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

This paper presents a neural adaptive controller for a class of nonlinear system in the presence of input saturation and arbitrary unknown information by integrating the design framework of neural networks (NNs) with the dynamic surface control (DSC) and backstepping technique. NNs are directly used to deal with unknown nonlinear terms. By introducing a well-defined smooth function and a Nussbaum function, a novel robust adaptive control scheme is developed to guarantee the states of system to be semi-globally uniformly ultimately bounded (SGUUB). Owing to the introduction of DSC, the problem of ‘explosion of complexity’ inherent in the backstepping design is effectively eliminated. Simulation results demonstrate the effectiveness and simplicity of the proposed method.

This paper presents distributed formation control with guaranteed prescribed performances for multiple parametric uncertain marine surface vehicles (MSVs) subject to external time-varying disturbances, under an undirected graph. To provide the transient and steady-state performances of neighborhood errors, the prescribed performance constraints are imposed on the neighborhood errors and these prescribed performance constraints are guaranteed by the prescribed performance control methodology. The uncertain parameters and the upper bounds of the external disturbances are estimated by a distributed parameter estimate method, in which the additive terms consisting of parameter estimates from the neighbors are added to adaptive laws to improve the consensus speed of adaptive parameters. Based on dynamic surface control (DSC) technique, prescribed performance control methodology, and Lyapunov synthesis, distributed adaptive formation controllers are designed to force the following vehicles to perform desired formation pattern with guaranteed prescribed performances. The proposed controllers are distributed in the sense that each vehicle calculates its control signals based on local information from its neighborhood set, and the utilization of DSC technique could avoid using the neighbors' accelerations in the distributed formation controllers. An illustrated example is given to show the performances of the distributed formation controllers.

This paper is concerned with the synchronized path following problem of multiple underactuated autonomous underwater vehicles (AUVs) in the presence of uncertain dynamics and input constrains. Nonlinear path following controllers are proposed for individual AUV that yield convergence of the position tracking errors to a small neighborhood of the origin. Vehicles coordination is achieved by synchronizing the along-path speed and path variables, as determined by the communications topology adopted. The proposed control design use a combination of neural network (NN), dynamic surface control (DSC) technique and an auxiliary system. The NN based DSC adaptive technique is incorporated to handle the uncertain dynamics, and simplifies the synchronized path following controllers by introducing the first-order filters. Input constrains problem is taken into account with the aid of auxiliary system design. Under the proposed controllers, uniformly ultimately bounded (UUB) of the closed-loop system are guaranteed for all signals. The design procedure is illustrated through the simulation results.

Robust time-varying formation control for underactuated autonomous underwater vehicles with disturbances under input saturation

Fully distributed dynamic event-triggered reliable COR of nonlinear MASs with communication link faults and actuator faults for directed graphs

To adapt to complex navigation conditions, this paper addresses the coordination formation of autonomous surface vehicles (ASVs) with the constraint of information interruption. For this purpose, a distributed robust fast finite-time formation control algorithm is proposed by fusion of the directed graph and neural network method. In the strategy, the graph theory is utilized for the channel of information transmission to maintain the stability of the formation system. In addition, the radial basic function (RBF) neural network is employed to approximate the structure uncertainty. Due to the merits of the robust neural damping technique, only two adaptive parameters are designed to compensate the perturbation from the model uncertainty and external environmental. Furthermore, an improved dynamic surface control (DSC) technology is developed for constituting the exponential term of the Lyapunov function. It is proven that the proposed scheme is able to achieve consensus tracking in finite time quickly, and the errors rapidly approach a small region around the origin. Finally, the feasibility and effectiveness of the algorithm are verified by two numerical simulations.

In this paper, a surge speed tracking problem for an unmanned surface vehicle (USV) driven by a direct current (DC) motor is investigated. The surge velocity tracking controller is developed based on a modular neural dynamic surface control (MNDSC) design method. Specifically, two neural-network-based identifiers are designed to identify the uncertainties existing in surge dynamics, propeller and DC motor. Then, a robust adaptive surge speed controller based on a dynamic surface control design method is designed by using the recovered unknown information from identifiers. The proposed surge velocity tracking controller is able to track any time-varying bounded velocity profiles. The stability of the closed-loop system is established based on cascade theory and input-to-state stability (ISS) theory. The effectiveness of the proposed surge speed controller for the USV is illustrated via simulations.

