Distributed Event-Triggered Formation Control of USVs with Prescribed Performance

This paper is concerned with the event-triggered neural learning control for strict-feedback nonlinear systems (SFNSs) subject to predefined tracking performance. A system transformation approach is utilized to transform the original SFNSs into the normal form, which makes it easily to verify the convergence of neural weights since only one neural network (NN) approximator is employed. Then, a novel finite-time performance function is first proposed to characterize the performance constraints of tracking error, thus guaranteeing the prescribed tracking performance. On this basis, a new lemma is given, which is crucial to ensure the stability of the closed-loop system. By using the error transformation technique, the constrained tracking control problem is converted into the stabilization problem of unconstrained error system. Subsequently, by introducing the first-order sliding mode differentiator into the backstepping procedure and with the help of the high-gain observer, a novel adaptive neural tracking control scheme is presented to ensure that the prescribed tracking performance is satisfied and all closed-loop signals are ultimately bounded. After ensuring the satisfaction of the partial persistent excitation condition for radial basis function NN, the neural weight estimates are verified to converge to the ideal values, and then the convergent weights are stored as the constant values. By utilizing the stored knowledge, an event-triggered neural learning controller is constructed to accomplish the same or similar control tasks, which can effectively improve the transient control performance, save the communication resource, and relieve the online computation at the same time. Finally, the effectiveness of the presented scheme is testified by numerical and practical examples.

Section: Discussionmentioning

confidence: 99%

Dynamic learning‐based event‐triggered control of strict‐feedback systems with finite‐time prescribed performance

Wang

Yang

2022

“…Thus, model-based control strategies are no longer feasible once there exist unknown parameters in the controlled system. Particularly, approximation-based approaches, such as neural networks (NNs) or fuzzy logic systems (FLSs)-based adaptive control, provide an effective solution to approximate the various kinds of uncertainties including unknown nonlinearities, [8][9][10][11][12][13] unknown external disturbances, 14,15 and unmodeled dynamics. 16 An NN target tracking control strategy for AUVs was proposed to compensate model uncertainties in Reference 17. Recently, a backstepping-based adaptive fuzzy tracking control approach was developed for AUVs with output constraints, which solved the issue of model uncertainties in Reference 18, to name just a few.…”

Section: Introductionmentioning

confidence: 99%

Singularity avoidance adaptive output‐feedback fixed‐time consensus control for multiple autonomous underwater vehicles subject to nonlinearities

Xue

Liang

et al. 2022

This article investigates the singularity avoidance adaptive output-feedback fixed-time consensus control problem for multiple autonomous underwater vehicles (AUVs) subject to nonlinearities. A distributed state observer is designed to reconstruct the unmeasurable coupling states of each AUV system.In order to handle the unknown nonlinearities of the multi-AUV systems, the fuzzy logic systems are adopted to approximate unknown hydrodynamic parameters. By combining adaptive fixed-time control with backstepping technology, an adaptive singularity avoidance fixed-time consensus control algorithm is developed, where a new error switching mechanism is designed for avoiding the singularity problem caused by the differentiation of the virtual control law. Subsequently, a singularity avoidance distributed adaptive output-feedback fixed-time fuzzy controller is presented, under which an auxiliary function is designed to handle the difficulty of the fixed-time stability analysis under the output-feedback control strategy, where two types of observer errors are needed in the designed Lyapunov function to achieve the fixed-time stability. Then, based on the fixed-time stability criterion, it is proved that both the consensus tracking performance and the closed-loop stability can be guaranteed in fixed time. Finally, a simulation example verifies the effectiveness of the developed control strategy.

“…It is worth noting that the requirement to the system cost index has witnessed rising interest in the guaranteed cost control (GCC), whose main purpose is to stabilize the system with an adequate level of control cost 12‐14 . An event‐triggered GCC algorithm is designed for nonlinear systems with actuator faults in Reference 15.…”

Section: Introductionmentioning

confidence: 99%

Iterative learning guaranteed cost control based on two‐dimensional Roesser systems subject to denial‐of‐service attacks and fading measurements

Huang

et al. 2022

In this article, the network-based iterative learning guaranteed cost control problem for the linear systems subject to denial-of-service (DoS) attacks at input and output (I/O) sides is studied via faded channels. First, the DoS attacks are modeled by independent Bernoulli sequences, where the expectation and variance are known. The fading measurements in I/O channels are described as independent Gaussian distributions with known expectations and variances respectively. Then, the repetitive system and the proposed ILC scheme involving both the iteration and time axes are transformed into a random two-dimensional (2D) Roesser model by using the 2D system theory. The mean-square asymptotic stability is introduced and followed by the definition of guaranteed cost function. Next, sufficient conditions that can not only ensure the asymptotic stability but also the cost index are derived. By applying the linear matrix inequality technology, the gain matrices and the upper bound of the control cost are further obtained. After exploring the adverse effect brought by the random fading phenomenon, a compensation algorithm is then designed and the analysis is strictly deduced. Finally, an injection molding process example is given to confirm the validity of the design.