This paper is concerned with the intelligent event-triggering-based positioning control of networked unmanned marine vehicle (UMV) systems with hybrid attacks, where the UMV and control station is connected by a communication network and the DoS attack and Deception attack are studied. Firstly, a stochastic switched Takagi-Sugeno (T-S) fuzzy system model is proposed for the networked nonlinear UMV systems subject to aperiodic DoS attack and random Deception attack. Then, a novel asynchronous advantage actor-critic (A3C) learning-based event-triggering approach is introduced to alleviate the communication load. By using the Lyapunov stability theory and switched system analysis method, the mean-square exponential stability condition of the closed-loop system and the design method of observer-based controller are devised. Finally, an example of a networked UMV system is given to verify the effectiveness of the proposed resilient control strategy. 

A3C-Based Intelligent Event-Triggering Control of Networked Nonlinear Unmanned Marine Vehicles Subject to Hybrid Attacks

This paper is concerned with security control of nonlinear unmanned marine vehicle (UMV) systems under a networked environment. The UMV system and land-based control station are connected by a communication network. Considering the limited communication resource in the marine environment, the dynamic event-triggering mechanisms are proposed in the sensor to controller and controller to actuator sides simultaneously. Meanwhile, the triggered output data is then quantized by a logarithmic quantizer before being sent to the remote control station. First, based on the Takagi-Sugeno (T-S) fuzzy theory, the nonlinear UMV system is molded as a T-S fuzzy model. Then a hybrid switched fuzzy system is established by taking the DoS attack and quantization effect into account. An observer-based sliding mode control (SMC) scheme is proposed to stabilize the system under DoS attack, and the observer gains and controller gains can be obtained by solving a set of matrix inequalities. Finally, a benchmark UMV system is used to show the effectiveness of control scheme.

Event-Triggering and Quantized Sliding Mode Control of UMV Systems Under DoS Attack

Adaptive tracking control of stochastic switched nonlinear systems with unknown dead‐zone output

This paper investigates the neuroadaptive fault-tolerant control of nonlinear vehicular platoon with unmodeled dynamics, external disturbances, time-varying actuator fault directions, and distance restrictions. For the cases of known and unknown fault directions, by combining adaptive terminal sliding mode (TSM) control technique with barrier Lyapunov function (BLF), two neuroadaptive fault-tolerant controllers are designed based on symmetric and asymmetric BLF to ensure reliability and safety of vehicular platoon. BLF approaches are adopted to avoid collisions and to maintain communication connections simultaneously. In addition, it is worth mentioning that the unfavorable symmetry assumptions in the symmetric BLF and prescribed performance methods can be removed by adopting asymmetric BLF. In the proposed scheme, we also combine the Nussbaum function to solve the influence of unknown time-varying fault directions effectively. Furthermore, the nonsingular TSM control technique and the minimum parameter approximation method in radial basis function neural network (RBFNN) are adopted to ensure that the spacing error can converge to an arbitrarily small region in finite-time. Through the Lyapunov stability theory, we prove that all signals in the closed-loop system are bounded in finite time. The proposed control schemes are validated by means of simulation examples. 

BLF-Based Neuroadaptive Fault-Tolerant Control for Nonlinear Vehicular Platoon With Time-Varying Fault Directions and Distance Restrictions

Robust finite-time fault-tolerant control for dynamic positioning of ships via nonsingular fast integral terminal sliding mode control

This paper proposes a novel fault tolerant control strategy for dynamic positioning of unmanned marine vehicles using the quantized feedback sliding mode control technique. Due to the complex ocean environment, the unmanned marine vehicles are modeled as the Takagi-Sugeno fuzzy system with unknown membership functions. When the membership functions are not available, traditional sliding mode control technique becomes infeasible. To tackle this difficulty, a novel quantized sliding mode control strategy based on switching mechanism is designed to compensate for thruster faults effects. In addition, the phenomenon of time-varying delay leads to conservativeness of the existing dynamic quantization parameter adjustment strategy. Then a larger quantization parameter adjustment range, by taking time delay and fault factor into account, is given. Combining the novel sliding mode controller design and the improved dynamic quantization parameter adjustment strategy, the dynamic positioning of unmanned marine vehicles with thruster faults and quantization can be maintained. Finally, the effectiveness of the proposed method is verified through the simulation comparison results.

C. L. Philip Chen

Papers

Fault Tolerant Control for Dynamic Positioning of Unmanned Marine Vehicles Based on T-S Fuzzy Model With Unknown Membership Functions