Dynamic Positioning System Design for A Marine Vessel with Unknown Dynamics Subject to External Disturbances Including Wave Effect

In this paper, a new control system is proposed for dynamic positioning(DP) of marine vessels with unknown dynamics and subject to external disturbances. The control system is composed of a substructure for wave filtering and state estimation together with a nonlinear PD-type controller. For wave filtering and state estimation, a cascade combination of a modified notch filter and an estimation stage is considered. In estimation stage, a modified extended-state observer(ESO) is proposed to estimate vessel velocities and unknown dynamics. The main advantage of the proposed method is its robustness to model uncertainties and external disturbances and it does not require prior knowledge of vessel model parameters. Besides, the stability of the cascade structure is analyzed and input to state stability(ISS) is guaranteed. Later on, a nonlinear PD-type controller with feedforward of filtered estimated dynamics is utilized. Detailed stability analyses are presented for the closed-loop DP control system and global uniform ultimate boundedness is proved using large scale systems method. Simulations are conducted to evaluate the performance of the proposed method for wave filtering and state estimation and comparisons are made with two conventional methods in terms of estimation accuracy and the presence of uncertainties. Besides, comparisons are made in closed-loop control system to demonstrate the performance of the proposed method compared with conventional methods. The proposed control system results in better performance in the presence of uncertainties,external disturbance and even in transients when the vessel is subjected to sudden changes in environmental disturbances.

Finite-Time Distributed Identification for Nonlinear Interconnected Systems

In this paper,a novel finite-time distributed identification method is introduced for nonlinear interconnected systems.A distributed concurrent learning-based discontinuous gradient descent update law is presented to learn uncertain interconnected subsystems’dynamics.The concurrent learning approach continually minimizes the identification error for a batch of previously recorded data collected from each subsystem as well as its neighboring subsystems.The state information of neighboring interconnected subsystems is acquired through direct communication.The overall update laws for all subsystems form coupled continuous-time gradient flow dynamics for which finite-time Lyapunov stability analysis is performed.As a byproduct of this Lyapunov analysis,easy-to-check rank conditions on data stored in the distributed memories of subsystems are obtained,under which finite-time stability of the distributed identifier is guaranteed.These rank conditions replace the restrictive persistence of excitation(PE)conditions which are hard and even impossible to achieve and verify for interconnected subsystems.Finally,simulation results verify the effectiveness of the presented distributed method in comparison with the other methods.

Observer-based motion axis control for hydraulic actuation systems

Unknown dynamics including mismatched mechanical dynamics(i.e.,parametric uncertainties,unmodeled friction and external disturbances)and matched actuator dynamics(i.e.,pressure and flow characteristic uncertainties)broadly exist in hydraulic actuation systems(HASs),which can hinder the achievement of high-precision motion axis control.To surmount the practical issue,an observer-based control framework with a simple structure and low computation is developed for HASs.First,a simple observer is utilized to estimate mismatched and matched unknown dynamics for feedforward compensation.Then combining the backstepping design and adaptive control,an appropriate observer-based composite controller is provided,in which nonlinear feedback terms with updated gains are adopted to further improve the tracking accuracy.Moreover,a smooth nonlinear filter is introduced to shun the“explosion of complexity”and attenuate the impact of sensor noise on control performance.As a result,this synthesized controller is more suitable for practical use.Stability analysis uncovers that the developed controller assures the asymptotic convergence of the tracking error.The merits of the proposed approach are validated via comparative experiment results applied in an HAS with an inertial load as well.

A neuro-observer-based optimal control for nonaffine nonlinear systems with control input saturations

In this study,an adaptive neuro-observer-based optimal control(ANOPC)policy is introduced for unknown nonaffine nonlinear systems with control input constraints.Hamilton–Jacobi–Bellman(HJB)framework is employed to minimize a non-quadratic cost function corresponding to the constrained control input.ANOPC consists of both analytical and algebraic parts.In the analytical part,first,an observer-based neural network(NN)approximates uncertain system dynamics,and then another NN structure solves the HJB equation.In the algebraic part,the optimal control input that does not exceed the saturation bounds is generated.The weights of two NNs associated with observer and controller are simultaneously updated in an online manner.The ultimately uniformly boundedness(UUB)of all signals of the whole closed-loop system is ensured through Lyapunov’s direct method.Finally,two numerical examples are provided to confirm the effectiveness of the proposed control strategy.