Jialu Du

A Trajectory Tracking Robust Controller of Surface Vessels With Disturbance Uncertainties

This brief considers the problem of trajectory tracking control for marine surface vessels with unknown time-variant environmental disturbances. The adopted mathematical model of the surface ship movement includes the Coriolis and centripetal matrix and the nonlinear damping terms. An observer is constructed to provide an estimation of unknown disturbances and is applied to design a novel trajectory tracking robust controller through a vectorial backstepping technique. It is proved that the designed tracking controller can force the ship to track the arbitrary reference trajectory and guarantee that all the signals of the closed-loop trajectory tracking control system of ships are globally uniformly ultimately bounded. The simulation results and comparisons illustrate the effectiveness of the proposed controller and its robustness to external disturbances.

A robust adaptive neural networks controller for maritime dynamic positioning system

This paper aims to develop a neural controller using the vectorial backstepping technique for dynamically positioned surface ships with uncertainties and unknown disturbances. The radial basis function networks are employed to compensate for the uncertainties of ship dynamics and disturbances in controller design. The advantage of the proposed control scheme is that there is no requirement of any priori knowledge about dynamics of ships and disturbances. It is shown that our proposed control law can regulate the position and heading of ships to the desired targets with arbitrarily small positioning error. Theoretical results on stability analysis indicate that our proposed controller guarantees uniformly ultimate boundedness of all signals of the closed-loop system. Simulation studies with comparisons on a supply ship are carried out, and results demonstrate the effectiveness of the proposed control scheme.

Adaptive Autopilot Design of Time-Varying Uncertain Ships With Completely Unknown Control Coefficient

This paper develops an adaptive course controller for time-varying parametric uncertain nonlinear ships with completely unknown time-varying bounded control coefficient. The proposed design method does not require any a priori knowledge of the sign of the unknown time-varying control coefficient. The designed adaptive autopilot can guarantee the regulation of the ship course to any prescribed accuracy and the global uniform ultimate boundedness of all signals in the closed-loop system. The effectiveness of the presented autopilot has been demonstrated in a simulation involving a ship of 45 m in length.

Robust Adaptive Control for Dynamic Positioning of Ships

In this paper, a robust adaptive control scheme with the global asymptotic stability with respect to positioning errors is proposed for dynamic positioning (DP) of ships in the presence of time-varying unknown bounded environmental disturbances. The unknown environmental disturbances are expressed as the outputs of a linear exosystem with unknown parameters and all eigenvalues of system matrix lying on the imaginary axis. On the basis of this exosystem, the disturbances are further represented as the outputs of a linear model of canonical form with unknown disturbances being inputs by a multivariate linear regression model whose regressor is the state vector of the linear model and whose regression parameters depend on unknown parameters of the linear exosystem. This representation allows us to construct an observer to estimate the unavailable state vector (regressor) in the linear model and hence convert the disturbance compensation control for the DP of ships to an adaptive control problem. Then, a robust adaptive control law for the DP of ships is designed incorporating the constructed observer and the projection algorithm into the vectorial backstepping method. The global asymptotic stability with respect to positioning errors of the DP closed-loop control system is proved applying Lyapunov stability theory and Barbalats lemma. Finally, simulation results on a supply ship Northern Clipper in two different disturbance cases and simulation comparisons with an existing DP adaptive robust control scheme demonstrate more effectiveness and less conservativeness of our proposed control scheme.

Robust adaptive NN-based output feedback control for a dynamic positioning ship using DSC approach

A robust adaptive NN-based output feedback control scheme is presented for a dynamic positioning ship with uncertainties and unknown external disturbances. We tackle the problem that velocity vector of a ship is not available by employing a high-gain observer, and develop the proposed control approach by combing vectorial backstepping with dynamic surface control approach, which is simpler and easier to implement in engineering practice. The neural network (NN) approximation technique is used to compensate for the uncertainties and unknown external disturbances, and it removes the requirement for the prior knowledge about the vessel parameters and external disturbances. Also, it is demonstrated that the proposed control strategy can force the position and yaw angle of a dynamic positioning ship to approach the desired point while guaranteeing all singles of the designed closed-loop dynamic positioning system semi-globally uniformly ultimately bounded by means of the Lyapunov function. Simulation results of a supply ship illustrate the effectiveness of the proposed scheme.

