A cutting-edge approach to observer-driven finite-time adaptive fault-tolerant control for unmanned surface vessels leveraging advanced neural architectures

Minimum-learning-parameter-based adaptive finite-time trajectory tracking event-triggered control for underactuated surface vessels with parametric uncertainties

Distributed adaptive finite-time fault-tolerant formation-containment control for networked Euler–Lagrange systems under directed communication interactions

This paper designs a finite-time trajectory tracking control system with event-triggered input for underactuated surface vessels(USVs) which are influenced by dynamic uncertainties, unknown time-varying disturbances and limited communication resources. In this system, the virtual reference heading is obtained by performing norm inverse calculation on the virtual control of sway and surge, and a new kinematic error equation is constructed based on this. Then, the internal dynamic uncertainty and the external time-varying disturbance in the system are compounded linearized, and the upper bound of the compound uncertainty term are approximated by adaptive technology. Combined with the finite-time control theory, the kinematic virtual control is optimized, and the finite-time control law is obtained. Through the event-triggered technology, the control frequency of the system is limited to ensure the effective use of communication resources. Finally, the design process of the whole system is strictly analyzed, and the performance of the system is effectively verified by two sets of simulations.

In this paper, we study the trajectory tracking control of underactuated surface vessels(USVs) subject to actuator faults, uncertain dynamics, unknown environmental disturbances, and communication resource constraints. Considering that the actuator is prone to bad faults, the uncertainties formed by the combination of fault factors, dynamic uncertainties and external disturbances are compensated by a single online updated adaptive parameter. In the compensation process, we combine the robust neural-damping technology with the minimum learning parameters (MLPs), which improves the compensation accuracy and reduces the computational complexity of the system. To further improve the steady-state performance and transient response of the system, finite-time control (FTC) theory is introduced into the design of the control scheme. At the same time, we adopt the event-triggered control (ETC) technology, which reduces the action frequency of the controller and effectively saves the remote communication resources of the system. The effectiveness of the proposed control scheme is verified by simulation. Simulation results show that the control scheme has high tracking accuracy and strong anti-interference ability. In addition, it can effectively compensate for the adverse influence of fault factors on the actuator, and save the remote communication resources of the system.

Considering the model parametric uncertainties and time-varying disturbances induced by wave, wind, and ocean-current in the trajectory tracking control of a 3 degree-of-freedom underactuated surface vessel (USV), a novel nonlinear finite time control law is designed, in view of robustness of the finite time control in anti system uncertainties and environmental disturbances. The path following problem of the underactuated system is transformed into stabilization problem of the nonlinear system by simplifying analysis of the USV system. The disturbance observer is designed to compensate control by observing unknown disturbance in real-time, which effectively restrains the disturbances of wind, wave and flow time-varying. The finite time control combined backstepping law is proposed, which ensures the error uniformly bounded by Lyapunov stability theorem, and improves robustness and convergence of system. Simulation results are provided to demonstrate effectiveness of the method.

Considering the effects of disturbances in permanent magnet synchronous motor (PMSM), in this paper, a non-smooth composite control approach, which includes finite time disturbance observer (FTDO) for feedforward compensation and finite time control (FTC) for feedback control, is proposed to improve the anti-disturbance performance of PMSM. First, the FTDO is used to estimate the lumped disturbances, such as friction, parameter perturbation, and load variation. Then, the observed value is added to the speed controller as a feedforward compensation to eliminate the effect of disturbance. Second, FTC is introduced into the feedback control design part. In the end, by utilizing Lyapunov theory, the stability analysis of the overall closed-loop system is demonstrated. In contrast to the conventional asymptotically stable control strategy, the proposed composite scheme can provide not only a faster dynamic response but also a stronger capacity of disturbance rejection. The simulation and experimental tests are presented to demonstrate the superior properties of the proposed control scheme.

Finite-time dynamic positioning control design for surface vessels with external disturbances, input saturation and error constraints

Fixed-time self-structuring neural network fault-tolerant tracking control of underactuated surface vessels with state constraints

Prescribed performances based fuzzy-adaptive output feedback containment control for multiple underactuated surface vessels

