Changzhong Pan

A biologically inspired approach to tracking control of underactuated surface vessels subject to unknown dynamics

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. 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.

BIOINSPIRED NEURODYNAMICS-BASED POSITION-TRACKING CONTROL OF HOVERCRAFT VESSELS

This paper proposes a novel bioinspired neurodynamics-based position-tracking control approach for hovercrafts, where smooth and continuous velocity commands are desirable for safe steering control. The control algorithm is derived from the tracking error dynamics by incorporating backstepping technique and neurodynamics model derived from biological membrane equation. The tracking error is proved to converge to a small neighbourhood of the origin by a Lyapunov stability theory. The proposed approach is capable of generating smooth and continuous control signals with zero initial velocities, dealing with the velocity-jump problem. In addition, it can track any sufficiently smooth-bounded curves with constant or time-varying velocities. The effectiveness and efficiency of the proposed approach are demonstrated by simulation and comparison results.

Robust aperiodic-disturbance rejection in an uncertain modified repetitive-control system

Abstract This paper concerns the problem of designing an EID-based robust output-feedback modified repetitive-control system (ROFMRCS) that provides satisfactory aperiodic-disturbance rejection performance for a class of plants with time-varying structured uncertainties. An equivalent-input-disturbance (EID) estimator is added to the ROFMRCS that estimates the influences of all types of disturbances and compensates them. A continuous-discrete two-dimensional model is built to describe the EID-based ROFMRCS that accurately presents the features of repetitive control, thereby enabling the control and learning actions to be preferentially adjusted. A robust stability condition for the closed-loop system is given in terms of a linear matrix inequality. It yields the parameters of the repetitive controller, the output-feedback controller, and the EID-estimator. Finally, a numerical example demonstrates the validity of the method.