Dynamic Surface Control for Nonlinear Dynamic Positioning System of Ship

This paper addresses the problem of dynamic positioning of ships in the horizontal plane. A nonlinear controller including a first-order low-pass filter is proposed that yields convergence of the ship’s position and orientation to a desired target point in the presence of unknown environmental disturbances. The proposed controller is derived by incorporating dynamic surface control technique into vectorial backstepping, which leads to no need for the differentiation of the model in the controller design and hence eliminates the problem of “explosion of terms” inherent in the backstepping approach, while being simpler to implement. In addition, it is proven that the control law can guarantee the uniformly ultimate boundedness of all signals of closed-loop dynamic positioning system of ship based on Lyapunov function under the assumption that the bound of uncertain disturbance is known. Finally, simulation results on the mathematical model of a supply ship are presented to validate the effectiveness of the proposed control scheme.

Swarm-based intelligent optimization approach for layout problem

Layout problem is a kind of NP-Complete problem. It is concerned more and more in recent years and arises in a variety of application fields such as the layout design of spacecraft modules, plant equipment, platforms of marine drilling well, shipping, vehicle and robots. The algorithms based on swarm intelligence are considered powerful tools for solving this kind of problems. While usually swarm intelligence algorithms also have several disadvantages, including premature and slow convergence. Aiming at solving engineering complex layout problems satisfactorily, a new improved swarm-based intelligent optimization algorithm is presented on the basis of parallel genetic algorithms. In proposed approach, chaos initialization and multi-subpopulation evolution strategy based on improved adaptive crossover and mutation are adopted. The proposed interpolating rank-based selection with pressure is adaptive with evolution process. That is to say, it can avoid early premature as well as benefit speeding up convergence of later period effectively. And more importantly, proposed PSO update operators based on different versions PSO are introduced into presented algorithm. It can take full advantage of the outstanding convergence characteristic of particle swarm optimization (PSO) and improve the global performance of the proposed algorithm. An example originated from layout of printed circuit boards (PCB) and plant equipment shows the feasibility and effectiveness of presented algorithm.

Adaptive Robust Nonlinear Control Design for Course Tracking of Ships Subject to External Disturbances and Input Saturation

An adaptive robust control law is proposed for the course tracking problem of ships in this paper incorporating a Nussbaum function and an auxiliary dynamic system into the adaptive dynamic surface control (DSC) technique. The ship steering dynamics is described by the Norrbin nonlinear model with completely unknown control coefficient, parameter uncertainties, and unknown external disturbances and input saturation caused by the rudder constraint. The Nussbaum function is adopted to deal with completely unknown control coefficient and avoid the controller singularity problem. An auxiliary dynamic system is introduced to handle the effect of input saturation. The DSC technique makes the control law be simple to compute and easy to implement in engineering practice. It is proved that the proposed course tracking control law of ships makes the course tracking error be arbitrarily small by an appropriate choice of the design parameters and guarantees the uniform ultimate boundedness of all signals in the closed-loop ship course control system. Finally, simulation results on two ships and simulation comparison with an existing adaptive neural control scheme demonstrate the effectiveness and the superiority of the proposed control scheme.

Research and application on a fuzzy CMAC controller with eligibility

A fuzzy CMAC controller with eligibility (FCE) is proposed. The eligibility can forecast the controlled system, and improve the system stability. The structure of FCE system is presented, and its learning algorithm is deduced. To make the algorithm fit to on-line control, the efficient implementation of FCE method is also given. Applying the FCE controller in a ship steering control system, the simulation results show that the ship course can be properly controlled in case of the disturbances of wave, wind, current and error in measure apparatus exist. It is demonstrated that the proposed algorithm is a promising alternative to conventional autopilots.

Robust adaptive NN control of dynamically positioned vessels under input constraints

Abstract A robust adaptive neural network (NN) control law is developed for dynamic positioning (DP) of vessels with unknown dynamics and unknown time-varying disturbances under input constraints through incorporating adaptive radial basis function (RBF) NNs, an auxiliary dynamic system and a robust control term into dynamic surface control method. The developed DP control law makes the DP closed-loop system be uniformly ultimately stable and the vessels position and heading be maintained at the desired values with arbitrarily small errors. The advantages of the proposed control scheme are that: first, the developed DP control law does not require any priori knowledge of vessel dynamics and disturbances under input constraints, and prevents the presence of input constraints from degrading control performance and even destabilizing the DP control system; second, the developed DP control law compensates for not only unknown time-varying disturbances but also NN approximation errors for unknown vessel dynamics. Simulations on two supply vessels are conducted to exhibit the efficiency and control performance of the developed DP control law.