Applications of the proposed control algorithm on 2-DOF underactuated mechanical systems (UMS) have already been discussed in the previous chapter. Needless to say that during real-life applications, most of the practical UMSs that comes in the scenario has more degrees of freedom. Therefore, only dealing with the 2-DOF systems would not give readers full working knowledge. Keeping in view the immense importance of higher order UMSs, authors dedicate this chapter to describe the applications of the proposed control algorithm on the same in a systematic manner. Following the similar presentation approach of Chap. 4, at the onset, a very simple flat 3-DOF system model is considered to demonstrate the controller design procedure for higher order systems. Since flat UMS has most simple dynamic characteristics than that of the other UMSs, it is always easy to deal with the control problems for such type of systems. Application of the proposed control algorithm on a vertical-takeoff-landing air craft (VTOL), which is also a type of flat UMS, is described in the first section. Thereafter, Sect. 5.2 describes application of control law on underactuated surface vessel (USV). Being a member of nonholonomic systems, it fails to satisfy the Brocket’s condition of feedback linearization. Therefore, USV requires nonsmooth or time varying control input for its stabilization. Needless to say that designing a control law for USV is more difficult than that of other holonomic UMS (e.g., VTOL). Nonetheless, without considering robotic applications, discussions on UMSs would not be able to take its complete shape. Therefore, at the end, Sect. 5.3 demonstrates application of the control law on the robotic manipulator. Like USV, 3-DOF manipulator also belongs to the class of nonholonomic systems; however, unlike USV it possesses interacting control inputs. Hence, it is easy to understand that control law design for the same is more difficult than that of USV. Like the previous chapter, here also the reader will observe that no such significant modification is required to recast the control law for individual systems. Proposed control law is generalized enough that it can address the control problems of most of the higher order UMSs. All being well after going through this chapter, the readers will find themselves ready to design control law for any practical underactuated systems.

Fault-tolerant control of underactuated MSVs based on neural finite-time disturbance observer: An Event-triggered Mechanism

In this article, we present an adaptive reinforcement learning optimal tracking control (RLOTC) algorithm for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances. By integrating backstepping technique with the optimized control design, we show that the desired optimal tracking performance of vessel control is guaranteed due to the fact that the virtual and actual control inputs are designed as optimized solutions of every subsystem. To enhance the robustness of vessel control systems, we employ neural network (NN) approximators to approximate uncertain vessel dynamics and present adaptive control technique to estimate the upper boundedness of external disturbances. Under the reinforcement learning framework, we construct actor-critic networks to solve the Hamilton-Jacobi-Bellman equations corresponding to subsystems of surface vessel to achieve the optimized control. The optimized control algorithm can synchronously train the adaptive parameters not only for actor-critic networks but also for NN approximators and adaptive control. By Lyapunov stability theorem, we show that the RLOTC algorithm can ensure the semiglobal uniform ultimate boundedness of the closed-loop systems. Compared with the existing reinforcement learning control results, the presented RLOTC algorithm can compensate for uncertain vessel dynamics and unknown disturbances, and obtain the optimized control performance by considering optimization in every backstepping design. Simulation studies on an underactuated surface vessel are given to illustrate the effectiveness of the RLOTC algorithm.

The stability control of the underactuated surface vessel is a new focus of nonlinear control system in the automatic control field. The systems of unmanned surface vessels with two water-jets are typically underatuated systems. In this paper, the three degree of freedom (DOF) planar model of unmanned surface vessels is built at first. The kinematic and dynamic models of underactuated unmanned surface vessels are converted to two subsystems with diffeomorphism and input transformation. Then we propose the control law based on the Lyapunov functions and backstepping techniques to make underactuated unmanned surface vessel systems to be globally asymptotically stable. The simulation experiments based on the proposed control law are carried out and the numerical simulation results are given to illustrate the effectiveness of the proposed control law.

In this paper, finite-time fault-tolerant attitude tracking control is investigated for rigid spacecraft system with external disturbances, inertia uncertainties and actuator faults. A novel finite-time disturbance observer combined with a nonsingular terminal sliding mode controller is developed. Using an equivalent output error injection approach, a finite-time disturbance observer with simple structure is firstly designed to estimate lumped uncertainty. Then, to remove the requirement of prior knowledge about lumped uncertainty and reduce chattering, an adaptive finite-time disturbance observer is further proposed, and the estimations converge to the neighborhood of the true values. Based on the designed observer, a unified finite-time attitude controller is obtained automatically. Finally, both additive and multiplicative faults are considered for simulations and the results illustrate the great fault-tolerant capability of the proposed scheme.

In this study, a novel composite control scheme for the vehicle-guideway coupling systems is proposed, consisting of FTDOs and a FTC, aiming to address the challenges of unknown disturbances and vibration suppression. Specifically, this method adopts a single magnet-track coupling model and introduces a finite-time disturbance observer (FTDO) that utilizes only measured electromagnet-side signals to estimate unmeasurable states and unknown disturbances. Based on the estimated information provided by the FTDO, a finite-time control (FTC) scheme is developed, which simultaneously handles the problems of disturbance compensation and finite-time tracking control. Additionally, the finite-time stability of the levitation system is analyzed and proven. Finally, simulation and experimental results are given to demonstrate the feasibility and superiority of the proposed control approach.