Neural adaptive steering of an unmanned surface vehicle with measurement noises

Distributed adaptive output consensus control of a class of heterogeneous multi-agent systems under switching directed topologies

This paper studies the platoon formation control problem for unmanned surface vehicles, in the presence of modeling uncertainties and time-varying external disturbances. The control objective is to make the vehicular platoons proceed along a given trajectory while maintaining a desired line-of-sight (LOS) range between each vehicle and its predecessor. To provide transient performance specifications on formation errors, including LOS range and angle errors, we enforce prescribed performance guarantees in the control design. The prescribed performance guarantees mean that formation errors evolve always within the predefined regions that are bounded by exponentially decaying functions of time. Using prescribed performance control methodology, neural network approximation, disturbance observers, dynamic surface control technique, and Lyapunov synthesis, we propose an adaptive formation control that ensures internal stability of closed-loop systems with guaranteed prescribed performance. Meanwhile, both collision avoidance and connectivity maintenance between two consecutive vehicles are guaranteed during the whole operation. The proposed formation control is decentralized in the sense that the control action on each vehicle depends only on information from its immediate predecessor. Simulation results demonstrate the performance of the proposed control.

Single-parameter-learning-based fuzzy fault-tolerant output feedback dynamic surface control of constrained-input nonlinear systems

Deterministic learning-based neural control for output-constrained strict-feedback nonlinear systems

Long-term unmanned surface vehicle (USV) is an effective means in ocean environmental monitoring and target detection for extended periods. Efficient steering control is the basis and premise for long-term USV to improve the adaptability and the endurance ability in complex sea states. In this paper, an efficient control algorithm is developed with the aid of fuzzy inference system and dynamic surface control technology. Specifically, based on data mining technology the fuzzy inference system is employed to identify the unknown yaw dynamics for the USV in presence of sea environmental disturbance and measurement noises. Besides, a robust adaptive steering law is established by using the dynamic surface control (DSC) design approach in Lyapunov stability analysis. In simulation, the simulation object is the independent designed “Lanxin” USV of Dalian Maritime University in China. Simulation results are given to validate the efficacy of the proposed method.

A novel adaptive neural network dynamic surface control is proposed for hypersonic vehicle. Based on the hypersonic vehicle model characteristics, the adaptive neural network dynamic surface control attitude controller and the neural networks velocity controller are designed, respectively. By combining Nussbaum gain function with decoupled backstepping, the problems of unknown control directions and control singularity from hypersonic vehicles model in strict-feedback form are simultaneously solved. Furthermore, the norm of the ideal weighting vector in neural network systems is considered as the estimation parameter, such that only one parameter is adjusted at each recursive step. Based on decoupled backstepping method and Lyapunov stability theorem, the semi-global stability of the flight control system is proved. Simulation results show that the new controller can guarantee the expected tracking performance.

This paper presents a neural adaptive finite-time dynamic surface control for the permanent magnet synchronous motor system with time delays and asymmetric time-varying output constraint. The core challenge is how to address the time delays and asymmetric output constraint when designing a finite-time control scheme. Given this, a proper Lyapunov–Krasovskii functional is introduced to address time delays, and a nonlinear transformation function is considered to convert the output-constrained problem into an unconstrained one. Then, a neural adaptive finite-time dynamic surface control approach is devised in the finite-time backstepping framework, which applies neural networks to estimate the unknown nonlinear functions and introduces first-order filters to solve the “explosion of complexity” problems. Furthermore, it is demonstrated that all the signals of the resulting system are finite-time stable and the tracking error converges to a neighborhood of origin in finite time without violating the output constraint. Finally, the simulation results show that the integration of squared error results, the integral of time and absolute error results as well as the integration of absolute value error results of the proposed scheme is smaller than the tested scheme by 0.3458 , 22.2977 , and 2.2513 , respectively, when the time delays are considered. It further elucidates the availability and superiority of the developed method.