Zhouhua Peng

Adaptive Dynamic Surface Control for Formations of Autonomous Surface Vehicles With Uncertain Dynamics

In this brief, we consider the formation control problem of underactuated autonomous surface vehicles (ASVs) moving in a leader-follower formation, in the presence of uncertainties and ocean disturbances. A robust adaptive formation controller is developed by employing neural network and dynamic surface control technique. The stability of the design is proven via Lyapunov analysis where semiglobal uniform ultimate boundedness of the closed-loop signals is guaranteed. The advantages of the proposed formation controller are that: first, the proposed method only uses the measurements of line-of-sight range and angle by local sensors, no other information about the leader is required for control implementation; second, the developed neural formation controller is able to capture the vehicle dynamics without exact information of coriolis and centripetal force, hydrodynamic damping and disturbances from the environment. Comparative analysis with a model-based approach is given to demonstrate the effectiveness of the proposed method.

Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems.

This paper addresses the leader-follower synchronization problem of uncertain dynamical multiagent systems with nonlinear dynamics. Distributed adaptive synchronization controllers are proposed based on the state information of neighboring agents. The control design is developed for both undirected and directed communication topologies without requiring the accurate model of each agent. This result is further extended to the output feedback case where a neighborhood observer is proposed based on relative output information of neighboring agents. Then, distributed observer-based synchronization controllers are derived and a parameter-dependent Riccati inequality is employed to prove the stability. This design has a favorable decouple property between the observer and the controller designs for nonlinear multiagent systems. For both cases, the developed controllers guarantee that the state of each agent synchronizes to that of the leader with bounded residual errors. Two illustrative examples validate the efficacy of the proposed methods.

Containment control of networked autonomous underwater vehicles with model uncertainty and ocean disturbances guided by multiple leaders

This paper considers the containment control of networked autonomous underwater vehicles guided by multiple dynamic leaders over a directed network. Each vehicle is subject to model uncertainty and unknown time-varying ocean disturbances. A new predictor-based neural dynamic surface control design approach is presented to develop the adaptive containment controllers, under which the trajectories of vehicles converge to the convex hull spanned by those of the leaders. Specifically, iterative neural updating laws, based on prediction errors, are constructed, which enable the accurate identification of the unknown dynamics for each vehicle, not only in steady state but also in transient state. Furthermore, this result is extended to the output-feedback case where only the position-yaw information can be measured. A neural observer is developed to recover the unmeasured velocity information. Based on the observed velocities of neighboring vehicles, distributed output-feedback containment controllers are devised, under which the containment can be achieved regardless of model uncertainty, unknown ocean disturbances, and unmeasured velocity information. For both cases, Lyapunov-Krasovskii functionals are used to prove the uniform ultimate boundedness of the closed-loop error signals. Comparative studies are given to show the performance improvement of the proposed methods.

Distributed Containment Maneuvering of Multiple Marine Vessels via Neurodynamics-Based Output Feedback

In this paper, a neurodynamics-based output feedback scheme is proposed for distributed containment maneuvering of marine vessels guided by multiple parameterized paths without using velocity measurements. Each vessel is subject to internal model uncertainties and external disturbances induced by wind, waves, and ocean currents. In order to recover unmeasured velocity information as well as to identify unknown vessel dynamics, an echo state network (ESN) based observer using recorded input–output data is proposed for each vessel. Based on the observed velocity information of neighboring vessels, distributed containment maneuvering laws are developed at the kinematic level. Next, in order to shape the transient motion profile for vessel kinetics to follow, finite-time nonlinear tracking differentiators are employed to generate smooth reference signals as well as to extract the time derivatives of kinematic control laws. Finally, ESN-based dynamic control laws are constructed at the kinetic level. The stability of the closed-loop system is analyzed via input-to-state stability and cascade theory. Simulation results are provided to illustrate the efficacy of the proposed neurodynamics-based output feedback approach.

