This paper addresses the co-design problem of a fault detection filter and controller for a networked-based unmanned surface vehicle (USV) system subject to communication delays, external disturbance, faults, and aperiodic denial-of-service (DoS) jamming attacks. First, an event-triggering communication scheme is proposed to enhance the efficiency of network resource utilization while counteracting the impact of aperiodic DoS attacks on the USV control system performance. Second, an event-based switched USV control system is presented to account for the simultaneous presence of communication delays, disturbance, faults, and DoS jamming attacks. Third, by using the piecewise Lyapunov functional (PLF) approach, criteria for exponential stability analysis and co-design of a desired observer-based fault detection filter and an event-triggered controller are derived and expressed in terms of linear matrix inequalities (LMIs). Finally, the simulation results verify the effectiveness of the proposed co-design method. The results show that this method not only ensures the safe and stable operation of the USV but also reduces the amount of data transmissions.

Fault Detection Filter and Controller Co-Design for Unmanned Surface Vehicles Under DoS Attacks

In this paper, we investigate the problem of trajectory tracking control for marine surface vehicles (MSVs), which are subject to dynamic uncertainties, external disturbances and unmeasurable velocities. To recover the unmeasurable velocities, a novel adaptive neural network-based (NN-based) state observer is constructed. To guarantee the transient and steady-state tracking performance of the system, a novel nonlinear transformation method is proposed by employing a tracking error transformation together with a newly constructed performance function, which is characterized by a user-defined settling time and tracking control accuracy. With the aid of the state observer and the nonlinear transformation method in combination with the adaptive NN technique and vector-backstepping design tool, an adaptive neural output-feedback trajectory tracking control scheme with predefined performance is developed. With regard to the developed control scheme, uncertainties can be reconstructed only by utilizing the position and heading of the MSVs. Independent designs of the state observer and the controller can be achieved, and the position tracking error can be guaranteed to fall into a predefined residual set in the user-defined time frame and remain in the above set. A rigorous stability analysis validates that all signals in the closed-loop trajectory tracking control system for MSVs are uniformly ultimately bounded. Simulation results verify the effectiveness of the developed adaptive neural output-feedback trajectory tracking control scheme.

Adaptive Neural Output Feedback Control for MSVs With Predefined Performance

This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> knowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations—a virtual parameter and a lower bound on the uncertain gains of the actuators—and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results. 

Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation

This article investigates the adaptive fuzzy output-feedback path-following control of autonomous surface vessels (ASVs) via the event-triggered mechanism, in which the fuzzy logic systems (FLSs) are employed to approximate the uncertainties. By transforming the estimation errors, an adaptive fuzzy observer is established to estimate the kinematic states of the ASV, and a serial–parallel estimation model is fabricated based on this observer. By combining the tracking errors with the prediction errors between the observer and the serial–parallel estimation model, the composite adaptive laws are devised for the weights of the FLSs. Then, the event-triggered controller is developed in the channel of sensor to controller, in which the estimated kinematic states, the positional coordinates, and the heading angle of the ASV are sampled only when the triggering condition is violated. In this article, we put forward the problem of “jumps of virtual control laws” at the triggering instants, which commonly exists in the backstepping-based event-triggered control (ETC) in the channel of sensor to controller. To solve this problem, the dynamic surface filters are employed to generate the continuous substitutes for the virtual control laws. The proposed scheme can guarantee at least the similar control performance compared with its continuous alternatives while it importantly reduces the communication burden through the ETC and can enhance the estimation accuracy of the observer by applying the composite learning. All the errors are ensured to be semiglobally uniformly ultimately bounded in the closed-loop system. Finally, the effectiveness of the proposed scheme is verified through the numerical experiment.

Event-Triggered Composite Adaptive Fuzzy Output-Feedback Control for Path Following of Autonomous Surface Vessels

Event-triggered fuzzy control of networked nonlinear underactuated unmanned surface vehicle

This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the a priori knowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations--a virtual parameter and a lower bound on the uncertain gains of the actuators--and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results.

In this paper, we investigate the trajectory tracking control problem of surface ship subject to the dynamic uncertainties, unknown time-varying disturbances and input saturation. To handle the non-smooth input saturation nonlinearity and compensate the ship dynamic uncertainties, Gaussian error function and adaptive neural network technique are employed. In control design, to obtain the transient motion reference signal, finite-time nonlinear tracking differentiator is applied to generate virtual reference signal and to extract the derivative of virtual control law. Referring to the effects of the kinematics subsystem on the kinetics subsystem caused by the error of tracking differentiator, and the effects of the input saturation on the control accuracy and the dynamic quality of the trajectory tracking control system, we propose an error-driven-based nonlinear feedback recursive design technique to design trajectory tracking control law, and employ a new non-quadratic Lyapunov functions to analyze the trajectory tracking control system stability. The proposed control scheme fully embodies the characteristics of the lowgain and high-gain control, and overcomes the effect of tracking differentiator error on closed-loop system by recursive design method. Simulation results verify the effectiveness of our proposed control scheme.

Guibing Zhu

Papers

Event-Triggered Adaptive Neural Fault-Tolerant Control of Underactuated MSVs With Input Saturation

Adaptive Neural Output Feedback Control for MSVs With Predefined Performance

Error-Driven-Based Nonlinear Feedback Recursive Design for Adaptive NN Trajectory Tracking Control of Surface Ships With Input Saturation