Neural network based adaptive dynamic surface control for cooperative path following of marine surface vehicles via state and output feedback

Distributed anti-windup NN-sliding mode formation control of multi-ships with minimum cost

Due to the harsh marine environment, the communication cost of multi-ship formation is expensive, but it is often ignored in the existing research. On this basis, this paper proposes a novel distributed anti-windup neural network (NN)-sliding mode formation controller of multi-ships with minimum cost. Firstly, distributed control is applied to devise the formation controller of multi-ships because it is a promising solution for the problem of single point failure. Secondly, the Dijkstra algorithm is introduced to optimize the communication topology, and then an optimized communication topology with minimum cost is used in the distributed formation controller design. Thirdly, to alleviate the influence of input saturation, an anti-windup mechanism is devised by combining an auxiliary design system with sliding mode control and radial basis function neural network method; and then a novel distributed anti-windup neural network-sliding mode formation controller of multi-ships is obtained, which can also handle the problem of nonlinearity, model uncertainty, and time-varying disturbances of ship motion. On the strength of Lyapunov theory, the closed-loop signals are proved to be stable. Multiple comparative simulations are carried out to validate the effectiveness and advantage of the proposed distributed formation controller.

Adaptive bounded neural network control for coordinated path-following of networked underactuated autonomous surface vehicles under time-varying state-dependent cyber-attack

This paper is concerned with the problem of coordinated path-following for networked underactuated autonomous surface vehicles in the presence of time-varying state-dependent cyber-attack. An adaptive bounded neural network controller is proposed to mitigate the malicious effect of the cyber-attack. At first, an individual path-following control law is designed for each vehicle by fusing a back-stepping technique, a line-of-sight guidance principle and a predictor-based neural network method. Second, a path update law is developed based on a synchronization approach together with an adaptive control method. The salient features of the proposed controller are presented as follows. First, an adaptive corrective signal is incorporated into the path update law design such that a desired formation can be achieved regardless of the time-varying state-dependent cyber-attack. Second, by using a saturation function and a projection operator, the proposed controller is bounded and the bound is known as a priori. It is proven that the closed-loop system is input-to-state practical stable in the face of time-varying state-dependent cyber-attack. Simulation results show the effectiveness of the proposed adaptive bounded neural network controller for coordinated path-following of networked underactuated autonomous surface vehicles subject to the cyber-attack.

Cooperative Path Following Ring-Networked Under-Actuated Autonomous Surface Vehicles: Algorithms and Experimental Results

This paper addresses the cooperative path following the problem of ring-networked under-actuated autonomous surface vehicles on a closed curve. A cooperative guidance law is proposed at the kinematic level such that a symmetric formation pattern is achieved. Specifically, individual guidance laws of surge speed and angular rate are developed by using a backstepping technique and a line-of-sight guidance method. Then, a coordination design is proposed to update the path variables under a ring-networked topology. The equilibrium point of the closed-loop system has been proven to be globally asymptotically stable. The result is extended to the cooperative path following the lack of sharing of a global reference velocity, and a distributed observer is designed to recover the reference velocity to each vehicle. Moreover, the cooperative path following the presence of an unknown sideslip is considered, and an extended state observer is developed to compensate for the effect of the unknown sideslip. Both simulation and experimental results are provided to illustrate the effectiveness of the proposed cooperative guidance law for the path following over a closed curve.

Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences

An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended Kalman filter (EKF)-based method, combining a modified kinematic model of underwater gliders with the travel-time differences between signals received from a single beacon, is proposed for estimating the glider positions in a predict-update cycle. First, to accurately establish a motion model for underwater gliders moving in the ocean, we introduce two modification parameters, the attack and drift angles, into a kinematic model of underwater gliders, along with depth-averaged current velocities. The attack and drift angles are calculated based on the coefficients of hydrodynamic forces and the sensor-measured angle variation over time. Then, instead of satisfying synchronization requirements, the travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To further reduce the EKF estimation error, the Rauch-Tung-Striebel (RTS) smoothing method is merged into the EKF system. The proposed method is tested in a virtual spatiotemporal environment from an ocean model. The experimental results show that the performance of the RTS-EKF estimate is improved when compared with the motion model estimate, especially by 46% at the inflection point, at least in the particular study developed in this article.

Adaptive fuzzy tracking control for a constrained flexible air-breathing hypersonic vehicle based on actuator compensation

The design of an adaptive fuzzy tracking control for a flexible air-breathing hypersonic vehicle with actuator constraints is discussed. Based on functional decomposition methodology, velocity and altitude controllers are designed. Fuzzy logic systems are applied to approximate the lumped uncertainty of each subsystem of air-breathing hypersonic vehicle model. Every controllers contain only one adaptive parameter that needs to be updated online with a minimal-learning-parameter scheme. The back-stepping design is not demanded by converting the altitude subsystem into the normal output-feedback formulation, which predigests the design of a controller. The special contribution is that novel auxiliary systems are developed to compensate both the tracking errors and desired control laws, based on which the explored controller can still provide effective tracking of velocity and altitude commands when the inputs are saturated. Finally, reference trajectory tracking simulation shows the effectiveness of the proposed method in its application to air-breathing hypersonic vehicle control.

Coordinated path following of multiple underacutated marine surface vehicles along one curve

This paper investigates the coordinated path following problem for a fleet of underactuated marine surface vehicles (MSVs) along one curve. The dedicated control design is divided into two tasks. One is to steer individual underactuated MSV to track the given spatial path, and the other is to force the vehicles dispersed on a parameterized path subject to the constraints of a communication network. Specifically, a robust individual path following controller is developed based on a line-of-sight (LOS) guidance law and a reduced-order extended state observer (ESO). The vehicle sideslip angle due to environmental disturbances can be exactly identified. Then, the vehicle coordination is achieved by a path variable containment approach, under which the path variables are evenly dispersed between two virtual leaders. Another reduced-order ESO is developed to identify the composite disturbance related to the speed of virtual leaders and neighboring vehicles. The proposed coordination design is distributed since the reference speed does not need to be known by all vehicles as a priori. The input-to-state stability of the closed-loop network system is established via cascade theory. Simulation results demonstrate the effectiveness of the proposed design method.

Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.