Prescribed Performance Consensus of Uncertain Nonlinear Strict-Feedback Systems With Unknown Control Directions

In this paper, a leader-following consensus scheme is presented for networked uncertain nonlinear strict-feedback systems with unknown control directions under directed graphs, which can achieve predefined synchronization error bounds. Fuzzy logic systems are employed to approximate system uncertainties. A specific Nussbaum-type function is introduced to solve the problem of unknown control directions. Using a dynamic surface control technique, distributed consensus controllers are developed to guarantee that the outputs of all followers synchronize with that of the leader with prescribed performance. Based on Lyapunov stability theory, it is proved that all signals in closed-loop systems are uniformly ultimately bounded and the outputs of all followers ultimately synchronize with that of the leader with bounded tracking errors. Simulation results are provided to demonstrate the effectiveness of the proposed consensus scheme.

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

This paper addresses the problem of steering a group of marine surface vehicles along given spatial paths, while holding a desired formation pattern subject to dynamical uncertainty and ocean disturbances induced by unknown wind, waves and ocean currents. The control design is categorized into two envelopes. One is to steer individual marine surface vehicle to track a given spatial path. The other is to synchronize the speed of each vehicle along its path and path variables under the constraints of an underlying communication network in order to holding a desired formation pattern. The key features of the developed controllers are that, first, the neural network adaptive technique allows one to handle the dynamical uncertainty and ocean disturbances, without the need for explicit knowledge of the model; second, the proposed dynamic surface control technique simplifies the controller design by introducing the first-order filters and avoids the calculation of derivatives of virtual control signals. Further, this result is extended to the output feedback case, where a high-gain observer based cooperative path following controller is developed without measuring the velocity of each vehicle. Under the proposed controllers, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded for both state and output feedback cases. Simulation results validate the performance and robustness improvement of the proposed strategy.

Cooperative Dynamic Positioning of Multiple Marine Offshore Vessels: A Modular Design

In this paper, a new cooperative control scheme is presented for the dynamic positioning of multiple offshore vessels, subject to the influence of persistent ocean disturbances induced by wind, waves, and ocean currents. The vessels are interconnected through an underlying directed network. Unlike the traditional dynamic positioning of individual marine surface vessels, cooperative dynamic positioning controllers are developed based on a modular design approach. Specifically, a predictor module is proposed for estimating the unknown ocean disturbances, which is able to achieve the disturbance estimation as fast as possible. Then, the controller module is designed based on a dynamic surface control technique. The input-to-state stability of the closed-loop network system is established via cascade theory. Furthermore, this result is extended to output feedback, where only the position-yaw information is available. Another predictor module is developed for estimating the unmeasured velocities, as well as unknown ocean disturbances. Then, the dynamic surface control technique is employed to devise the output feedback controller. The proposed designs result in decoupled estimate and control, and can achieve fast adaptation for both state and output feedbacks. Results of comparative studies are given to substantiate the efficacy of the proposed methods.

Containment Maneuvering of Marine Surface Vehicles With Multiple Parameterized Paths via Spatial-Temporal Decoupling

The containment maneuvering of marine surface vehicles has two objectives. The first one is to force the marine vehicles to follow a convex hull spanned by multiple parameterized paths. The second one is to meet the requirement of a desired dynamic behavior along multiple paths during containment. A modular design approach to the containment maneuvering of marine surface vehicles is presented. At first, an estimator module using a recurrent neural network is proposed to estimate the unknown kinetics induced by model uncertainty, unmodeled dynamics, and environmental disturbances. Next, a controller module is developed based on a distributed path maneuvering design and a linear tracking differentiator. Finally, two path update laws based on a maneuvering error feedback and a filtering update scheme, respectively, are constructed. The estimator-controller pair forms a cascade system, which is proved to be input-to-state stable. The developed controller has a desirable spatial-temporal decoupling property, and geometric and dynamic objectives can be achieved separately. Results of comparative studies are provided to substantiate the efficacy of the proposed method.

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method.

Cooperative fuzzy adaptive output feedback control for synchronisation of nonlinear multi-agent systems under directed graphs

This paper considers the leader-following synchronisation problem of nonlinear multi-agent systems with unmeasurable states and a dynamic leader whose input is not available to any follower. Each follower is governed by a nonlinear system with unknown dynamics. Two distributed fuzzy adaptive protocols, based on local and neighbourhood observers, respectively, are proposed to guarantee that the states of all followers synchronise to that of the leader, under the condition that the communication graph among the followers contains a directed spanning tree. Based on Lyapunov stability theory, the synchronisation errors are guaranteed to be cooperatively uniformly ultimately bounded. Two examples are provided to show the effectiveness of the proposed controllers.