Date: 2015-03
• Subjects: Robotics -- Bio-inspired neural dynamics -- Neural network -- Surface vessel -- Tracking control; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2014.09.042
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The tracking control problem of underactuated surface vessels is studied. A biologically inspired approach is proposed using backstepping, neurodynamics model and NN. The control algorithm is efficient as no time derivatives of virtual controls are needed. The NN learning algorithm derived from Lyapunov theory is computationally efficient. The control performance is shown to be faster and better than other approaches. Abstract: In this paper, a novel biologically inspired approach is proposed for the tracking control of an underactuated surface vessel subject to unknown dynamics. The tracking control algorithm is first derived from the error dynamics analysis of the vessel using backstepping. Then, three shunting neural dynamics derived from biological membrane equation are employed to avoid the inherent complexity of numerical derivatives of virtual control signals in the backstepping design. A single-layer neural network (NN) is finally used to approximate the unknown dynamics including uncertain model parameters and hydrodynamics coefficients. Unlike some existing tracking methods for surface vessel whose control algorithms suffer from requiring high computational effort, the proposed tracking control algorithm is computationally efficient as no derivative calculations on virtual controls are required. In addition, it is capable of tracking any smooth trajectories without any prior knowledge of the dynamics parameters. The effectiveness and efficiency of the proposed control approach are demonstrated by simulation and comparison studies.